Optimization of processing technology for commercial

Transcription

FORSCHUNGSBERICHT AGRARTECHNIK
des Fachausschusses Forschung und Lehre der
Max-Eyth-Gesellschaft Agrartechnik im VDI (VDI-MEG)
530
John Bosco Kawongolo
Optimization of Processing Technology for
Commercial Drying of Bananas (Matooke)
Dissertation
Witzenhausen 2013
Universität Kassel
Fachbereich Ökologische Agrarwissenschaften
Fachgebiet Agrartechnik
Prof. Dr. sc. agr. Oliver Hensel
Optimization of Processing Technology for Commercial
Drying of Bananas (Matooke)
Dissertation
zur Erlangung des akademischen Grades
Doktor der Agrarwissenschaften (Dr. agr.)
Fachbereich Ökologische Agrarwissenschaften
von
John Bosco Kawongolo,
B.Sc. (Mech.) Eng, M.Sc. (Agric. Eng.) Agric.
aus Uganda
2013
Die vorliegende Arbeit wurde vom Fachbereich für Ökologische Agrarwissenschaften,
Fachgebiet Agrartechnik der Universität Kassel als Dissertation zur Erlangung des
akademischen Grades Doktor der Agrarwissenschaften angenommen.
Tag der mündlichen Prüfung: 13.12.2013
Erster Gutachter :
Prof. Dr. sc. agr. Oliver Hensel
Zweiter Gutachter :
Prof. Dr.-Ing. Werner Hofacker
Mündliche Prüfung: PD Dr. Johannes Kahl
Prof. Dr. Florence I. Muranga
Alle Rechte vorbehalten. Die Verwendung von Texten und Bildern, auch
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Das gilt insbesondere für Vervielfältigung, Übersetzung, Mikroverfilmung sowie die
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© 2013
Im Selbstverlag:
Bezugsquelle:
Universität Kassel Fachbereich Ökologische Agrarwissenschaften
Fachgebiet Agrartechnik
Nordbahnhofstrasse 1a
37213 Witzenhausen
Acknowledgements
The work in this manuscript was accomplished under the inspiring guidance
and dynamic supervision of Prof. Dr. Oliver Hensel, Department of Agricultural
Engineering, Faculty of Organic Agricultural Sciences, University of Kassel,
Germany, who kept his door always open for consultation and advice at all
times when required. I want to thank the staff of the Department of Agricultural
Engineering, University of Kassel, for their support. I want to acknowledge
Prof. Dr. Werner Hofacker, Environmental Process Engineering, Thermal
Processing Engineering, University of Applied Sciences, Konstanz, Germany,
for his kind advice and guidance. Special thanks to Dr. Ing. Albert Esper, M/s
Innotech, Germany, for kind academic and professional guidance during my
research.
I want to give special thanks and would like to express my gratitude to Rev.
Dr. Florence I. Muranga for both academic and parental guidance to ensure
that I keep track of my research. I would like to thank all the professionals
working at PIBID, the laboratory technician and all other staff of PIBID for their
continued support and encouragement in my academic career. I am deeply
grateful to the BMC of PIBID for taking me on as a research fellow and giving
me the opportunity to undertake my research in order to contribute to the
development of the banana processing industry in Uganda. Special thanks to
the Government of the Republic of Uganda for the financial support, through
PIBID, during the course of my study to enable me accomplish my academic
endeavors.
Special thanks to my brothers in our Lord Jesus Christ, and to my mother and
father (deceased), who tirelessly prayed and encouraged me to be focused in
life and in my career. Finally, I have no words to use to express my gratitude
to my beloved wife Mary and children Israel, Rebecca and John, for their
continued sacrifice at a time when they needed me most, for their patience,
eternal affection, support and encouragement during my academic career.
Glory be to The Almighty God for the gift of life and success. “The fear of the
Lord is the beginning of wisdom and the knowledge of the Holy One is insight”
(Proverbs 9:10) Amen.
2013
List of Tables
Table of Contents
I
II
List of Tables
List of Figures
List of Figures
III
IV
List of Figures
List of Figures
V
VI
List of Tables
List of Tables
List of Tables
VII
Nomenclature
VIII
Nomenclature
A
m2
Area
aw
dimensionless
Water activity
c, c1, c2 &
c3 cv.
dimensionless
Empirical constant
Cultivar
DD
days
Degree-days
Deff
m2/s
Effective diffusivity
Do
m2/s
Empirical constant
df
Numerical
Degree of freedom (n-1)
Ea
kJ/mol
Activation energy
i
Numerical
1, 2, 3, …
k
dimensionless
Empirical constant
k
/s
Drying rate constant
L
mm
Half thickness of slice
MR
dimensionless
Moisture ratio
n
Numerical
1, 2, 3, …
P
degrees
Total rotation
p
kPa
Equilibrium partial vapor pressure of water in the
system
P & P΄
degrees
Rotation
po
kPa
Equilibrium partial vapor pressure of pure liquid
water at the same temperature
qo
dimensionless
Empirical constant
Qst
kJ/kg
Total isosteric heat of sorption
qst
kJ/kg
Net isosteric heat of sorption
R
spp.
kJ/kg K, kJ/mol K
Universal gas constant
Species (plural)
sp.
Species (single)
t
s
Time
T
˚C/K
Temperature
xe
kg water/kg dry matter
Equilibrium moisture content
Experimental equilibrium moisture Content at ith
xi
data
xm
Monolayer
xo
dimensionless
Empirical constant
xpred
Predicted moisture content
α
α
degrees
degrees
Hue angle
angular rotation
λ
kJ/kg
Latent heat of pure water
Introduction
1
Chapter One
Introduction
Bananas and plantains (Musa spp.) are grown worldwide in 120 -130
tropical countries and are the fourth most important food crop after
rice, wheat, and maize (Qi et al., 2000; Nelson et al., 2006; Nyombi, 2010;
Akcoaz, 2011). Bananas and plantains are a source of nutrition and household
income for 400 – 500 million people in Africa, Asia and South America (Nelson
et al., 2006; Adeniji et al., 2010; IITA 2009). The East African highland banana
(EAHB) is a distinct group of AAA bananas which are found in East African
highlands and a staple food for 80 million people in the Great Lakes region of
East Africa (Robinson & Saúco, 2010; Adewole et al., 2012).
Banana is an important crop in Uganda; it is a staple food for more than 70%
of Uganda’s population and contributes to about 42% of household income
in rural areas (Karugaba & Kimaru, 1999). According to FAO, Uganda is
the second largest producer of bananas and plantains after India, with a
production of 10.5 million metric tons of plantains and 0.5 million metric tons of
dessert bananas, giving a total production of 11 million metric tons (FAOSTAT,
2011). The most common of these plantains is the cooking type, locally called
matooke, a Musa sp. triploid acuminate genome group (AAA-EAHB), steamed
and served as a main course (Karamura et al., 1998; Nyombi et al., 2009).
Matooke are perishable and are traded in fresh form (Vinzenz, 2011). This
leads to high postharvest losses ranging from 22-45%, worth $411.9 – 842.5
million (Muranga et al., 2010). These high postharvest losses are attributed
to many silent factors including non-uniform level of maturity at harvest,
poor handling, bulk transportation and lack of value addition/processing
technologies, which are currently the main challenges for trade and export,
and its diversified utilization. This is evidenced by the fact that even though
Uganda is the second world producer of bananas and plantains, it does not
appear anywhere among the world exporters of bananas (Robinson & Saúco,
2010).
Drying of fruits is a widely applied process to increase shelf life and reduce
bulk transportation; thus value addition/processing by drying would contribute
2
Introduction
to solving the above-mentioned challenges. Muranga (1998) demonstrated the
stabilization of raw matooke flour and showed that it possesses good properties
for the food industry which include among others: more than 80% starch (db),
extensive shelf life due to low sugar and fat, high levels of potassium and
negligible levels of condensed tannins (therefore, non-astringent). In addition,
research on banana flour has shown that the properties of banana flour are
important for their utilization in industrial food products and non-food products
as stabilizers and tablet binders in pharmaceutical industries (Adewole et al.,
2012; Vatanausuchart et al., 2012; Niba et al., 2002).
Drying is one of the oldest technologies employed in the processing of
agricultural produce and a lot of research has been carried out on the drying of
different crops. The objective of drying is to remove water to a level such that
microbial spoilage and deterioration reactions are greatly minimized (Doymaz,
2007). Drying is a complex process in which heat and mass transfer occur
simultaneously. It is important to understand the parameters controlling this
complex process and to understand the drying behavior of a given product for
use in improving the existing drying systems, controlling the drying process
and improving on the quality of the dried product.
A number of studies have been conducted in order to understand the drying
behavior of bananas. Baini & Langrish, (2007) reported that the diffusion
model suited the moisture content better than the empirical model. It has been
reported that drying of bananas takes place in three drying regimes, i.e., one
warming-up and two falling periods, implying that diffusion was the dominant
physical mechanism controlling the moisture movement during the drying
process. The diffusion model was, therefore, used to evaluate the drying
(Demirel & Turhan, 2003; Prachayawarakorn et al., 2008; Kaddumukasa
et al., 2005). Prachayawarakorn et al., (2008) also reported that drying
temperature significantly affects the quality attributes of ripe bananas: color,
shrinkage and texture of dried banana slices. Banana maturity (ripeness)
has little influence on the drying kinetics, despite there being a significant
difference in morphology and chemical composition between green and ripe
bananas (Nguyen & Price, 2007; Baini & Langrish, 2007). However, there is
little information available on the drying behavior of matooke (Musa sp. triploid
acuminate AAA EAHB). Also no information is available relating the drying
conditions to the physiochemical properties and quality attributes of matooke
flour for industrial application.
Introduction
3
Due to the increased market demand of quality products (Sablani, 2006) and
consistency of product quality on the market, drying of fruits should comply
with certain specifications like degree of maturity and use of healthy fruits,
since drying does not improve the initial quality (Leite et al., 2007). Matooke,
like any other fruit, undergoes a developmental process by which the fruit
attains maturity. During the fruit development, starch grains are deposited
initially in the pulp cells which form in the vicinity of the vascular bundles,
and thereafter starch deposition moves centripetally and continues until fruit
maturity (Robinson & Saúco, 2010). This indicates that maturity directly
affects the quality of the final product. As Kader (1999) reported, maturity at
harvest is the most important factor that determines storage life and final fruit
quality. Traditionally, matooke maturity is judged by the visual appearance of
the fingers, particularly the angularity (the fingers being full and rounded), the
color (the color of the fingers being lighter) and the finger tips turning black
(Dadzie, 1998). At present there are no specifications and procedures for
estimating the harvest maturity age for commercial processing of matooke.
On the other hand, moisture sorption isotherms of dried products are important
for understanding the uptake of moisture during storage and distribution of
dried commodities (Yan et al., 2008). They are also employed in process
design and control, such as in predicting the end-point of drying (Sablani et al.,
2007; Wang & Brennan, 1991). The moisture sorption isotherms can be used
to obtain information on microbiological and shelf-stability of a product, as it
was reported that microorganisms do not grow on food products with water
activity below 0.6 (Iglesias and Chirife, 1982; Yan et al., 2008; Bezerra et al.,
2013). In addition, moisture sorption isotherm characteristics of a product are
important when introducing a new product on the market (Mujumdar, 2000).
No moisture sorption isotherm has been reported for dried matooke.
The functional application of the flour for use in food and non food industry
depends on the physicochemical properties and quality attributes of the flour
such as pasting properties and starch content (Niba et al., 2002; Zhang et
al., 2005). Little information is available on the influence of maturity on starch
content and pasting properties of matooke flour.
The Government of Uganda in 2005 commissioned the pilot plant for the
processing of matooke to flour under the Presidential Initiative on Banana
Introduction
4
Industrial Development (PIBID). PIBID is centered on adopting the research
findings on matooke flour (Muranga 1998), in order to diversify its utilization.
PIBID has gone a step further to brand the matooke product as Tooke,
received two patents: Patent No. AP/P/2005/003308, for Raw Tooke Flour
(RTF), which is used for bakery, confectionery and extruded products; and
Patent No. UG/P/04/00011, for Instant Tooke Flour (ITF), which is used for
porridge and as a vehicle food for soups and instant foods for infants. This
study has focused on Raw Tooke Four (referred to as matooke flour) because
of its good starch properties and diverse utilization.
However, there is still limited information available on the processing of
matooke into flour. It was therefore imperative to carry out in-depth study to
bridge the following gaps:
•
Lack of information on estimating the maturity window, in order to process
flour of consistent quality at all times on the market.
•
Lack of information on moisture sorption isotherm.
•
Lack of information on drying behavior.
•
Lack of standardized process parameters in relation to physicochemical
properties of flour.
The main objective of the study was therefore to establish the optimum
harvest maturity window and optimize the processing parameters for
obtaining microbiologically shelf-stable matooke flour with good starch quality
attributes.
The specific objectives of the study on matooke were:
1. To establish the optimum harvest maturity window.
2. To establish the moisture sorption isotherms.
3. To establish the effect of process parameters on drying
characteristics.
4. To optimize the process parameters.
5. Validation of the models of maturity and optimum process
parameters.
Literature review
Literature Review
5
Chapter Two
Chapter Two
Literature Review
Literature Review
2.1 Banana fruit maturity
The physiological maturity is the stage of development when a plant or part of the plan
2.1
Banana fruit maturity
continue ontogeny even if detached (Shewfelt, 2009; Kader, 1999). Harvest maturity,
The physiological maturity is the stage of development when a plant or
referred to as horticultural maturity or commercial maturity, is defined as the stag
part of the plant will continue ontogeny even if detached (Shewfelt, 2009;
development
when a maturity,
plant or part
the plant
possesses
the maturity
prerequisites
for utilizatio
Kader, 1999). Harvest
also of
referred
to as
horticultural
or
consumers maturity,
for a particular
purpose
(Kader,
The harvest
maturity
in this
commercial
is defined
as the
stage1999).
of development
when
a plant
or study refers
part
the when
plant possesses
theemerges
prerequisites
for stem
utilization
consumers
timeoffrom
inflorescence
from the
to theby
time
when the for
sample is harves
ahas
particular
purpose
(Kader,
1999).
The
harvest
maturity
in
this
study
refers
been reported that matooke and bananas are generally harvested between three-quart
tofull
thematurity
time from
when inflorescence
emerges from
the stem
to the
time
when period is depe
(Muranga
et al., 2007; Robinson
& Saúco,
2010).
The
maturity
the sample is harvested. It has been reported that matooke and bananas are
on various environmental conditions that include: temperatures, rainfall, soil moisture, lat
generally harvested between three-quarters to full maturity (Muranga et al.,
altitude and nutrients (nitrogen, phosphorous and potassium-(NPK), type of soil, cultiva
2007; Robinson & Saúco, 2010). The maturity period is dependent on various
many other factors).
Temperature
hastemperatures,
a strong influence
on soil
the moisture,
physiological maturity p
environmental
conditions
that include:
rainfall,
(Karugaba
& Kimaru,
1999). (nitrogen, phosphorous and potassium-(NPK),
latitude,
altitude
and nutrients
type of soil, cultivar and many other factors). Temperature has a strong
influence on the physiological maturity period (Karugaba & Kimaru, 1999).
Chilletetetal.,
al.,(2006),
(2006),reported
reported aa close
close relationship
relationship between
between the temperatures accumulated b
Chillet
accumulated
by the
during growth
and
physiological
The
fruit during growth
andfruit
its physiological
age.
Theitsphysiological
ageage.
of the
fruit is calculated
physiological
age
of
the
fruit
is
calculated
from
equation
2.1.
equation 2.1.
𝑃ℎ𝑦𝑠𝑖𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑎𝑔𝑒 (𝑑𝑎𝑦𝑠) =
�00 𝑑𝑒𝑔𝑟𝑒𝑒−𝑑𝑎𝑦𝑠
2.1
𝐷𝑎𝑖𝑙𝑦 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑒𝑔𝑟𝑒𝑒−𝑑𝑎𝑦𝑠
Where:
Where:
Daily average degree-days = (Average daily temperature – Base temperature)
Daily average degree-days = (Average daily temperature – Base
temperature)
The total degree-days are defined as heat/energy required for complete fruit
The total degree-days are defined as heat/energy required for complete fruit growth. Ban
growth. Bananas under normal growth conditions accumulate a total of 900
under normal
accumulate
a total
of 2010;
900 Robinson
degree-days (DD) to
degree-days
(DD)growth
to reachconditions
physiological
maturity (Chillet
et al.,
physiological maturity (Chillet et al., 2010; Robinson & Saúco, 2010; Chillet et al., 2006).
daily degree-day is calculated from the daily mean temperature sums accumulated by the
o
above the temperature threshold (base temperature) 14 C (Bagaud et al., 2010; Nyombi
Literature Review
6
& Saúco, 2010; Chillet et al., 2006). The daily degree-day is calculated
from the daily mean temperature sums accumulated by the fruit above the
Literature review
temperature
threshold (base temperature) 14oC (Bagaud et al., 2010; Nyombi
et al., 2009; Chillet et al., 2006; Robinson & Saúco, 2010).
6
Literature
6
There
arereview
a number of changes in maturity indices both visual and fruitThere are a number
of changes
maturity
indices both
visual
characteristic
that occur
duringinthe
fruit growth
period
dueand
to fruit-characteristic
morphological that occur
during theThe
fruit visual
growthchanges
period due
to morphological
changes.
The visual
changes
changes.
include:
size, shape,
angularity,
skin color
andinclude: size,
shape,
angularity,
skin
color
and
nature
of
the
stylar
end;
and
the
fruit
characteristics
changes
nature of the stylar end; and the fruit characteristics changes include: fruit
There are a number of changes in maturity indices both visual and fruit-characteristic that occur
include:length,
fruit weight,
length, girth/circumference,
and pulp-to-peel
ratio (Dadzie, 1998;
weight,
girth/circumference,
diameter,diameter,
and pulp-to-peel
ratio (Dadzie,
during the fruit growth period due to morphological changes. The visual changes include: size,
1998;
Muranga,
1998;
Dhatt & Mahajan,
2007;
Ramma,
1999;
Singh,&2004;
Muranga,
1998;
Dhatt
&
Mahajan,
2007;
Ramma,
1999;
Singh,
2004;
Dadzie
Orchard, 1997).
shape, angularity, skin color and nature of the stylar end; and the fruit characteristics changes
Dadzie
& Orchard, 1997).
include: fruit weight, length, girth/circumference, diameter, and pulp-to-peel ratio (Dadzie, 1998;
Muranga, 1998; Dhatt & Mahajan, 2007; Ramma, 1999; Singh, 2004; Dadzie & Orchard, 1997).
The color of the pulp is also used as one of the maturity indices; it is traditionally
The color
of famers
the pulpand
is also
usedwho
as one
indices;
is traditionally
used
by the
traders,
lookofatthe
thematurity
intensity
of theit yellowness
of used by the
famers
traders,
who lookcolor
at the
intensity
the been
yellowness
of the pulp.
There are several
the
pulp.and
There
are several
scales
thatofhave
developed.
However,
The
color
of the
pulp
is also
used
as one of However,
the maturitythe
indices;
it is“Lab”
traditionally
used
by the
color
scales
that
have
been
developed.
Hunter
and
its
variant
the Hunter “Lab” and its variant CIE L*a*b*, are the commonly used scales for CIE L*a*b*,
famers and traders, who look at the intensity of the yellowness of the pulp. There are several
are the
commonly
used(Shewfelt,
scales for fruits
andThe
vegetables
2009).
The
fruits
and
vegetables
2009).
Hunter (Shewfelt,
L, a, b color
scale
isHunter
more L, a, b color
color scales that have been developed. However, the Hunter “Lab” and its variant CIE L*a*b*,
scale
is
more
visually
uniform
than
the
XYZ
color
scale.
The
Hunter
L,
a,
b color
space is
visually uniform than the XYZ color scale. The Hunter L, a, b color space
is
are the commonly used scales for fruits and vegetables (Shewfelt, 2009). The Hunter L, a, b color
organized in
color
values
recorded
as “L”
= Black;
100 = 100
White), “a” (-a =
organized
in aa cube
cubeform,
form,with
with
color
values
recorded
as(0“L”
(0 = Black;
scale is more visually uniform than the XYZ color scale. The Hunter L, a, b color space is
=Green;
White),
(-a = and
Green;
+a ==Blue;
Red),+band
“b” (-b The
= Blue;
+b =(Saturation)
Yellow). The
+a“a”
= Red),
“b” (-b
= Yellow).
Chroma
(C*) represents
organized in a cube form, with color values recorded as “L” (0 = Black; 100 = White), “a” (-a =
Chroma
(Saturation)
(C*)
represents
“richness
of
color
or
color
saturation”,
or
“richness
color or
color
saturation”,
or=color
intensity;
hue angle
depicts how
an average
person
Green;
+a of
= Red),
and
“b” (-b
= Blue; +b
Yellow).
The Chroma
(Saturation)
(C*) represents
color
intensity;
hue
angle
depicts
how
an
average
person
will
perceive
that
will
perceive
that
color
(Hunter
Lab,
2008,
Siddiq
et
al.,
2010).
According
to
Falade
&
Olugbuyi
“richness of color or color saturation”, or color intensity; hue angle depicts how an average person
color
(Hunter
Lab,
2008,
Siddiq
et al.,Siddiq
2010).
According
to
Falade
& Olugbuyi
(2010),
the that
hue
angle
is one
parameter
thatet isal.,
frequently
used
toto characterize
color in food
will
perceive
color
(Hunter
Lab,
2008,
2010). According
Falade
& Olugbuyi
(2010),
the
hue
angle
is
one
parameter
that
is
frequently
used
to
characterize
(2010),
theThe
huesaturation
angle is one
that isinfrequently
used
to characterize
colorasinfollows:
food
products.
(C*)parameter
and hue angle
degree (α)
values
are calculated
color
in food
products.(C*)
The
and(α)hue
angle
in degreeas(α)
values
products.
The saturation
andsaturation
hue angle in(C*)
degree
values
are calculated
follows:
are calculated as follows:
𝐶 ∗ = √𝑎∗2 + 𝑏 ∗2
2.2
𝐶 ∗ = √𝑎∗2 + 𝑏 ∗2
−1 𝑏 ∗𝑏
2.2
∗
𝛼=
= 𝑡𝑎𝑛
𝑡𝑎𝑛−1 � � �∗�
𝛼
∗𝑎
2.3
2.3
𝑎
Shewfelt, (2009) reported that in human terms an increase in yellowness is
signaled by the closeness of the hue angle (α) to 90o.
Shewfelt,
thatthat
in human
termsterms
an increase
in yellowness
is signaled
by the by the
Shewfelt,(2009)
(2009)reported
reported
in human
an increase
in yellowness
is signaled
o
closeness
ofofthe
angle
(α)program
to 90
The
Visual-Basic-based
closeness
thehue
hue
angle
(α)
to .90 . used to analyze images, transforms the
RGB-values to XYZ-values in the first step, then in transforms the XYZ-values
o
The Visual-Basic-based program used to analyze images, transforms the RGB-values to XYZ-
The Visual-Basic-based program used to analyze images, transforms the RGB-values to XYZ-
values in the first step, then in transforms the XYZ-values to the CIE-L*a*b* color space in the
values in the first step, then in transforms the XYZ-values to the CIE-L*a*b* color space in the
Literature Review
7
to the CIE-L*a*b* color space in the second step (Sturm & Hofacker, 2009;
Literature review
7
HunterLab,
2008; Sturm et al., 2012).
The
color
difference
ΔE*
has
beentoreported
toimportant
be the inmost
important
The total
total color
difference
ΔE* has
been
reported
be the most
determining
the color
*
inchange
determining
the
color
change
in
food
(Sturm
et
al.,
2012).
The
total
color
in food (Sturm et al., 2012). The total color difference ΔE is calculated as follows:
difference ΔE* is calculated as follows:
ΔE ∗ = �(ΔL∗2 + Δa∗2 + Δb ∗2 )
2.4
The color change of dried bananas is estimated by subtracting the color of the
The color
changefrom
of dried
is estimated
subtracting(Bani
the color
of the dried 2007).
bananas
dried
bananas
thebananas
color of
the freshbybananas
& Langrish,
from the color of the fresh bananas (Bani & Langrish, 2007).
2.2
Physicochemical properties
Raw matooke flour is a generic developed to improve shelf stability of the fruit
and to find alternative uses. It is rich in starch (80 - 85% db) and subsequently
has a high potential as a calorie resource base (Muranga, 1998). Table 2.1
shows the average chemical composition of matooke.
Table 2.1 Chemical composition of matooke from different cultivars
Chemical Components
Cultivar and Chemical Composition (%)
2.2 Physicochemical properties
Nandigobe
Bukumu
Embururu
Raw matooke flour is a generic developed to improve shelf stability of the fruit and to find
(AAA-AE)
(AAA-AE)
(AAA-AE)
Moisture Content
9.6
8.1
8.8
Starch
matooke.
81.8
82.5
82.9
Protein
4.71
5.1
4.01
alternative uses. It is rich in starch (80 - 85% db) and subsequently has a high potential as a
calorie resource base (Muranga, 1998).
Table 2.1 shows the average chemical composition of
Fat
ND cultivars
Table 2.1 Chemical composition0.87
of matooke from different
0.56
Crude Fiber
1.33
Ash
Chemical Components
1.25
4.34
ND
Cultivar and Chemical Composition (%)
3.58
Nandigobe
Bukumu
Embururu
0.0058
Potassium
(K)
Moisture Content
1.9
Magnesium
(Mg)
Starch
0.09
81.8 0.09
Tannin (Abs
at 500nm)
Protein
0.111
4.71 0.181
5.1
0.01 82.9
0.012 4.01
0.87
ND
0.56
1.25
ND
1.33
4.34
3.58
4.1
Calcium (Ca)
0.0058
0.0044
0.0052
Potassium (K)
1.9
1.82
1.84
(Source: Fat
Muranga, 1998)
Crude Fiber
Ash
(AAA-AE)
9.6
0.0044
4.1
Calcium (Ca)
(AAA-AE)
1.82
8.1
82.5
0.0052
(AAA-AE)
1.84
8.8
8
Literature Review
Physicochemical properties of flour and starchy products are important for
their use in the food and non-food industry (Niba et al., 2002). Raw matooke
flour consists of native starch which exists as microscopic granules consisting
of two distinct polymers: the amylose, which are linear in structure, and
amylopectin, which exhibits substantial branching in its structure as shown
in Figure 2.1.
Figure 2.1: Amylose & amylopectin molecular structures (source: Damodaran
et al., 2008)
Functional properties
Pasting properties of flour are important in the sense that they give an indication
of the cooking and baking qualities of the flour (Oluwalana et al., 2011). Peak
viscosity is a measure of the water holding capacity of the starch in terms
of the resistance of swollen granules to shear and the swelling performance
of granules (Newport Scientific, 2006; Daramola & Osaosnylusi, 2006). It is
often correlated with the final product quality (Kolawole et al., 2012). Final
viscosity is the most commonly used parameter to define a particular sample’s
quality, as it indicates the ability of the material to form a viscous paste or gel
after cooking and cooling (Iglett et al. 2012; Osungbaro et al., 2010; Ikegwu
et al., 2009; Niba et al. 2002; Muranga, 1998). Therefore, the integrity of the
starch granules and hydration properties of starch can easily be investigated
by measuring the pasting behavior of flour (Lai & Cheng, 2004). If the starch
Literature Review
9
granules are damaged, they will take in more water and will reach the maximum
swelling early and will collapse at a lower peak, compared to starch granules
of the same sample which have not been damaged (Oluwalana et al., 2011).
Figure 2.2 shows the standard rapid viscosity analyzer’s temperature profile
and pasting curve. Table 2.2 shows the pasting properties of raw matooke
flour from the given sources.
Figure 2.2: Typical rapid visco-analyzer temperature profiles and pasting
curve (Source: Newport Scientific, 2006).
Table 2.2: Pasting properties of raw matooke flour (Source: Muranga et
al., 2010)
Peak
(RVU)
Trough
(RVU)
Breakdown
(RVU)
Final
Viscosity
(RVU)
Setback
(RVU)
Peak
Time
(sec)
Pasting
Temp
(oC)
375.92
201.29
173.63
244.7
43.38
4.7
74.73
2.3
Moisture sorption isotherms
“Moisture sorption isotherms” is defined as a graphic relationship between water
activity (equilibrium relative humidity) and the equilibrium moisture content of
the material at a constant temperature (Thys et al., 2010; Veg-Galvez et al.,
2007). Moisture content of the food product is very important for the design
10
Literature Review
and optimizing of the drying equipment, the design of packages, the prediction
of quality, stability, and shelf-life, and the calculation of moisture changes
during storage (Andrade et al., 2011, Johnson & Brennan, 2000, Moreira et
al., 2008). In order to preserve and stabilize the main food properties such
as texture and microbiological stability, it is important to control its moisture
content (Ayala et al., 2011).
The state of moisture in a food product is expressed in terms of water activity
(aw). Water activity (aw) is defined as the ratio of vapor pressure of water
in food to vapor pressure of pure water at a constant temperature (Sahu &
Tiwari, 2007). It is essential for describing water availability and mobility in
foods (Ayala et al., 2011).
Most processing operations are concerned with foods having a water activity
(aw) range below 0.95, which are depicted by the general types of moisture
sorption isotherms (Bell & Labuza, 2000). Moisture sorption isotherms are
classified according to their shape in five different types: I, II, III, IV and V.
However, Types II, IV and V are similar, with one or more inflection points
(Yan et al., 2008; Andrade et al., 2010). Previous researches reported that the
moisture sorption isotherms for bananas and plantains exhibited the Type II
curves (Johnson & Brennan, 2000; Yan et al., 2008).
Figure 2.3 shows the chemical ‘stability map’, relating the effect of water
activity (aw) in the food material on the microbial growth. From the stability
map, it is evident that during the intermediate moisture range, many reactions
occur causing deterioration (Labuza, 1972). It has been reported that
microorganisms do not grow on food products with water activity aw below
0.6 (Labuza et al., 1972; Sahu & Tiwari, 2007; Yan et al., 2008). The moisture
sorption isotherm was used to determine the desired water activity from the
stability map, below which the dried product would be microbiologically safe.
Literature Review
11
Figure 2.3: Stability map of foods as a function of water activity (Source:
Labuza et al., 1972).
2.3.1 Moisture sorption models
Moisture sorption isotherms can be constructed by either an adsorption
process, which starts from the dry state with water activity (aw) = 0, for which
it takes in moisture to reach equilibrium moisture content at any given water
activity to (aw) ≈ 1, or by a desorption process, which starts from the initial
wet state with water activity (aw) ≈ 1 for which moisture is removed to reach
equilibrium moisture content at any given water activity to (aw) ≈ 1 (Bell &
Labuza, 2000, Yan et al., 2008). The shapes of the adsorption and desorption
isotherms are sometimes different from each other for a given product; this
phenomenon is referred to as moisture hysteresis (Bell & Labuza, 2000, Yan
et al 2008). The hysteresis phenomenon indicates that the water adsorption
and desorption processes are irreversible because fresh foods hold more
moisture than dry foods (Vega-Gálvez et al., 2008).
There are several models reported in literature. However, the following models
have been fitted for bananas and plantains: Yan et al., (2008) reported that
the GAB (1981), Henderson (1952), modified Hasley (1948), and modified
Oswin fitted well the equilibrium moisture content data over 10 – 70% relative
humidity. Ajibola, (1986) fitted Henderson, Chung-Pfost, modified Halsey, and
modified Hasley (1948), and modified Oswin fitted well the equilibrium moisture content data over
10 – 70% relative humidity. Ajibola, (1986) fitted Henderson, Chung-Pfost, modified Halsey, and
Chen-Clayton, the results of the standard error was less than 3%, indicating a good fit. Johnson
Literature
Review
and
and BET.
Of the
12Brennan (2000) fitted five models: GAB, Henderson, Halsey, Iglesias-Chirfe
five only Henderson fitted best the isotherm of fresh material. Phoungchandang & Woods (2000)
Chen-Clayton,
the results
of the
standard
wasOswin,
less than
3%, indicating
fitted
modified Chung-Pfost,
modified
Hernderson,
anderror
modified
all of which
gave a good
good fit.
and Brennan
models:
fit. a Falade
and Johnson
Awoyele (2005)
fitted eight(2000)
modelsfitted
whichfive
included:
BET,GAB,
GAB, Henderson,
Oswin, Hasley,
Halsey, Chung-Pfost,
Iglesias-Chirfe
BET. (1947).
Of theOffive
only
fitted the
best
the
Henderson,
Chenand
and Smith
these
onlyHenderson
GAB best described
sorption
of fresh material.
Phoungchandang
& Woods
fitted
of isotherm
fresh and pre-osmosed
oven-dried
banana slices. Aguire-Cruz
et (2000)
al. (2010)
fittedmodified
BET, GAB,
Chung-Pfost,
modified
Hernderson,
andGAB
modified
of which
a
Smith
and Iglesias-Chirife.
Of these
only BET and
gave theOswin,
best fit. all
Therefore,
for gave
this study
thegood
following
models were
selected to(2005)
describe
the eight
moisture
sorption
isotherms
for matooke:
fit. Falade
and Awoyele
fitted
models
which
included:
BET,
BET,
GAB,
Hernderson,
Smith. Therefore,
the Smith
models(1947).
selectedOffor
GAB,
Oswin,
Hasley,Iglesias-Chirife
Henderson, and
Chung-Pfost,
Chen and
describing
moisture
isotherms
for matookethe
aresorption
given in the
these only
GAB
best described
of Table
fresh2.3.
and
pre-osmosed ovendried banana slices. Aguire-Cruz et al. (2010) fitted BET, GAB, Smith and
Iglesias-Chirife. Of these only BET and GAB gave the best fit. Therefore,
for this study the following models were selected to describe the moisture
sorption isotherms for matooke: BET, GAB, Hernderson, Iglesias-Chirife and
Smith. Therefore, the models selected for describing moisture isotherms for
matooke are given in the Table 2.3.
Table
Moisture
Model
Equations
to sorption
describe
Table
2.3: 2.3:
Moisture
IsothermIsotherm
Model Equations
used
to describeused
moisture
isotherms for
moisture
matooke
sorption isotherms for matooke
Author
Model
𝑎𝑤 𝑥𝑚 𝑐
(1 − 𝑎𝑤 )(1 + (𝑐 − 1)𝑎𝑤 )
BET (1938)
𝑥𝑒 =
GAB (1981)
𝑥𝑒 =
Henderson (1952)
𝐼𝑛(1 − 𝑎𝑤
𝑥𝑒 = �
�
−c2
Chung-Pfost (1967)
Iglsias-Chirife (1976)
Smith (1947)
𝑥𝑚 𝑐𝑘𝑎𝑤
(1 − 𝑘𝑎𝑤 )(1 − 𝑘𝑎𝑤 + 𝑐𝑘𝑎𝑤 )
1�
𝑐1
−1
𝑇 ∗ 𝑙𝑛𝑎𝑤
𝑥𝑒 = � � ∗ 𝑙𝑛 �
�
(−𝑐1 )
𝑐2
𝑥𝑒 = 𝑐1 + 𝑐2 ∗ �
𝑎𝑤
�
(1 − 𝑎𝑤 )
𝑥𝑒 = 𝑐1 − 𝑐2 ∗ 𝑙𝑛(1 − 𝑎𝑤 )
2.3.2 Isosteric heat of sorption
The isosteric heat of sorption is used to estimate the energy requirements for
drying and provides important information on the state of water in food products
(Yan et al., 2008). In addition, it provides valuable data for energy consumption
important information on the state of water in food products (Yan et al., 2008). In addition, it
2.3.2 Isosteric
heat ofon
sorption
important
information
the state of water in food products (Yan et al., 2008). In addition, it
provides valuable data for energy consumption calculations and subsequent design of drying
provides valuable data for energy consumption calculations and subsequent design of drying
equipment
(McMinn
& Magee,
2003).
The isosteric
heat requirements
of sorption for
The isosteric
heat of sorption
is used
to estimate
the energy
for desorption
drying and isotherm
provides
equipment (McMinn & Magee, 2003). The isosteric heat of sorption for desorption isotherm
represents
the
energy
required
to
break
the
Van
der
Waals
force
between
the
molecules
Literature
Review
important information on the state of water in food products (Yan et al., 2008).13
In addition, ofit
represents the energy required to break the Van der Waals force between the molecules of
moisture
waterdata
absorbent
the during
dehydration
process and
(Yang
et al., 2012).
Theofisosteric
provides and
valuable
for energy
consumption
calculations
subsequent
design
drying
moisture and and
watersubsequent
absorbent the
duringofdehydration
process (Yang
et al.,
2012). The isosteric
calculations
design
drying equipment
(McMinn
& Magee,
heat
of
sorption
is
calculated
from
the
experimental
data
using
the
Clausius-Clapeyron
equation
equipment (McMinn & Magee, 2003). The isosteric heat of sorption for desorption isotherm
heat of sorption
is calculated
from
the experimental
data using
the Clausius-Clapeyron
equation
2003).
TheBrennan,
isosteric
heatMcMinn
of
sorption
for desorption
isotherm
represents
the
(Johnson
2000,
& Magee,
YanWaals
et
al., 2008)
given in
Equation
2.5: of
represents& the
energy
required to break
the 2003,
Van der
force as
between
the
molecules
(Johnsonrequired
& Brennan,
2000, McMinn
Magee,
2003,
Yanbetween
et al., 2008)
given in Equation
2.5:
energy
to break
the Van&der
Waals
force
the as
molecules
of
moisture and water absorbent the during dehydration process (Yang et al., 2012). The isosteric
moisture and water absorbent the during dehydration process (Yang et al.,
heat of sorption is calculated from the experimental data using the Clausius-Clapeyron equation
2012). The isosteric heat of sorption is calculated from the experimental data
(Johnson
2000,
McMinn & Magee, 2003, Yan et al., 2008) as given in Equation 2.5:
𝑑(𝑙𝑛𝑎 ) & Brennan,
𝑄 −𝜆
q
using
equation (Johnson & Brennan, 2000, McMinn
� Clausius-Clapeyron
= 𝑠𝑡 = − ��
2.5&
� 1 𝑤the
𝑑(𝑙𝑛𝑎
𝑄𝑠𝑡𝑅−𝜆
q𝑅��
𝑑� �𝑤 )
𝑇
=−
2.5
� 1 �𝑥2003,
𝑒 =
Magee,
Yan
et
al.,
2008)
as
given
in
Equation
2.5:
𝑅
𝑅
𝑑� �
𝑇
𝑥𝑒
Where:
𝑑(𝑙𝑛𝑎 )
𝑄 −𝜆
q
Where:
� 1 𝑤 � = 𝑠𝑡 = − ��
𝑅
𝑅
𝑑� �
𝑇
Q
𝑥𝑒st – Total isosteric heat of sorption (kJ/kg)
Qst – Total isosteric heat of sorption (kJ/kg)
2.5
Where: qst – Net isosteric heat of sorption (kJ/kg)
Where:
qst – Net isosteric heat of sorption (kJ/kg)
Total
isosteric
heatheat
of sorption
(kJ/kg)
λ stQ
- –Latent
heat
of water
(kJ/kg)
– Total
isosteric
of sorption
(kJ/kg)
Q
st
λ - Latent heat of water (kJ/kg)
– Net
isosteric
heat
of sorption
(kJ/kg)
R
Net
isosteric
of sorption
(kJ/kg)
qstq-–stUniversal
gasheat
constant
R - Universal gas constant
λ - Latent heat of water (kJ/kg)
λ -Equation
Latent heat
water
(kJ/kg)
Integrating
2.5,ofwe
obtain
the following Equation 2.6:
REquation
- Universal
gasobtain
constant
Integrating
2.5, we
the following Equation 2.6:
R - Universal gas constant
Integrating Equation 2.5, we obtain the following Equation 2.6:
𝑎
�𝑠𝑡 1
1
Integrating
𝑙𝑛
� 𝑤2� = −Equation
� + 2.5,
� we obtain the following Equation 2.6:
𝑎𝑤1
𝑇11
�𝑅𝑠𝑡 𝑇12
𝑤2
𝑙𝑛 � � = − � + �
𝑎𝑤1
𝑅
𝑇2
𝑇1
2.6
2.6
𝑎 net isosteric
�
1 heat
1
The
of sorption is determined from the slope of the graph
𝑙𝑛
� 𝑤2
� =isosteric
− 𝑠𝑡 � heat
+ �of sorption
2.6) versus (1/T)
The
net
is determined from the slope of the graph of ln(a
𝑎𝑤1
𝑅 𝑇2
𝑇1
w
versusheat
(1/T)of for
a specific
moisture
content.
The
ofofthe
of
Theln(a
netw)isosteric
sorption
is determined
from
the slope
of values
the graph
ln(anet
w) versus (1/T)
for a specific moisture content. The values of the net isosteric heat of sorption obtained
are fitted
isosteric
heat
of
sorption
obtained
are
fitted
by
the
empirical
relationship
for a specific moisture content. The values of the net isosteric heat of sorption obtained are fitted
by the empirical relationship between the isosteric heat of sorption and moisture content
between
the isosteric
heat of
sorption
moisture
(Equation
2.6)
by the empirical
relationship
between
theand
isosteric
heatcontent
of sorption
and moisture
content
(Equation
2.6) asheat
proposed
by Tsami
(1991). from
The the
energy
required
for breaking
the Van der
The
net isosteric
of sorption
isThe
determined
slope
of the graph
ln(a
w) versus (1/T)
as
proposed
by Tsami
(1991).
energy
required
for breaking
the of
Van
der
(Equation 2.6) as proposed by Tsami (1991). The energy required for breaking the Van der
Waals
force
drying
is obtained
by integrating
2.7:
for
a specific
moisture
content.
The values
the netEquation
isosteric
heat of
sorption obtained are fitted
Waals
forceduring
during
drying
is obtained
byofintegrating
Equation
2.7:
Waals force during drying is obtained by integrating Equation 2.7:
by the empirical relationship between the isosteric heat of sorption and moisture content
−�
� eas
� proposed by Tsami (1991). The energy required for breaking
2.7 the Van der
q
�� = q� exp
(Equation
2.6)
�
−�
2.7
q�� = q� exp � �e �
��
Waals force during drying is obtained by integrating Equation 2.7:
−�
2.4
exp � e � process for bananas
q = q Drying
��
�
��
2.7
This section describes the drying behavior of bananas. It is defined by moisture
diffusivity and drying rate constant. These were then used to compare the
effect of the processing parameters. The activation energy was also described
14
Literature Review
to estimate the energy requirement for drying as compared to the net isoteric
heat of sorption for desorption isotherm. The engineering, quality properties
and drying methods have also been discussed in order to establish how they
are affected during drying.
Drying is the oldest technology used for food preservation to extend shelf life,
minimizing postharvest losses, bulk transportation and storage space. Drying
is simultaneously a heat and mass transfer process, in which heat is transferred to the product by drying air and moisture is transferred from the product
by diffusion to the product surface and the drying air stream picks the moisture
from the surface (Lykov 1958 as reported by Corzo et al., 2008). A successful
drying process requires the conditions of the drying air to have the capacity
to transfer heat to the drying product and also pick up the moisture from the
product surface. In the convection drying system, drying kinetics is affected by
drying air temperature, air humidity, air velocity and characteristic particle size
(sample thickness) (Krokida et al., 20002; Nguyen & Price, 2007; Corzo et al.,
2008; Sturm et al., 2012). However, Krokida et al. (2000) reported that of all
those factors, the drying kinetics is greatly affected by drying air temperature
and the characteristic particle size.
Drying affects the properties of the dried product because of the physicochemical
changes that occur within the product during drying (Krokida et al., 2000; Leite
et al., 2007). Krokida et al. (2000) further reported that the properties of dried
products are classified between two major categories, the engineering and
quality properties. The engineering properties include: effective moisture
diffusivity, effective thermal conductivity, drying kinetics, specific heat and
equilibrium moisture content; meanwhile, the quality properties include: thermal
properties, structural properties, textural properties, optical properties, sensory
properties, nutritional characteristics and rehydration properties. However,
Krokida et al. (2000) reported that of all those factors, the drying kinetics is
greatly affected by drying air temperature and the characteristic particle size
(thickness of banana slice). Studies on banana drying characteristics and
quality attributes have been carried out considering the utilization of the dried
product and quality product requirements. In this study effective moisture
diffusivity, drying kinetics and equilibrium moisture content were the engineering
properties considered; color and physicochemical properties (starch content
and pasting properties) were considered for quality properties as described in
Literature Review
15
Section 2.2. From the study on the effect of drying temperature on the quality
of dried bananas (ripe bananas), Leite et al. (2007) reported that drying did
not affect the chemical composition of the product. Previous researchers on
bananas reported that the drying time decreases with increased temperature,
and the drying rate decreases with increase in thickness of slice (Nguyen &
Rice, 2007; Islam et al., 2012).
The drying method/technology employed in drying has a direct effect on
the final dried product (Nguyen & Price, 2007; Bani & Langrish, 2007).
Convectional hot-air drying and sun drying have been reported to be largely
used in the drying of unripe bananas (Johnson et al., 1998; Islam et al., 2012).
Pacheco-Delahaye et al., (2008), reported the effect of drying methods/
technologies employed in drying on starch content (% db) of dried bananas
as follows: freeze drying (74.65±2.08), drum drying (63.50±0.55), microwave
drying (64.52±0.25) and drying chamber (hot-air drying) (74.30±2.32). These
results showed that although freeze drying was superior compared to the
convectional hot–air drying, the starch content for both (freeze drying and
hot-air drying) were not significantly different. Also, Krokida et al. (1999)
reported that dehydrated products do not keep their viscoelastic behavior
after rehydration due structural damages that occur during drying. However,
hot-air and vacuum-dried products kept their viscoelastic characteristics close
to those of dehydrated products before rehydration. In addition, hot-air drying
is considered as one of the simplest and most economic ways for commercial
processing of fruit and vegetables and appropriate for developing countries
(Johnson et al., 1998; Kaddumukasa et al., 2005). Therefore, the hot-air
drying method was selected for this study.
Drying of food products is not limited to selection of dryer, but rather there is
also need to understand the physicochemical concepts associated with food
drying in order to appropriately assess the drying phenomena of any food
product (Vega-Mercado et al., 2001). In terms of food applications, starch
functionality is largely related to its gelatinization and pasting characteristics
(Zhang et al., 2005). It has been reported that gelatinization temperature of
matooke is 72˚C (Muranga, 1998).
Drying of bananas dominantly takes place in the falling rate periods,
implying that diffusion was the mechanism controlling the moisture migration
Literature Review
16
(Prachayawarakon et al., 2008; Demirel & Turhan, 2003; Kaddumukasa
etLiterature
al., 2005;
Bani & Langrish, 2007). The diffusion model based on Fick’s
review
16
Literature
review
16
second
law
is used to describe the transport of moisture inside a banana
Literature review
16
(Thuwapanichayaan
et al., 2011;
Langrish,
Fick’s law2007).
of diffusion
has been
used
slice
(Thuwapanichayaan
et Bani
al., &2011;
Bani2007).
& Langrish,
Fick’s
law of
by
a number
researchers
describe
drying
kinetics
bananas,
and
diffusion
hasofbeen
used
by
a number
of researchers
to describe
the vegetables
drying
(Thuwapanichayaan
et al., to
2011;
Bani the
& Langrish,
2007).of Fick’s
lawfruits
of diffusion
has been used
(Thuwapanichayaan
et al.,
2011;
Bani & Langrish,
2007).
Fick’s
law ofKasheninejd
diffusion has
been used
(Karim
&
Hawlader,
2005;
Baini
&
Langrish,
2007;
Nguyen
&
Price,
(2007);
et&al.,
kinetics
of bananas,
fruits and
vegetables
(Karimkinetics
& Hawlader,
2005;
Baini
by a number
of researchers
to describe
the drying
of bananas,
fruits
and
vegetables
Literature
review
16
by
a
number
of
researchers
to
describe
the
drying
kinetics
of
bananas,
fruits
and
vegetables
(2007); Doymaz,
Katekawa
& Silva
(2007);Kasheninejd
Islam et al., 2012).al., (2007); Doymaz,
Langrish,
2007;(2007);
Nguyen
Price,
(2007);
(Karim & Hawlader,
2005; &Baini
& Langrish,
2007; Nguyenet
& Price, (2007); Kasheninejd et al.,
(Karim & Hawlader, 2005; Baini & Langrish, 2007; Nguyen & Price, (2007); Kasheninejd et al.,
(2007);
& Silva
(2007);& Islam
et al., Islam
2012).et al., 2012).
(2007); Katekawa
Doymaz, (2007);
Katekawa
Silva (2007);
(Thuwapanichayaan
et al., Katekawa
2011; Bani &
& Silva
Langrish,
2007).
Fick’s
law2012).
of diffusion has been used
(2007); Doymaz, (2007);
(2007);
Islam
et al.,
by a number
of law
researchers
to describe
the expressed
drying kinetics
of bananas, fruits
and vegetables
Fick’s
second
of diffusion
be
&
Fick’s second
law of diffusion
can becan
expressed
as Equationas
2.8Equation
(Nguyen &2.8
Price(Nguyen
2007, Doymaz
(Karim
&
Hawlader,
2005;
Baini
&
Langrish,
2007;
Nguyen
&
Price,
(2007);
Kasheninejd
et al.,
Price
2007):2007, Doymaz 2007):
(2007);
Doymaz,law
(2007);
Katekawa
& Silva
(2007); Islam
et al., 2012).
Fick’s second
of diffusion
can
be expressed
as Equation
2.8 (Nguyen & Price 2007, Doymaz
Fick’s second law of diffusion can be expressed as Equation 2.8 (Nguyen & Price 2007, Doymaz
2007): �2 �
��
= 𝐷𝑒𝑓𝑓 2
2007):
�𝑡
�𝐿
2.8
Fick’s
second
�2 � law of diffusion can be expressed as Equation 2.8 (Nguyen & Price 2007, Doymaz
��
=analytical
𝐷𝑒𝑓𝑓 �2 �2 solution of Fick’s second law of diffusion was obtained on the assumption that 2.8
The
��
�𝑡
2007):
= 𝐷𝑒𝑓𝑓 �𝐿2
�𝑡
�𝐿
The
analytical
solution
of diffusion,
Fick’s second
law of diffusion
was obtained
on 2.8
moisture
migration
was only by
negligible shrinkage,
constant temperature
and diffusion
the
assumption
that From
moisture
migration was
only by
negligible
constant
(Crank,
1975).
the analytical
Equation
2.9,diffusion,
the dimensionless
amount
The analytical
solution
of Fick’s
second solution
law of of
diffusion
was
obtained
on the assumption
that
�2 �
��
= 𝐷analytical
2.8
𝑒𝑓𝑓 �𝐿2constant
The
solution
of
Fick’s
second
law
of
diffusion
was
obtained
on
the
assumption
that
shrinkage,
temperature
and
diffusion
constant
(Crank,
1975).
From
�𝑡
of diffusing water (Moisture Ratio – MR) in the slab was deduced Crank, 1975 to Equation 2.9:
moisture migration was only by diffusion, negligible shrinkage, constant temperature and diffusion
moisture
migration
was only
by diffusion,
shrinkage, constant
temperature
and diffusion
the
analytical
solution
of Equation
2.9,negligible
the dimensionless
amount
of diffusing
constant (Crank, 1975). From the analytical solution of Equation 2.9, the dimensionless amount
2�
2 𝐷 of𝑡 diffusion was obtained on the assumption that
The
analytical
solution
of1–Fick’s
second
law
−(2𝑛+1)
𝑥− 𝑥(Crank,
8
water
MR)
slab
deduced
Crank,2.9,
1975
Equation amount
constant
1975).
From
thethe
analytical
of Equation
theto
dimensionless
𝑒𝑓𝑓was
𝑒
�
= 2 ∑Ratio
𝑒𝑥𝑝 � in
�solution
2.9
𝑀𝑅
= (Moisture
𝑛=1
of diffusing
(Moisture
4 𝐿2 in the slab was deduced Crank, 1975 to Equation 2.9:
𝑥𝑜 − 𝑥𝑒 water
�
(2𝑛+1)2 Ratio – MR)
moisture
migration
was
only
by
diffusion,
negligible
shrinkage,
constant Crank,
temperature
diffusion 2.9:
2.9:
of diffusing water (Moisture Ratio – MR) in the slab
was deduced
1975and
to Equation
constant (Crank, 1975). From the analytical solution of Equation 2.9, the dimensionless amount
2 �2 𝐷 Equation
It has
been
a long
time
drying
2.9 Crank,
is expressed
Equation
−(2𝑛+1)
𝑡 deduced
𝑥− 𝑥water
8(Moisture
1Ratio
𝑒 reported
� that for
of
diffusing
– MR)
in the
slab𝑒𝑓𝑓was
1975 toas
Equation
2.9:2.10
𝑀𝑅 =
𝑥− 𝑥
=
82
∑𝑛=1
1
2
𝑒𝑥𝑝 �−(2𝑛+1)2 �22 𝐷𝑒𝑓𝑓 𝑡�
4𝐿
�
2
𝑥𝑒𝑒
(2𝑛+1)
(Nguyen
2007):
∑�
= �2007;
𝑀𝑅 = 𝑥𝑜&− Price
𝑛=1Doymaz
2
2 𝑒𝑥𝑝 �
𝑥𝑜 − 𝑥𝑒
𝑥− 𝑥
8
�
(2𝑛+1)
1
4𝐿
−(2𝑛+1)2 �2 𝐷
2.9
2.9
𝑡
𝑒𝑓𝑓
𝑒
= 2 ∑�
𝑒𝑥𝑝 �
�
2.9
𝑀𝑅 =
𝑛=1 (2𝑛+1)
4 𝐿2time drying Equation 2.9 is expressed as Equation 2.10
−�𝑥𝑒𝑒
�8
− that
�2 𝐷2𝑒𝑓𝑓for
𝑡 a long
𝑜
It has 𝑥�−
been
reported
=
�𝑒𝑥𝑝
�
��
2.10 as Equation 2.10
𝑀𝑅
=
2
2
hasbeen
reported
that
2.92.9
is expressed
4 𝐿 for
�been
�
𝑜 − �𝑒 reported
ItIthas
that
for aa long
longtime
timedrying
dryingEquation
Equation
is expressed
as
(Nguyen & Price 2007; Doymaz 2007):
(Nguyen
&2.10
Price
2007;
2007):
Equation
(Nguyen
& Price
2007;
Doymaz
2007):
It
has been
reported
thatDoymaz
for
a long
time drying
Equation
2.9 is
expressed as Equation 2.10
Integrating
Equation
2.10,
for low
xe values and MR<0.6, can be written in logarithmic form as
(Nguyen
& Price
2007;
Doymaz
2007):
2
− � 𝐷𝑒𝑓𝑓 𝑡
�− �𝑒
8
2 𝐷2
�− �2012):
��
2.10
𝑀𝑅 = �−2.11
Equation
(Islam
et al.,
�𝑒 =
82 �𝑒𝑥𝑝
𝑒𝑓𝑓 𝑡
𝑀𝑅 =
�𝑜 − �𝑒
�𝑜 − �𝑒
�− �𝑒
= �2 �𝑒𝑥𝑝 �
8
�
4𝐿
4 𝐿2
− �2 𝐷𝑒𝑓𝑓 𝑡
��
2.10
= 2 �𝑒𝑥𝑝
�
��
2.10
𝑀𝑅 =
�𝑜 − �8
� �2 𝐷𝑒𝑓𝑓 𝑡 4 𝐿2
𝑒
�
= 𝐿𝑛 2 Equation
− �
�
2.11
𝐿𝑛
Integrating
4 𝐿2 2.10, for low xe values and MR<0.6, can be written in logarithmic form as
�𝑜
�
Integrating Equation 2.10, for low x values and MR<0.6, can be written in logarithmic form as
e
Integrating
Equation
for low
x values and MR<0.6, can be written in
Equation 2.11
(Islam
et2.10,
al.,
Integrating
Equation
2.10,
for 2012):
low x valuese and MR<0.6, can be written in logarithmic form as
Equation 2.11 (Islam et al., 2012):e
logarithmic
form
as
Equation
2.11
(Islam
et al.,time,
2012):
Plotting
the
natural
logarithm
of
moisture
ratio versus
the effective diffusivity is calculated
Equation 2.11 (Islam et al., 2012):
from� the slope
�2 𝐷𝑒𝑓𝑓2.12):
𝑡
8 (Equation
𝐿𝑛
�
= 𝐿𝑛2 82 − ��2 𝐷𝑒𝑓𝑓
2 𝑡�
4𝐿
� �𝐷𝑒𝑓𝑓
− ��2 𝐷𝑒𝑓𝑓
�
𝐿𝑛 ��𝑜 = 𝐿𝑛
𝑡
𝑆𝑙𝑜𝑝𝑒
� 8�2−
4 𝐿2�
𝐿𝑛 �𝑜 ==𝐿𝑛
4 𝐿2 �
2
2
�𝑜
4𝐿
�
2.12
2.11
2.11
2.11
Plotting the natural logarithm of moisture ratio versus time, the effective diffusivity is calculated
The
drying
rate
constant
has been
usingtime,
a first
order
approach
described
Plotting
the
natural
logarithm
ofempirically
moisture
ratio
versus
the
effective
diffusivity
is by
calculated
Plotting
natural
logarithm
of moisture
ratiopredicted
versus
time,
the
effective
diffusivity
is calculated
from thethe
slope
(Equation
2.12):
Equation
2.13
(Baina
&
Langrish,
2007;
Hofsetz
et
al.,
2007;
Doymaz,
2007):
from
the
slope
(Equation
2.12):
from the slope (Equation 2.12):
�2 𝐷
𝑆𝑙𝑜𝑝𝑒 = ��2�2𝐷 𝐷𝑒𝑓𝑓
2 �
𝐿𝑒𝑓𝑓
�
𝑆𝑙𝑜𝑝𝑒 == �� 4 4𝐿𝑒𝑓𝑓
𝑆𝑙𝑜𝑝𝑒
2 2�
4𝐿
2.12
2.12
2.12
Integrating Equation 2.10, for low xe values and MR<0.6, can be written in logarithmic form as
Equation 2.11 (Islam et al., 2012):
Literature Review2
𝐿𝑛
�
�𝑜
= 𝐿𝑛
8
�2
− �
17
� 𝐷𝑒𝑓𝑓 𝑡
�
4 𝐿2
2.11
Plotting the natural logarithm of moisture ratio versus time, the effective
Plotting theisnatural
logarithm
of moisture
ratio(Equation
versus time,
the effective diffusivity is calculated
diffusivity
calculated
from
the slope
2.12):
from the slope (Equation 2.12):
𝑆𝑙𝑜𝑝𝑒 = �
�2 𝐷𝑒𝑓𝑓
4 𝐿2
�
2.12
The drying
constant
has been
predicted using
a first order
approach
described
The
dryingrate
rate
constant
hasempirically
been empirically
predicted
using
a first
order by
Literature review
17
Equation 2.13
(Baina & Langrish,
2007; 2.13
Hofsetz
et al., 2007;
Doymaz, 2007;
2007): Hofsetz et al.,
approach
described
by Equation
(Baina
& Langrish,
Literature
review 2007):
17
2007;
Doymaz,
�−�
𝑒
Literature
review
= exp(−𝑘𝑡)
𝑀𝑅 =
� −�
𝑀𝑅 =
17
2.13
�𝑜 −�𝑒
�−�𝑒
= exp(−𝑘𝑡)
Where:�𝑜−�
k 𝑒– drying rate constant
2.13
Where: k – drying rate constant
�−�Where:
k – drying
𝑒
= exp(−𝑘𝑡)
𝑀𝑅 =
rate constant
� −�
2.13
𝑜
𝑒
The drying
rate constant can be obtained by integrating Equation 2.13 to get Equation 2.14. The
Where:
k –rate
drying
rate constant
The
drying
constant
can be
obtained
by integrating
Equation 2.13
to get Equation
2.14.
The
The
drying
constant
can
befrom
obtained
by integrating
Equation
2.13 to
get
drying
rate rate
constant
is obtained
the slope
of a plot of
natural logarithm
against time
drying rate constant is obtained from the slope of a plot of natural logarithm against time
Equation
(Equation 2.14.
2.15). The drying rate constant is obtained from the slope of a plot of
The
drying2.15).
rate constant can be obtained by integrating Equation 2.13 to get Equation 2.14. The
(Equation
natural logarithm against time (Equation 2.15).
drying rate constant is obtained from the slope of a plot of natural logarithm against time
(Equation 2.15).
(𝑀𝑅)==−𝑘𝑡
𝐿𝑛 (𝑀𝑅)
−𝑘𝑡
𝐿𝑛
2.14
𝐿𝑛 (𝑀𝑅) = −𝑘𝑡
2.14
2𝐷
�2�𝐷
𝑒𝑓𝑓
𝑒𝑓𝑓
4 𝐿2 2
𝑘
𝑘=
= 𝑆𝑙𝑜𝑝𝑒
𝑆𝑙𝑜𝑝𝑒==� �
𝑘 = 𝑆𝑙𝑜𝑝𝑒 = �
2.14
4𝐿
�2 𝐷𝑒𝑓𝑓
4 𝐿2
� �
�
2.15
2.15
2.15
The
activation
energy
Ea represents
the
energy
required
to break bananathe energy
required
to break
banana-moisture
bonding of a
The activation
energy
E represents
the energy required to break banana-moisture bonding of a
The activation energy aEa represents
moisture
bonding
of
a
water
molecule
and
to
bring
it
to
the
surface
where(Bani
the &
water molecule and to bring it to the surface where the molecule finally evaporates
water molecule and to bring it to the surface where the molecule finally evaporates (Bani &
molecule
finally
(Bani
& Langrish,
2007).
Ininterpreted
the caseasofthe
drying,
theactivation
energy
required
banana-moisture
bonding
of a
The activation
energy
can be
minimum
Langrish,
2007).
In evaporates
theEcase
of drying,
energy to
E
a represents
a,break
Langrish, 2007). In the case of drying, activation energy E a, can be interpreted as the minimum
water
to
the
surface
where
the water-water
molecule
finally
evaporates
(Bani
&
, canittobe
interpreted
as
the
minimum
energy
that
activation
Eabring
energymolecule
that energy
mustand
be to
supplied
break
water-solid
and/or
interactions
and must
to
move
energy that must be supplied to break water-solid and/or water-water interactions and to move
be&interpreted
as the
minimum
Langrish,
the
case
drying,
activation
energy
the
water 2007).
molecules
from
oneofpoint
to another
inwater-water
the
solidE(Demirel
Turhan, and
2003).
be
supplied
to In
break
water-solid
and/or
interactions
to Arrhenius’
move
a, can
the water
from one
point to
another
in
the solid
(Demirel
&diffusivity
Turhan,
2003).
Arrhenius’
energy
that
must to
bedescribe
supplied
to
break
water-solid
and/or
interactions
to move
Equation
ismolecules
used
temperature
on
moisture
(Nguyen
&
the
water
molecules
fromthe
one
point
to dependence
another
in water-water
thethe
solid
(Demirel
&and
Turhan,
Equation
is Doymaz,
used tofrom
describe
the to
temperature
dependence
on &the
moisture
diffusivity
(Nguyen &
the
water
molecules
one
point
another
in
the
solid
(Demirel
Turhan,
2003).
Arrhenius’
Price,
2007;
2007)
(Equation
2.16):
2003). Arrhenius’ Equation is used to describe the temperature dependence
Price,
Doymaz,
2007)the
(Equation
2.16):
Equation
is used
to diffusivity
describe
temperature
dependence
on Doymaz,
the moisture2007)
diffusivity
(Nguyen &
on
the2007;
moisture
(Nguyen
& Price,
2007;
(Equation
Price, 2007; Doymaz, 2007) (Equation 2.16):
2.16):
𝐸𝑎
𝐷𝑒𝑓𝑓 = 𝐷𝑜 𝑒𝑥𝑝 �−
𝐷𝑒𝑓𝑓 =
𝑅𝑇
�
𝐸
𝐷𝑜 𝑒𝑥𝑝 �− 𝑎 �
𝐸𝑎𝑅𝑇
𝑅𝑇
�
2.16
2.16
2.16
Equation 2.16 can be written in logarithmic form as Equation 2.17:
𝐸
Equation
𝑙𝑛𝐷𝑒𝑓𝑓 = 2.16
𝑙𝑛𝐷𝑜 can
− 𝑎be written in logarithmic form as Equation 2.17:
𝑅𝑇
𝐸𝑎
𝑎
𝑙𝑛𝐷𝑒𝑓𝑓 == 𝑙𝑛𝐷
𝑙𝑛𝐷𝑜−−𝑎
𝑙𝑛𝐷
𝑅𝑇 is calculated from the slope of the plot on ln(D ) versus 1/(T).
𝑒𝑓𝑓
The
activation𝑜 energy
𝑅𝑇
eff
𝐸
2.17
2.17
2.17
𝐸
𝐷𝑒𝑓𝑓 = 𝐷𝑜 𝑒𝑥𝑝 �−𝐸𝑎 𝑎 �
18
𝑅𝑇
�
𝑅𝑇
2.16
2.16
Literature Review
Equation
2.16 can be written in logarithmic form as Equation 2.17:
𝐸 𝐸𝑎
𝑎
𝑙𝑛𝐷
𝑙𝑛𝐷𝑒𝑓𝑓
= 𝑙𝑛𝐷
𝑙𝑛𝐷
𝑜 𝑜−−
𝑒𝑓𝑓 =
2.17
𝑅𝑇𝑅𝑇
The
activation
energy
is calculated
the
slope
of ln(D
the plot
on ln(D
The
is is
calculated
from
thefrom
slope
of the
versus
1/(T).eff) versus
The activation
activationenergy
energy
calculated
from
the slope
ofplot
theon
plot oneff)ln(D
eff) versus 1/(T).
1/(T).
𝐸𝑎 = 𝑅 ∗ 𝑆𝑙𝑜𝑝𝑒
𝐸𝑎 = 𝑅 ∗ 𝑆𝑙𝑜𝑝𝑒
2.18
2.18
2.17
Establishment of optimum harvest maturity window for matooke
19
Chapter Three:
Objective: Establishment of optimum harvest maturity
window for matooke
3.1
Background
Matooke has now become both a food and cash crop in Uganda. In the event
of commercial processing of it into flour, it requires the flour to have a uniform
quality on the market at all times. This calls for processing matooke of uniform
physicochemical properties, which can be reproduced on the market. It was
therefore, imperative to establish the harvest maturity window as an initial input
to achieving flour of the same physicochemical properties. The objective of
this study was to establish the optimum harvest maturity window for matooke
through the following specific objectives:
To establish the effect of harvest maturity with respect to:
1. Maturity indices.
2. Pasting properties of matooke flour.
3. Starch content.
3.2
Materials and methods
Samples were obtained from thr banana plantation established on 20th
December 2007 at Presidential Initiative on Banana Industrial Development
(PIBID) field station in Bushenyi, Western Uganda. A completely randomized
design (CRD) was employed in selecting the banana stools from which
samples for the experiments were picked. The banana stools were monitored
and coded as soon as the flower shot up (inflorescence emerging from the
stem) (Figure 3.1), in order to establish harvest maturity at the time of picking
samples of the banana fruit. The cultivar Mbwazirume (Musa sp triploid
acuminate genome group AAA-EAHB) which is soft cooking and commonly
grown was selected for the study.
20
Figure 3.1: Illustration of (from left to right) inflorescence emerging from the
stem, coding on the matooke stool, growing bunch on the matooke stem in
the banana plantation.
The maturity indices considered in these investigations were: circumference,
finger weight, pulp-peel ratio, color of the pulp, moisture content and drymatter content of the fingers for matooke at different harvest maturity (10 - 23
weeks). The samples for determining pasting properties and starch content of
flour were dried at 55˚C in a cabinet dryer.
A total of five fingers were picked at random from a bunch from middle to top,
because fingers from the bottom are 30 – 40% smaller than those from top
(Robinson & Saúco, 2010). The fruit circumference was measured by a tape
measure around the middle of the fruit. The fruit weights were measured on an
electronic balance. The fingers were hand- peeled, and then the pulp and peel
were weighed separately on the electronic balance. The same data was used
to calculate the pulp-peel ratio. The moisture content of fresh matooke was
determined using the oven method at 105˚C for 24 h (Sablani et al., 2007; Bani
& Langrish, 2007). Five fingers from each sample bunch of a given harvest
maturity were sliced in the middle and a total of five slices for each finger were
photographed. The photographs were taken using a digital camera with a ring
of light to provide clear photographs.
21
Table 3.1 Specification of the measuring equipment used in the study
Type of
measurement
Equipment
Model
Manufacturer
Accuracy
Finger
circumference
Tape measure
Finger, pulp &
peel weight
Electronic
balance
Explorer Pro
RS232
OHAUS
Corporation
±0.01 g
Pulp color
IC Capture 2.0
camera
Sony
ICX204AK
Sony Corporation
1024x768
pixel
Slicing
Slicing machine
V20,
Gabr. Graef
GmbH & Co. KG
±0.1mm
Milling flour
Starch Mill
IKA M20
IKA WERKER
GmbH & Co.
Pasting
properties
Rapid Viscosity
Analyzer
Model Series 4 Newport Scientific
Pty Limited,
Warriewood,
Australia
Starch content
Polarimeter
Model AP 300
Air velocity
Hot wire
anemometer
±0.02 m/s
Air and dew
temperatures
Pt-100 Thermal
resistor
±0.2K
OHAUS
Corporation,
Parspary, USA
±0.01˚
Table 3.1 above shows the measuring equipment used in these investigations.
All experiments were carried out at the PIBID - Technical Business Incubator
(TBI).
The samples for determining pasting properties and starch content were
harvested at harvest maturity from Week 10 – 23, washed, peeled and sliced
to specified uniform thickness using a slicing machine. The sliced samples
were pretreated with 1% sodium metabisulphite solution as per Patent No.
AP/P/2005/003308 (Muranga 2010) and dried at 55oC. The dried matooke
chips were milled to flour using the starch mill with double-walled grinding
chamber cooled with running water.
22
Pasting Properties
The pasting properties were determined using the Rapid Visco Analyzer (RVA).
The following pasting properties were recorded: peak time, peak viscosity,
pasting temperature, breakdown, holding strength, setback and final viscosity.
The pasting properties were determined using the following procedure: 3 g
of flour (adjusted to 14% wb) and 25 g of distilled water (a total of 28 g) were
placed in an RVA canister. The moisture content of the sample was used to
determine the amount of distilled water to be added in order to maintain the
same amount of solids in the sample. The RVA pasting curve was obtained by
using a 13-minute test profile which included heating to 90oC for 5 minutes,
holding at 90oC for 3 minutes, and cooling to 50oC for 5 minutes. The peak
viscosity, breakdown, set-back, peak time and final viscosity were obtained
using the RVA software.
Starch Content
The starch content was determined using a polarimeter by employing the
general polarimeter method (Kirk & Sawyer, 1991) in two parts: total optical
rotation (P) and optical rotation (P’) in duplicates, plus the blank samples.
2.5 g of matooke flour was weighed in flask of 100 ml, and 50 ml of HCL (11.28
g/l) was added mixed well then placed in boiling water bath for exactly 15 min
(the flask was continuously shaken while in bath for the first 3 min). The flask
was removed from the bath, added 20 ml of cold water and left it cool to room
temperature. The cold sample was then put in 100 ml volumetric flask, 5 ml of
Correz 1 solution was added and shaken for 1 min then 5 ml of Correz II was
added and shaken well, it was then diluted with distilled water to 100 ml mark,
and filtered. The filtrate was used to determine the optical rotation (P) using
the polarimeter cell 200 mm (Range 4o – 8o)
5 g of matooke flour sample was weighed and placed in a volumetric flask of
100 ml, 40% ethanol was added to 100 ml mark and shaken for 6 times in 1
hr then filtered. 50 ml of filtrate was placed in 250 ml quick-fit conical flask,
2.1 ml HCL (density = 1.126), was added and shaken, then put on boiling
plate with reflux cooler for 15 min and allowed to cool to room temperature.
After cooling, 50 ml of solution was put in volumetric flask of 100 ml, 5 ml of
Carrez I was added and shaken for 1 min and 5 ml of Carrez II was added
then diluted with distilled to the 100 ml mark, shaken and filtered. The filtrate
Establishment
harvest
maturity
window
for matooke
Establishmentofofoptimum
optimum
harvest
maturity
window
for matooke
23
2
23
Establishment of optimum harvest maturity
window for matooke
′)
was to determine the optical2000(𝑃−𝑃
rotation
(P’), using the polarimeter cell 200 mm
′)
3.1
𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑜𝑓 𝑆𝑡𝑎𝑟𝑐ℎ (𝑑𝑏) =
2000(𝑃−𝑃
o
𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒
(𝑑𝑏) = 𝛼
).𝑆𝑡𝑎𝑟𝑐ℎ
(Range
4o – 8𝑜𝑓
𝛼
Establishment
of
optimum harvest maturity
window for matooke
23
3.1
23
Angular rotation (α) at 20˚C for matooke flour was approximated to 184.0 for mixed flour (Kirk &
The percentage starch of the sample was calculated from the optical rotation
Angular rotation (α) at 20˚C for matooke
flour was approximated to 184.0 for mixed flour (K
2000(𝑃−𝑃′ )
Sawyer, 1991).
according
mass
as =
follows
(Manufacture’s manual):
3.1
𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒to𝑜𝑓the
𝑆𝑡𝑎𝑟𝑐ℎ
(𝑑𝑏)
𝛼
Sawyer, 1991).
2000(𝑃−𝑃′ )
3.1 flour (Kirk &
𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒
𝑜𝑓 𝑆𝑡𝑎𝑟𝑐ℎ
Angular
rotation
(α) at (𝑑𝑏)
20˚C=for matooke
flour was approximated to 184.0 for mixed
𝛼
Sawyer,
1991). readings for determining the optical rotation were carried out at room
The polarimeter
Angular rotation (α) at 20˚C for matooke flour
was approximated to 184.0 for mixed flour (Kirk &
o
Angular
rotation
(α)the
at recommended
20˚C for
was
to 184.0
for
therefore,
the
specific
angular
rotation
read on
the at
temperature
above
20 C,flour
The polarimeter
readings
formatooke
determining
the approximated
optical
rotation
were
carried
out
Sawyer,
1991).
o
mixed
flour
(Kirk
&
Sawyer,
1991).
o
C using rotation
the equation
polarimeter
was
converted
to the equivalent20specific
angular rotation
at 20angular
C, therefore,
the specific
read o
temperature
above
the recommended
oout at room
belowpolarimeter
(Manufacture’s
manual):
The
readings
for
determining
thespecific
opticalrotation
rotationwere
werecarried
carried
using the equ
polarimeter
wasreadings
converted
todetermining
the
equivalent
angular
rotation
at 20
The
polarimeter
for
the
optical
outC
o
othe specific angular rotation read on the
temperature
above the above
recommended
20 C, therefore,
C,
therefore,
the
specific
at
room
temperature
the
recommended
20
below (Manufacture’s manual):
The polarimeter readings for determining the optical rotation were carried
out at room
o
C using the equation
polarimeter
was converted
the polarimeter
equivalent specific
angular rotation
at 20
angular
rotation
read ontothe
was converted
to the
equivalent
o
C,
therefore,
the
specific
angular
rotation
read on the
temperature above the recommended
20
��ar
r��a��
a� �e�pera��re
�
below
(Manufacture’s
manual):
using
the equation
below (Manufacture’s
specific
angular
at
20oC
3.2
𝐴𝑛𝑔𝑢𝑙𝑎𝑟
𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 rotation
𝑎𝑡 20˚C
= {1+0.000144×(�e�pera��re
o
�−20˚C)}
polarimeter was converted to the equivalent specific
angular rotation at 20 C using the equation
manual):
below (Manufacture’s manual): ��ar r��a�� a� �e�pera��re �
3.2
𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20˚C = {1+0.000144×(�e�pera��re
�−20˚C)}
��ar r��a�� a� �e�pera��re �
𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20˚C =2.�×𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20𝐶
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑡𝑖𝑐𝑎𝑙 𝑅𝑜𝑡𝑎𝑖𝑜𝑛 𝑃 = {1+0.000144×(�e�pera��re �−20˚C)}
𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20˚C = {1+0.000144×(�e�pera��re
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑡𝑖𝑐𝑎𝑙 𝑅𝑜𝑡𝑎𝑖𝑜𝑛 𝑃 =
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑡𝑖𝑐𝑎𝑙 𝑅𝑜𝑡𝑎𝑖𝑜𝑛 𝑃 =
𝑂𝑝𝑡𝑖𝑐𝑎𝑙 𝑅𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑃′ =
Therefore,
Therefore,
Therefore,
𝑇𝑜𝑡𝑎𝑙 𝑆𝑎𝑚𝑝𝑙𝑒 𝑚𝑎𝑠𝑠
𝑆𝑎𝑚𝑝𝑙𝑒 𝑚𝑎𝑠𝑠 𝑓𝑜𝑟 40% 𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑆𝑎𝑚𝑝𝑙𝑒
2.�×𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20𝐶
3.3
3.4
3.4
𝑓𝑜𝑟
40%
�×𝐴𝑛𝑔𝑢𝑙𝑎𝑟𝑆𝑎𝑚𝑝𝑙𝑒
𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛𝑚𝑎𝑠𝑠
𝑎𝑡 20𝐶
𝑓𝑜𝑟
40%𝐸𝑡ℎ𝑎𝑛𝑜𝑙
𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑆𝑎𝑚𝑝𝑙𝑒
𝑆𝑎𝑚𝑝𝑙𝑒
3.4
�×𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20𝐶 𝑓𝑜𝑟 40% 𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑆𝑎𝑚𝑝𝑙𝑒
3.4
184.0
3.3
3.3
�×𝐴𝑛𝑔𝑢𝑙𝑎𝑟 𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑡 20𝐶 𝑓𝑜𝑟 40% 𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑆𝑎𝑚𝑝𝑙𝑒
�2000�𝑃−𝑃′��
Therefore,
x 100
𝑆𝑡𝑎𝑟𝑐ℎ 𝐶𝑜𝑛𝑡𝑒𝑛𝑡 (% 𝑑𝑏) =
Therefore,
3.2
�−20˚C)}
𝑅𝑜𝑡𝑎𝑖𝑜𝑛 �×𝐴𝑛𝑔𝑢𝑙𝑎𝑟
𝑃=
𝑟𝑜𝑡𝑎𝑡𝑖𝑜𝑛
𝑎𝑡 20𝐶
𝑓𝑜𝑟 40% 𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑆𝑎𝑚𝑝𝑙𝑒
𝑇𝑜𝑡𝑎𝑙
𝑆𝑎𝑚𝑝𝑙𝑒
𝑚𝑎𝑠𝑠
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑡𝑖𝑐𝑎𝑙
3.2
3.3
��ar r��a�� a� �e�pera��re �
3.5
The data was analyzed using MINTAB Software (version 14 for Windows
′ �� ′ ��
�2000�𝑃−𝑃
�2000�𝑃−𝑃
(2003)
Minitab
Inc.,
Pennsylvania,
The Pearson
Correlation was used
(%
(%
xSoftware
100
3.5 Inc.,
𝐶𝑜𝑛𝑡𝑒𝑛𝑡
𝑑𝑏)
= 184.0
xUSA).
100
3.5 Minitab
𝐶𝑜𝑛𝑡𝑒𝑛𝑡
𝑑𝑏)
=using
The
data
was analyzed
MINTAB
(version 14 for Windows (2003)
184.0
to determine the significant correlation
between
harvest
maturity
and
maturity
Pennsylvania, USA). The �2000�𝑃−𝑃
Pearson′��Correlation was used to determine the significant correlation
x 100 is greater than 0.5 and a p-value
3.5
𝑆𝑡𝑎𝑟𝑐ℎ 𝐶𝑜𝑛𝑡𝑒𝑛𝑡
=
indices.
When (%
the𝑑𝑏)
Pearson
correlation
184.0
between harvest maturity and maturity indices. When the Pearson correlation is greater than 0.5
(p<0.05), it indicated that the relationship was statistically significant at α-level
and
p-value
indicated
that Software
the relationship
was
significant
at α-level of
The adata
was (p<0.05),
analyzed itusing
MINTAB
(version
14statistically
for Windows
(2003) Minitab
data
was
analyzed ofusing
MINTAB
Software
(version
14 for Windows
(2003) Inc.,
Minitab
ofThe
0.05
(95%
confidence
the relationship).
0.05
(95% confidence
of the
relationship).
Pennsylvania,
USA). The
Pearson
Correlation was used to determine the significant correlation
Pennsylvania,
USA). The
Pearson
was used
to determine
the significant
corre
The
data was analyzed
using
MINTABCorrelation
Software (version
14 for
Windows (2003)
Minitab Inc.,
between harvest maturity and maturity indices. When the Pearson correlation is greater than 0.5
between harvest
maturity
and maturity
indices.
Pearson
greater tha
Pennsylvania,
USA).
The Pearson
Correlation
was When
used tothe
determine
thecorrelation
significant is
correlation
and a p-value (p<0.05), it indicated that the relationship was statistically significant at α-level of
between
harvest(p<0.05),
maturity and
maturity indices.
the Pearson
is greater
than at
0.5α-le
and a p-value
it indicated
that theWhen
relationship
wascorrelation
statistically
significant
0.05 (95% confidence of the relationship).
24
3.3
Results and Discussion
3.3.1 Physiological maturity
The physiological maturity was determined using the meteorological data for
Bushenyi located at the Latitude: 0.59˚S, Longitude: 30.21˚E and Altitude:
1570 m. It was obtained by substituting meteorological data at Bushenyi
during the period of data collection from January 2011 to January 2013 in
equations 2.1 and employing the base Temperature for banana = 14˚C.
Average daily maximum temperature = 25.44 ± 0.35˚C
Average daily minimum temperature =
14.81 ± 0.25˚C
Substitute the above temperatures in equation 2.1 the physiological maturity
was 147 days (21 weeks). This implies that physiological maturity for matooke
cv. Mbwazirume (Musa sp triploid acuminate genome group AAA-EAHB) at
Bushenyi is 21 weeks.
3.3.2 Effect of harvest maturity on maturity indices
The effect of harvest maturity of matooke on maturity indices was studied
by taking harvest maturity (weeks) as independent variable and the maturity
indices as dependent variables. The maturity indices considered in this study
include the following:
•
Finger weight (g)
•
Pulp weight (g)
•
Peel weight (g)
•
Pulp/Peel ratio
•
Finger length (mm)
•
Finger circumference/girth (mm)
The Pearson correlation was used to determine the correlation of different
indices to harvest maturity and amongst themselves as shown in Table 3.2.
It was observed from Table 3.2, that all maturity indices were statistically
significant. This was in agreement with Dadzie (1998), Muranga (1998) and
Robinson & Saúco (2010). From the Table 3.2, the maturity indices with the
highest Pearson correlation above 0.90 were selected as the best maturity
25
indices for estimating the harvest maturity and these include: Finger, peel and
pulp weight.
However, comparing the level of significance, finger length had the lowest
which indicated that the finger had almost the maximum length before week
10; implying that there was slow development in finger length after week 10.
This was in agreement with Robinson & Saúco (2010), who reported that the
finger length increases rapidly for 30 days after which growth in length slows
down and is completed in 40 – 80 days after emergence depending on the
area and climate.
Table 3.2: Pearson correlation for maturity indices
Harvest
Maturity
(weeks)
Finger Weight
(g)
Finger
Weight
(g)
Finger
Length
(mm)
Finger
Circum1
(mm)
Peel
Weight
(g)
Pulp
Weight
(g)
Pulp/
Peel
Ratio
0.939*
0.000**
Finger Length
(mm)
0.276
0.199
0.055
0.170
Finger Circum
(mm)
0.881
0.916
-0.006
0.000
0.000
0.965
Peel Weight
(g)
0.808
0.896
0.060
0.834
0.000
0.000
0.649
0.000
Pulp Weight
(g)
0.958
0.978
0.121
0.919
0.897
0.000
0.000
0.143
0.000
0.000
Pulp/ Peel
Ratio
0.953
0.900
0.332
0.847
0.691
0.933
0.000
0.000
0.021
0.000
0.000
0.000
Moisture
Content
(%wb)
0.794
0.727
0.186
0.694
0.599
0.774
0.766
0.000
0.000
0.201
0.000
0.000
0.000
0.000
Key: Cell Contents - * Pearson correlation
** p - value (p = 0.000 => p < 0.0001)
1 Circumference
26
Figure 3.2 shows the effect of harvest maturity on finger, pulp and peel
weights.
Figure 3.2
300
Finger
Peel
Pulp
Weight (g)
200
100
0
10
15
20
25
Harvest maturity (weeks)
Figure 3.2: Effect of harvest maturity on finger, pulp and peel weights
Weight (g)
It was observed
from Figure 3.2 that both finger and pulp weight increased with
Figure 3.5
increase in time until week 22 then decreased. This indicated that matooke
300
at week 22 had reached Finger
its maximum harvest maturity. The peel weight also
increased with increasing
time until week 21 and then decreased to week
Peel
22. This reduction in peel
weights after week 21 could be attributed to cell
Pulp
200
wall degradation which starts underneath the peel (Robinson & Saúco, 2010).
After week 22, matooke, being a climacteric fruit, it enters the post climacteric
phase at which point dramatic physiological changes occur; these include the
peel color and 100
pulp texture among others. In advanced stages of maturity,
slight peel color change occurs to the initial stages of ripening. The reduction
in finger and pulp weight is attributed to loss of starch, which plays a major
0
role in textural changes
in ripening.
10
15
20
25
27
Figure 3.3, shows that the pulp/peel ratio increased with time from week 10 to
weekEstablishment
22 and then
decreased.
of optimum
harvest maturity window for matooke
29
2.00
Pulp/Peel Ratio
1.50
y = 0.0707x + 0.2437
R² = 0.9829
1.00
0.50
0.00
10
15
20
25
Harvest Maturity (weeks)
Figure 3.3: Effect of harvest maturity on pulp/peel ratio.
3.3: Effect
on pulp/peel
ratio.
FromFigure
Figure
3.3of itharvest
wasmaturity
further
observed
that matooke attains maximum
maturity
the3.3harvest
atobserved
21 weeks
and attains
if it ismaximum
not harvested
starts
From at
Figure
it was further
that matooke
maturity at the itharvest
at going
21 weeks
if it is not harvested itstage
starts going
through the
stage (Muchui
et al., 2012).
through
the and
post-climacteric
(Muchui
et post-climacteric
al., 2010, Tapre
& Jain,
Tapre & Jain, 2012). These results are within comparable range to those reported by
These2010,
results
are within comparable range to those reported by Robinson &
Robinson & Saúco, (2010), that the pulp/peel ratio of 1.0 was achieved at about 70 days, in this
Saúco,
(2010),
that the pulp/peel ratio of 1.0 was achieved at about 70 days,
study was about 77 days (11 weeks).
in this study was about 77 days (11 weeks).
Finger Circumference (mm)
Figure
3.43.4shows
thatthethe
circumference
of increased
the fingers
increased
time
Figure
shows that
circumference
of the fingers
with time
and this waswith
in
agreement
with
Muchui et al., (2010)
and
Robinson
&al.,
Saúco,
(2010), and
when Robinson
they reported that
and this
was
in
agreement
with
Muchui
et
(2010)
&
Saúco,
30
diameter of the fingers increases with bunch age.
(2010),
when they reported that diameter of the fingers increases with bunch
18
age.
12
6
10
15
20
Harvest Maturity (weeks)
25
Figure 3.4:
harvest
maturity
on finger circumference.
Figure Effect
3.4: Effectof
of harvest
maturity
on finger circumference.
The Pearson Correlation was used to determine the correlation of different CIE L*a*b* color
28
The Pearson Correlation was used to determine the correlation of different
CIE L*a*b* color space to harvest maturity and amongst themselves. It was
observed from Table 3.3, that all the different color space were statistically
significant, except the lightness (L*). It was further observed that L* and a*
showed inverse relationship with harvest maturity, whereas the saturation
(C*), b* and hue angle in degrees (α) values showed a direct relationship with
harvest maturity.
Table 3.3: Pearson Correlation for Color Space
Harvest maturity
(weeks)
L*
a*
b*
L*
-0.134*
0.287**
a*
-0.617
0.000
0.442
0.000
b*
0.549
0.000
-0.700
0.000
-0.946
0.000
C*
0.556
0.000
-0.685
0.000
-0.953
0.000
1.000
0.000
α
0.540
0.000
0.491
0.000
-0.395
0.001
0.144
0.252
Key: Cell Contents -
C*
0.161
0.201
* Pearson correlation
** p - value (p = 0.00 => p < 0.0001)
The CIE-Lab parameters were used to express the color of fresh pulp;
Lightness (L*), Saturation (C*) and Hue angle in degrees (α) values are
calculated from equations 2.2 and 2.3. It was observed from Figure 3.5 and
Table 3.3 that lightness (L*) was not statistically significant. The hue angle (α
> 90˚) showed that the color of the pulp lies between green and yellow space
with low saturation (C*). Both hue angle (α˚) and saturation (C*) increased
with harvest maturity. It was noted that hue angle (α) was relatively constant
between weeks 17-20 which indicated that the color of the pulp was relatively
uniform within that harvest maturity range. Because of nearly constant
lightness and low saturation, one cannot easily differentiate between the
differences of colors at different harvest maturity. This indicates that color of
the pulp is not a good index for estimating the harvest maturity.
29
Figure 3.5
300
Finger
Peel
Pulp
Weight (g)
200
100
0
10
15
20
25
Figure 3.5 Effect of harvest maturity on given L*, C* & α
3.3.3 Effect of harvest maturity on the physicochemical
properties of matooke flour
The effect of harvest maturity in weeks of matooke on physicochemical
properties of the flour was studied by taking harvest maturity (weeks) as
independent variable and the physicochemical properties of flour as dependent
variables (starch content and pasting properties).
In order to establish the optimum harvest maturity to harvest matooke it was
imperative to determine the starch development from third week after the
inflorescence emerges from the stem. Figure 3.6, shows the effect of harvest
maturity on starch content of matooke flour. The starch data was collected
from week 3 then at intervals of three weeks to week 9, then collected every
week up to week 22.
Starch Content (%DB)
75
Fitted curve
30
50
y=
100
33
-0.34x2
+ 10.62x + 1.72
R² = 0.99
Experiment
25
0
0
Starch Content (%DB)
75
Fitted curve
50
5
y = -0.34x2 + 10.62x + 1.72
15 R² = 0.99
20
10
25
Maturity (weeks)
25
Figure 3.6: Effect of harvest maturity on starch content (%db) of matooke flour.
0
0
5
10
15
20
25
Maturity (weeks)
Figure 3.6: Effect of harvest maturity on starch content (%db) of matooke
flour.
Figurethat
3.6: Effect
harvest content
maturity on increased
starch content with
(%db) time
of matooke
Figure 3.6, shows
the ofstarch
fromflour.
week 3 - 12 and it became
relatively constant until week 20, when it started to decrease with age. This was an indication that
the Figure
starch accumulates
in the
ascontent
the fruit
continues
tofrom
grow
begins from
3.6,Figure
shows
that
the
starch
increased
with
time
from
week
3 - 12
3.6, shows
that pulp
the
starch
content
increased
with time
week until
3
- 12 maturation
and
it became
relatively
constant
untilmatooke
week 20, when
it started
to decrease
with
age.
This
indication
that
and
became
relatively
constant
until
week
20, when
it 12
started
decrease
week
12.it This
indicated
that
can
be
harvested
from
–was21anto
weeks
because
starch
the starch accumulates in the pulp as the fruit continues to grow until maturation begins from
withofage.
This was
indication
content
matooke
flouran
is above
80%that
(db).the starch accumulates in the pulp as the
week 12. This indicated that matooke can be harvested from 12 – 21 weeks because starch
fruit continues
grow flour
untilis above
maturation
content to
of matooke
80% (db). begins from week 12. This indicated
that matooke can be harvested from 12 – 21 weeks because starch content of
matooke flour is above 80% (db).
The quadratic model
fitting
thefitting
starch
datadata
could
auseful
useful
for estimating
the starch content
The quadratic
model
the starch
couldbe
be a
tooltool
for estimating
the starch content
of matooke
flourfitting
at a given
maturity,
which
to starch
as given in
The quadratic
theharvest
starch
datawhich
couldwas
bereferred
areferred
useful
tool
for estimating
of matooke
flour
atmodel
a given
harvest
maturity,
was
to model
starch
model as given in
equation 3.6.
the starch
equation
3.6. content of matooke flour at a given harvest maturity, which was
referred to starch model as given in equation 3.6.
𝒀 = 𝟏. 𝟕𝟐 + 𝟏𝟎. 𝟔𝟐𝒙 − 𝟎. 𝟑𝟒𝒙𝟐
𝒀 = 𝟏. 𝟕𝟐 + 𝟏𝟎. 𝟔𝟐𝒙 − 𝟎. 𝟑𝟒𝒙𝟐
2
R = 0.99
2
R = 0.99
3.6
3.6
Where:
Where:
Where:
Y = Starch content (%db)
x = Harvest maturity (weeks)
Table 3.4 showed that pasting properties were statistically significant and
also the interactions of all pasting properties have statistically significant
relationship. Table 3.5 shows pasting properties for the matooke. The results
obtained for the pasting properties were in agreement with previous work on
31
matooke (Table 2.2). Figure 3.7, shows the pasting curves were grouped with
respect to peak viscosities being relatively within the same range and selected
harvest maturity 10 &12, 16 & 20 and 22 weeks which are representatives for
range of harvest maturity of 10-15, 15-21 and 22 respectively. It was further
observed from Figure 3.7, that there are three peaks as per the above groupings
week 10-15, 15-21 and 22 from the highest to lowest respectively. The lower
peak suggests that at week 22, the starch granules were transformed by the
initial stages of ripening. This was also observed for effect of harvest maturity
on pulp/peel ratio and other maturity indices.
Table 3.4: Pearson Correlation for Pasting Properties
Peak Viscosity
(RVU)
Harvest
maturity
(weeks)
-0.395*
Peak
Viscosity
(RVU)
Trough
(RVU)
Break- Final
down Viscosity
(RVU) (RVU)
Setback
(RVU)
Peak
Time
(min)
0.001**
-0.274
0.634
0.039
-0.073
0.000
0.247
-0.583
0.568
-0.436
0.049
0.665
0.000
0.967
-0.519
0.001
Setback (RVU) -0.701
0.000
0.242
0.000
0.117
0.000
0.108
0.366
0.000
-0.482
0.054
0.165
0.356
0.675
0.396
-0.679
0.003
0.711
0.308
0.002
-0.321
0.192
-0.106
0.000
0.380
0.000
-0.594
0.000
0.425
0.013
0.268
0.852
0.010
0.402
0.002
0.000
0.000
0.032
0.000
Trough (RVU)
Breakdown
(RVU)
Final Viscosity
(RVU)
Peak Time
(min)
Pasting
Temperature
(˚C)
Key: Cell Contents - * Pearson correlation
** p - value (p = 0.00 => p < 0.0001)
32
Table 3.5: Pasting property of Matooke flour (cv Mbwazirume)
Peak Viscosity
(RVU)
Trough
Viscosity
(RVU)
Breakdown
Viscosity
(RVU)
Final
Viscosity
(RVU)
Setback
Viscosity
(RVU)
Peak Time
(min)
Pasting
Temperature
(oC)
10
427.83±23.02
213.33±16.03
214.50±14.73
277.53±5.99
64.19±10.44
4.84±0.08
78.63±0.42
11
407.56±14.74
223.20±16.55
175.36±24.17
281.82±20.56
49.63±7.87
4.92±0.13
79.94±1.52
12
442.43±11.19
296.93±31.78
145.50±34.25
347.73±28.59
50.80±4.21
5.04±0.20
78.69±4.49
13
448.07±32.63
299.14±28.21
148.99±7.91
346.36±21.80
47.22±9.29
5.17±0.04
82.12±1.46
14
445.49±7.61
290.61±20.73
154.88±25.37
342.96±20.47
52.35±4.57
4.92±0.06
79.98±0.34
15
401.87±33.52
279.28±19.24
122.58±16.64
327.25±11.11
47.97±8.53
5.15±0.04
83.88±1.03
16
408.90±53.53
257.28±48.10
151.62±11.40
297.20±47.90
39.92±0.86
4.96±0.08
79.31±2.73
17
414.26±16.92
243.83±3.73
170.43±15.52
279.14±4.13
35.31±3.91
4.68±0.02
74.76±0.59
18
399.07±11.22
244.40±39.53
154.67±35.54
286.79±41.15
42.39±2.63
4.84±0.21
78.98±2.02
19
404.34±59.87
228.13±19.98
176.21±79.85
269.92±10.02
41.79±9.96
4.80±0.28
79.60±3.96
20
400.00±6.55
235.67±7.60
164.33±4.45
274.58±7.69
38.92±1.31
4.73±0.07
76.48±1.46
21
412.89±6.69
240.50±10.90
172.39±12.11
278.68±8.63
38.18±4.35
4.72±0.10
77.26±0.75
22
366.44±13.74
214.22±4.57
152.22±9.43
238.14±2.30
23.92±2.39
4.80±0.00
79.37±0.41
Harvest
maturity
(weeks)
10
33
12
16
20
22
Figure 3.7: Pasting curves for Matooke. (.....) Peak viscosity in three groups
represented by harvest maturity 10 &12, 16 & 20 and 22 weeks which
are representatives for range of harvest maturity of 10-15, 15-21 and 22
respectively.
Figure 3.8 shows the effect of harvest maturity on peak viscosity and final
viscosity.
Figure 3.8
500
Viscosity (RVU)
400
300
200
Peak Viscosity (RVU)
Final Viscosity (RVU)
100
10
15
20
25
Maturity (weeks)
Figure 3.8: Effect of harvest maturity on peak viscosity and final viscosity.
34
It was observed from Figure 3.8 that there were two levels of peak viscosity
between weeks 12-14 and weeks 15- 21. The first peak between was weeks
12 – 15. This indicated a third stage of banana growth referred to as maturation
(Stover & Simmonds, 1987, Robinson & Sauco, 2010). This implied within
this period starch is being deposited and it had not yet stabilized, in other
words, it was still in transition. Robinson & Sauco, (2010) reported that starch
grains are initially deposited in the pulp cells which form in the vicinity of the
vascular bundles, and thereafter starch deposition moves centripetally and
continues until harvest maturity. This peak between 12 – 15 weeks could be
associated with amylose and amylopectin content were not yet fully developed
or stabilized which is responsible of the functional characteristics of flour.
Therefore, the high peak viscosity could be attributed to fiber and components
in the flour causing the resistance to the shearing leading to high viscosity.
The second peak viscosity from 15 – 21 weeks was rather uniform, which
was an indication that period was beyond the maturation stage, and starch
deposition had stabilized. This implied that both amylose and amylopectin
molecules had reached the equilibrium point between swelling and polymer
leaching, at nearly the same viscosity, which gave a relatively equal peak
viscosity and final viscosity.
3.3.4 Prediction Models
The following models were developed from the effect of harvest maturity on
the maturity indices by employing the regression equation using the MITAB
Software. Considering the fact that week 22 had a unique response for all
maturity indices, it was omitted from the regression analysis for predicting the
harvest maturity for matooke to be harvested for processing flour.
Table 3.6 shows the models which could be used to predict harvest maturity
(weeks) given the finger weight (g), or pulp/peel ratio, or a combination of
finger weight and pulp/peel ratio. However, it was observed that, the model of
a combination was the best followed by the pulp/peel model.
35
Table 3.6 Models for predicting harvest maturity
Model Parameters
Model
R2
r
P<
Harvest maturity (weeks) &
Finger weight (g)
Y = 3.29 + 0.07x1
0.87
0.939
0.01
Harvest maturity (weeks) &
Pulp/Peel ratio
Y = 11.9x2 – 0.298
0.91
0.958
0.01
& Finger weight (g) + Pulp/
Peel ratio (Combined)
Y = 0.387 +
0.031x1 + 7.27x2
0.94
Where:
Y = Harvest maturity (weeks)
x1 = Finger weight (g)
x2 = Pulp/Peel ratio
0.01
The model equation given in Table 3.7 can be used to predict the amount
of starch at a given harvest maturity (weeks) of matooke after inflorescence
emergence.
Table 3.7 Models for predicting starch content
Model Parameters
Model
R2
p<
Starch content (%db) &
Y = 1.72 + 10.62x – 0.34x2
0.99
0.016
Where:
Y = Starch content in flour (%db)
x = Harvest maturity of matooke (weeks)
3.3.5 Validation of prediction models
The above models given in Table 3.6 were validated by using a new set of
experiments to determine the actual values for selected maturity indices (finger
weight, pulp/peel ratio) at different harvest maturity from week 10 - 22. The
analysis of variance (ANOVA) was employed to determine the significance of
the relationship between the actual experimental data and the model predicted
values (Table 3.8). It was observed that the selected models were significant
(p<0.01), it indicated that the relationship was statistically significant at α-level
of 0.01(99% confident of the estimated harvest maturity). The combined
Finger weight with Pulp/peel ratio was the best followed by the Pulp/peel ratio
model. Therefore, the models in Table 3.6 can be adapted for estimating the
harvest maturity of matooke at Bushenyi.
36
Table 3.8 Significance of prediction model for estimating harvest
maturity
Model
R2
p<
Finger weight model
0.87
0.01
Pulp/peel ratio model
0.89
0.01
Combined finger weight and pulp/peel ratio model
0.92
0.01
3.4
Conclusions and Recommendations:
The physiological maturity of matooke (Mbwazirume cv. Musa sp. AAA EAHB)
at Bushenyi located at the Latitude: 0.59˚S, Longitude: 30.21˚E and Altitude
1570m is 21 weeks. Considering the results of starch content and pasting
properties, the uniform physicochemical properties were obtained between
weeks 15 – 21. This indicates that the optimum harvest maturity window for
commercial processing of matooke at Bushenyi is between 15-21 weeks for
processing standard raw matooke flour or Raw Tooke Flour (RTF).
However, in case of natural disaster like storm, matooke from the harvest
maturity from week 12-15 weeks can be processed into matooke flour since at
that harvest maturity it already has starch content above 80% (db).
In addition, matooke harvested beyond the above maturity window after 21
weeks is recommended for processing Instant Tooke Flour (ITF). This is
because beyond the recommended harvest maturity window, it would be in
the initial stages of starch loss which plays a major role in textural changes
in ripening which would not affect the functional properties of instant flour
significantly.
Theoretically the results of the study can be used elsewhere. Nevertheless,
it is recommended that similar studies be carried out in other the matooke
growing areas to determine the applicable models for those particular regions
since development of matooke and bananas in general is subject to location
(Robinson & Saúco, 2010)
Recommended Characteristics within the Maturity Window
The finger weight model is recommended for farmers to estimate harvest
maturity for matooke and the combined model of finger weight and pulp peel
37
ratio is recommended for commercial processors. Matooke cv mbwazirume
(Musa sp. AAA-EAHB) at Bushenyi, harvested within the optimum harvest
maturity window from week 15 – 21, should have the following characteristics
given in Table 3.10 and expected starch content ≥ 80 % (db).
Table 3.10 Characteristics of matooke cv mbwazirume (Musa sp. AAAEAHB)
Characteristics
Range
15 – 21
Finger weight (g)
190-234
Pulp/peel ratio
1.4 -1.8
Establishment of moisture sorption isotherms for matooke
38
Chapter Four
Objective: Establishment of moisture sorption isotherms
for matooke
4.1
Background
The commercial processing of matooke requires having microbiologically
shelf-stable products on the market. Moisture content is an important criterion
for judging food quality and water activity (aw), which is essential for describing
the water availability and mobility in food (Ayala et al., 2011). Previous research
on moisture isotherms focused on other types of bananas and plantains, but as
reported by Robinson and Saúco, (2010), the East African Highland bananas
are a distinct ‘AAA group’. No prior moisture sorption isotherms have been
reported on the East African Highland bananas (matooke, Musa sp. AAAEAHB). In order to achieve the objective of having a microbiologically shelfstable product it was important to establish its moisture sorption isotherm. The
main objective of this study was therefore to establish the moisture sorption
isotherms for matooke. The specific objectives were to determine:
•
The effect of temperature on the moisture sorption isotherm
•
The existing models which best predict the moisture sorption isotherms
•
The hysteresis behavior
•
The sorption heat.
4.2
Materials and methods
The static gravitation method recommended by COST 90 Project (Wolf et al.,
1985), was used for establishing both adsorption and desorption isotherms.
3 g of samples were put in air-sealed glass jars maintained at equilibrium
relative humidity using saturated salt solution. The glass jars were placed in
an incubator (BINDER CB Series, ILMACK, Basel, Switzerland) to maintain
the required set temperature (Sablani et al., 2007).
•
Dried samples used to determine the adsorption isotherm at 30 and
60˚C in were maintained at relative humidity ranging from 10 to 90% by
the static method employing standard saturated salt solutions. The dry
39
samples were kept in desiccators with silica gel at room temperature for
2 days prior to the beginning of experiments (Pahlevanzadeh & Yazdani,
2004; Yazdani et al., 2006).
•
Fresh samples used to determine the desorption isotherm at 50 and 60˚C
were maintained at relative humidity ranging from 10 to 90% by the static
method employing standard saturated salt solutions.
•
The final equilibrium moisture content of samples was determined by the
oven method at 105˚C for 24 h and results expressed on dry weight basis
(Baini & Langrish, 2007).
The temperatures selected for desorption isotherm determination are normally
used for drying of bananas (Queiroz & Nebra, 2001; Karim & Hawlader,
2005; Nguyen & Price, 2007). The adsorption isotherm temperature of 30˚C
was selected because it corresponds with the maximum ambient conditions
in Uganda in terms of considering the storage conditions. The adsorption
and desorption data at 60˚C were selected for establishing the hysteresis
phenomenon. The salt solutions used to obtain constant relative humidity of
the surrounding air from 10 to 90% are given in the Table 4.1; all the salts used
were of analytical grade.
Table 4.1 Relative humidity over saturated salts at 30, 50 and 60˚C
Salt Solution
Relative Humidity (%)
30˚C
50˚C
60˚C
LiCl
11.28
11.21
10.95
CH3COOK
21.61
19.20
18.00
MgCl2
32.44
30.54
29.26
K2CO3
43.17
42.65
42.11
Mg(NO3)2
51.40
46.16
45.44
NaNO2
73.14
69.04
67.27
NaCl
75.09
74.34
74.20
KCl
83.62
81.20
80.25
Statistical Analysis
The experiments were carried out in triplicates and the results were analyzed
by using SPSS (version 16) for Windows (© 2010 University of Bristol, UK). It
minimizes the sum of squares of derivatives between experimental and theory
in a series of iterative steps. It also evaluates the parameters/ constants of
40
the model and goodness-of-fit (R2). The suitability of the models was further
evaluated and compared using the following statistical errors:
•
The root mean square error (RMSE, equation 4.1), gives the fitting ability
of a model in relation to number of data points, the smaller the value the
better the fitting ability of an equation (Hassian et al., 2001; Mehta &
Singh,
2006; Siripatrawan
& Jantawat,
2006;for
Moreira
et al., 2008; Seid &
Establishment
of moisture sorption
isotherms
matooke
2
Hensel, 2012).
∑𝑛
�𝑥
−𝑥
�
𝑝𝑟𝑒𝑑
𝑖=1 𝑖
𝑅𝑀𝑆𝐸 = �
𝑅𝑀𝑆𝐸 = �
•
4.1
𝑛
2
∑𝑛
𝑖=1�𝑥𝑖 −𝑥𝑝𝑟𝑒𝑑 �
4.1
𝑛
Mean relative percentage deviation modulus (E: equation 4.2). This has
 Mean relative percentage deviation modulus (E: equation 4.2). This has been widely use
been widely used and it has been recommended that the E value less
been
the E
less than 10% is (Mehta
indicative
a good fit for practic
than
10% recommended
is
indicative
ofthat
a good
fit value
for practical
& of
Singh,
 Mean
relative
percentage
deviation
modulus purposes
(E: equation 4.2). This
has been widely use
(Mehta
& Singh, 2006;
Siripatrawan
Jantawat,
Yan
et al., 2008;
Moreira et al., 20
2006;
Siripatrawan
& Jantawat,
2006;& Yan
et al.,2006;
2008;
Moreira
et al.,
been recommended that the E value less than 10% is indicative of a good fit for practic
2008).
(Mehta & Singh, 2006; Siripatrawan & Jantawat, 2006; Yan et al., 2008; Moreira et al., 20
𝐸 =
100
𝑛
∑𝑛𝑖=1
�𝑥𝑖 − 𝑥𝑝𝑟𝑒𝑑 �
4.2
𝑥𝑖
46
�𝑥 − 𝑥
�
∑𝑛𝑖=1 𝑖 𝑝𝑟𝑒𝑑
𝑥𝑖
𝑛
4.3 Results and Discussion
𝐸 =
100
4.2
4.3.1
Effect of temperature on moisture isotherm for matooke
4.3.1 Effect of temperature on moisture isotherm for matooke
Figures 4.1 and 4.2 show the adsorption and desorption isotherms
Figures 4.1 and 4.2 show the adsorption and desorption isotherms respectively.
respectively.
0.3
(kg water/kg drymatter)
30˚C
60˚C
0.2
0.1
0.0
0
0.2
0.4
0.6
Water activity, aw
Figure
4.1 Experimental data adsorption isotherm
Figure 4.1 Experimental data adsorption isotherm
0.8
1
47
41
0.3
(kg water/kg dry matter)
50˚C
60˚C
0.2
0.1
0.0
0
0.2
0.4
0.6
Water activity, aw
0.8
1
Figure
4.2Experimental
Experimental
data isotherm
desorption isotherm
Figure 4.2
data desorption
It was observed from Figures 4.1 and 4.2, that the moisture sorption isotherms
was observed
Figures
4.1 and 4.2, that
the moisture sorption
isotherms in bothmoisture
cases were at a
in Itboth
casesfrom
were
temperature
dependant.
The equilibrium
temperature dependant. The equilibrium moisture at a given water activity, decreased with increase in
given water activity, decreased with increase in temperature. This was in
temperature. This was in agreement with the theory of physical sorption (Iglesias et al., 1975; Hassian et
agreement
withthat
thematooke
theory
of physical
sorption
(Iglesias
et al.,This
1975;
Hassian
al., 2001). Implying
became
less hygroscopic
with increasing
temperature.
was similar
the 2001).
report on starch
(Al-Muhtaseb
et al., 2004), became
pitahya fruit (Ayala
al., 2011) and coffee
et
et toal.,
Implying
that matooke
less et
hygroscopic
with(Corrêa
increasing
al., 2010). Both adsorption and desorption isotherms for matooke exhibited Type II curves which is more
temperature. This was similar to the report on starch (Al-Muhtaseb et al.,
pronounced for desorption moisture isotherm. It was consistent with previous researchers on bananas
2004),
pitahya fruit (Ayala et al., 2011) and coffee (Corrêa et al., 2010). Both
and plantains (Johnson & Brennan, 2000; and Yan et al., 2008).
adsorption and desorption isotherms for matooke exhibited Type II curves
which is more pronounced for desorption moisture isotherm. It was consistent
4.3.2 Fitting Models to Moisture Sorption Isotherms for Matooke
with
previous researchers on bananas and plantains (Johnson & Brennan,
The fitting
the moisture
isotherms was done using the nonlinear regression analysis. Figures
2000;
andof Yan
et al.,sorption
2008).
4.3, 4.4, 4.5 and 4.6 show the best fitted models given in Table 4.2, for both adsorption and desorption
moisture isotherms for matooke.
4.3.2 Fitting Models to Moisture Sorption Isotherms for
Matooke
The fitting of the moisture sorption isotherms was done using the nonlinear
regression analysis. Figures 4.3, 4.4, 4.5 and 4.6 show the best fitted models
given in Table 4.2, for both adsorption and desorption moisture isotherms for
matooke.
42
48
0.3
Xexp
Equilibrium
Equilibrium Moisture
ContentMoisture Content
Establishment of moisture sorption isotherms
for matooke
GAB
48
Chung-Pfost
0.3
0.2
Oswin
Xexp
Smith
GAB
Chung-Pfost
0.2
0.1
Oswin
Smith
0.1
0
0
0.2
0.4
0.6
Water Activity, aw
0.8
1
Figure 4.3 Best fitted models for adsorption isotherms of matooke at 30˚C
Figure 4.3 Best fitted
models for adsorption isotherms of matooke at 30˚C
0
0.3
0
0.2
Xexp
0.4
0.6
Water Activity, aw
0.8
1
Equilibrium
Equilibrium Moisture
ContentMoisture Content
BET
GAB
0.2
0.3
Xexp
Chung-Pfost
BET
Smith
GAB
0.1
0.2
0.0
0.1
Chung-Pfost
Smith
0
0.2
0.4
0.6
0.8
1
Water Activity, aw
0.0
Figure 4.4 Best fitted models0 for adsorption
of matooke at
0.2 isotherms0.4
0.660˚C
0.8
1
Water Activity, aw
49
43
49
0.3
Xexp
Equilibrium Moisture Content
Equilibrium(kg
Moisture
Content
water/kg
dry matter)
0.3
0.2
0.2
GAB
Xexp
Henderson
GAB
Smith
Henderson
Smith
0.1
0.1
0
0
0.2
0
0.4
0.6
Water Activity, aw
0.8
0
0.2
0.4
0.6
Figure 4.5 Best fitted models for desorption isotherms of
matooke
at 50˚C
Water
Activity,
a
1
0.8
1
w
Figure 4.5 Best fitted models for desorption isotherms of matooke at 50˚C
0.3
Equilibrium(kg
Moisture
Content
water/kg
drymatter)
Xexp
0.3
0.2
0.2
BET
Xexp
GAB
BET
Iglesias-Chirife
GAB
Iglesias-Chirife
0.1
0.1
0.0
0
0.0
0.2
0.4
0.6
Water Activity, aw
0.8
0
0.2
0.4
0.6
Figure 4.6 Best fitted models for desorption isotherms
of matooke
Water
Activity, at
a 60˚C
0.8
1
1
w
44
Table 4.2 Model constants and statistical errors for adsorption and
desorption isotherms
Model
Constants
Adsorption
30˚C
BET
Mm
C
R2
Desorption
60˚C
50˚C
60˚C
0.044
13.721
0.960
15.064
0.0104
0.038
22.020
0.977
5.294
0.0074
0.0519
24.845
0.829
19.810
0.0262
0.035
17.330
0.971
5.028
0.0131
0.087
4.104
0.737
0.983
3.091
0.0022
0.050
16.051
0.971
0.978
3.653
0.0072
0.059
20.572
0.862
0.999
1.255
0.0018
0.031
0.627
0.800
0.997
5.868
0.0040
E (%)
RSME
91.743
14.780
0.997
3.032
0.0028
192.427
14.696
0.931
1.366
0.0127
208.049
14.814
0.963
12.543
0.0122
73.618
8.762
0.995
4.733
0.055
Smith
C1
C2
R2
E (%)
RSME
0.017
0.108
0.934
8.712
0.0053
0.021
0.110
0.964
2.404
0.0093
0.031
0.097
0.997
2.857
0.0035
-0.032
0.140
0.999
2.937
0.0030
IglesiasChirife
C1
C2
R2
0.035
0.046
0.934
25.075
0.0134
0.039
0.049
0.966
9.011
0.0089
0.065
0.027
0.945
17.414
0.0149
0.034
0.034
0.970
6.657
0.1488
1.495
5.433
0.961
15.824
0.0336
2.282
3.311
0.905
15.978
0.0149
0.740
15.146
0.976
10.998
0.00984
9.806
0.879
0.963
20.012
3.2300
E (%)
RSME
GAB
Mm
C
k
R2
E (%)
RSME
Chung-Pfost
C1
C2
R2
E (%)
RSME
Henderson
C1
C2
R2
E (%)
RSME
45
Table 4.2 shows the model constants and statistical errors for adsorption and
desorption isotherms. It was observed from Table 4.2, that the relationship
between monolayer for BET and GAB for all cases, both adsorption and
desorption are consistent with Timmermann et al., (2001), for which the BET
monolayer was less than the GAB monolayer; and the constant (c) for BET
was greater than that of GAB in both adsorption and desorption isotherms.
All the BET and GAB monolayer values fall within the monolayer for starchy
foods which generally range from 0.032 – 0.16 (kg water/kg dry matter)
(Siripatrawan & Jantawal, 2006). Also, the GAB constants for adsorption are
0.737 and 0.971 at 30˚C and 60˚C respectively, while those of desorption are
0.862 and 0.800 at 50˚C and 60˚C respectively. This shows that they all fall
within the recommended range of: 0.7 < k < 1 (Timmermann et al., 2001).
The GAB monolayer has been reported to be indicative of optimum moisture
content for storage conditions for hydrated foods (Yan et al., 2008; Moreira et
al., 2008; Veg-Galvez et al., 2007; Pahlevanzadeh & Yazdani, 2005; Awoyele,
2004). For the case of matooke, the GAB monolayer for adsorption isotherm
at 30˚C was 0.087, which was below the recommended safe water activity
level of 0.6 (Labuza et al., 1972; Yan et al., 2008; Sahu & Tiwari, 2007). The
water activity level of 0.6 corresponds with equilibrium moisture content of 0.11
(kg water/kg dry-matter), equivalent to 10% (wb), on the adsorption isotherm
at 30˚C. This implies that the equilibrium moisture content for microbiologically
shelf-stable dried matooke is 10% (wb). In this study, all the subsequent drying
experiments, the samples were dried up to moisture content of 10 % (wb)
which was used as basis for analysis.
The six models fitted to describe both the adsorption and desorption isotherms
for matooke were: BET, GAB, Henderson, Iglesias-Chirife, Smith and ChungPfost. The models with R2 > 0.95 and E ≤ 10% were considered to be the best
fitting models to describe matooke moisture isotherm. Figure 4.3 shows the
best fitted models for adsorption isotherms which include: GAB, Chung-Pfost,
and Smith for adsorption at 30˚C and Figure 4.4 shows that BET, GAB, ChungPfost, Smith and Iglesiau-Chirife are the best fitting models for adsorption at
60˚C. While Figure 4.5 shows the best fitting for desorption isotherms at 50˚C
which include: BET, GAB and Henderson, and Figure 4.6 shows that BET,
GAB, and Iglesias-Chirife were the best fitting models for desorption isotherms
at 60˚C. The GAB model best described all the adsorption and desorption
46
isotherms. The results for statistical errors used to evaluate the best fitting
models together with model constants are given in Table 4.2.
It was observed from Table 4.2, that all models fitted had a small value for
RMSE as computed using equation 4.1, which indicated that all models could
be used to describe both adsorption and desorption moisture isotherms for
matooke. However, the computed values of E% using equation 4.2 indicated
that the BET and Iglesias-Chirife model, the E-values were greater than 10% for
adsorption and desorption moisture isotherms at 30˚C and 50˚C respectively.
Chung-Pfost model, the E-value was greater than 10% for desorption moisture
isotherm at 50˚C. The Henderson model, the E-value was greater than 10%
for all the adsorption and desorption moisture isotherms for matooke. This
indicated that although the RMSE had small values for all models, the E-value
eliminated those models whose values were greater than 10% as not being
good for describing the moisture sorption isotherms for matooke. Therefore
only the GAB model best described all the adsorption and desorption moisture
isotherms for matooke.
53
4.3.3 Hysteresis for matooke
4.3.4 Hysteresis for matooke
Figure 4.7 shows the hysteresis behavior for matooke.
Figure 4.7 shows the hysteresis behavior for matooke.
0.3
Desorption 60˚C
Adsorption 60˚C
0.2
0.1
0.0
0
0.2
0.4
0.6
0.8
1
Water activity, aw
Figure 4.7 Hysteresis for matooke
Figure 4.7 Hysteresis for matooke
From Figure 4.7 it was observed that matooke exhibits hysteresis which was consistent with previous
work on bananas and starch containing foods (Johnson & Brennan, 2000, Al-Muhtaseb et al., 2004),
which indicated that the fresh matooke holds more moisture than dry ones over the entire range of water
activity (aw). The hysteresis phenomenon was exhibited by the moisture sorption isotherms at 60˚C.
However, it has been reported by previous researchers that the hysteresis can be used as a food quality
index, with increased hysteresis being indicative of reduced stability and reduced or absence of
47
From Figure 4.7 it was observed that matooke exhibits hysteresis which
was consistent with previous work on bananas and starch containing foods
(Johnson & Brennan, 2000, Al-Muhtaseb et al., 2004), which indicated
that the fresh matooke holds more moisture than dry ones over the entire
range of water activity (aw). The hysteresis phenomenon was exhibited by
the moisture sorption isotherms at 60˚C. However, it has been reported by
previous researchers that the hysteresis can be used as a food quality index,
with increased hysteresis being indicative of reduced stability and reduced or
absence of hysteresis being indicative of improved stability of stored product
(Caurie, 2007). Also it was reported that the hysteresis is dependent on the
sorption temperatures (Sun & Byrne, 1998; Aviara et al., 2006; Vega-Gálvez
et al., 2008). It was further reported that the loop becomes wider with increase
in temperature (Aviara et al., 2006; Vega-Gálvez et al., 2008; Raji & Ojedrian,
2011). It is therefore, recommended that further investigations on hysteresis
at different temperatures should be carried out in order to establish the
temperature range within which there is increased hysteresis (being indicative
of reduced stability) and reduced hysteresis (being indicative of improved
stability) of stored dried matooke.
4.3.4 Isosteric Heat of Sorption for matooke
The isosteric heat of sorption for adsorption isotherm is a measure for the
energy released during adsorption. While that for desorption isotherm is the
energy required for breaking the intermolecular forces between the molecules
of water vapor and the surface of adsorbent (Ciro et al., 2008). The GAB
model was used to predict the water activity (aw) at different equilibrium
moisture content, which was used in the analysis. The isosteric heat of sorption
for adsorption and desorption isotherms were calculated from the slope of
equation 2.5 and 2.6. Figure 4.8 Isosteric heats of sorption for adsorption and
desorption of matooke as a function of moisture content.
forces between the molecules of water vapor and the surface of adsorbent (Ciro et al., 2008). The GAB
model was used to predict the water activity (aw) at different equilibrium moisture content, which was used
in the analysis. The isosteric heat of sorption for adsorption and desorption isotherms were calculated
from the slope of equation 2.5 and 2.6. Figure 4.8 Isosteric heats of sorption for adsorption and
48
desorption of matooke as a function of moisture content.
20,000
Isosteric Heat of Sorption (kJ/kg)
Desorption Qst (kJ/kg)
Adsorption Qst (kJ/kg)
15,000
Latent heat of Vaporization (kJ/kg)
10,000
5,000
-
0.1
0.2
0.3
0.4
4.8 Isosteric
heatsof
of sorption
sorption for adsorption
and desorption
of matooke
as a function
of moisture
Figure 4.8Figure
Isosteric
heats
for adsorption
and
desorption
of matooke
content.
as a function of moisture content.
It was observed from Figure 4.8 that the isosteric heat of sorption decreases
It was observed from Figure 4.8 that the isosteric heat of sorption decreases with increasing moisture
with increasing
moisture content for both adsorption and desorption, this was
content for both adsorption and desorption, this was consistent with literature on bananas (Yan et al.,
consistent2008).
withTheliterature
onof bananas
(Yan etwere
al.,higher
2008).
The
heat of
isosteric heat
sorption for desorption
than that
for isosteric
adsorption, indicating
that
moredesorption
energy is required
for desorption
adsorption
process. The
decrease of isosteric
sorption for
were
higherprocess
than than
thatfor for
adsorption,
indicating
that
heat of sorption with increasing moisture content could be related to the decrease of active sites leading
more energy is required for desorption process than for adsorption process.
to reduced energy required to break the bond between water molecules and product surface, as the water
The decrease
of sorption
with
increasing
moisture
content
occupiesof
theisosteric
active sites atheat
high moisture
content. This
trend
has been reported
for bananas
(Johnson &
Brennan,
2000;
Pahlevanzadeh
&
Yazdani,
2005;
Falade
&
Awoyele,
2005;
Ciro
et
al.,
2008;
Yan et al.,
could be related to the decrease of active sites leading to reduced energy
2008; Hossain et al., 2001) and for pitahaya fruits (Yala et al., 2011).
required to break the bond between water molecules and product surface,
as the water occupies the active sites at high moisture content. This trend
has been reported for bananas (Johnson & Brennan, 2000; Pahlevanzadeh
& Yazdani, 2005; Falade & Awoyele, 2005; Ciro et al., 2008; Yan et al., 2008;
Hossain et al., 2001) and for pitahaya fruits (Yala et al., 2011).
As the moisture further increases, xe ≥ 0.3, the sorption heat tends to latent
heat of vaporization of pure water, which indicates that the moisture exists in
its free form (Oluwamukomi et al., 2008; McLaughlin & Magee, 1998). The
values of isosteric heat of sorption reported in this study revealed the existence
of large amounts of bound water. The total sorption heat, Qst, are higher than
the latent heat of vaporization of pure water, indicating that the energy of
binding between the water molecules and the sorption sites is higher than the
energy which holds the water molecule of pure water together in liquid phase
(Al-Muhtaseb et al., 2004).
higher than the energy which holds the water molecule of pure water together in liquid phas
Muhtaseb et al., 2004).
49
The relationship between the net isoteric heat sorption and equilibrium moisture content was determ
The relationship between the net isoteric heat sorption and equilibrium
using the SPSS 16 in order to determine the constants of equation 2.7. The exponential relationshi
moisture content was determined using the SPSS 16 in order to determine the
total desorption
and adsorption
isosteric heatrelationships
of sorption, equation
4.3 was used to calculate
constants
of equation
2.7. The exponential
for total desorption
desorption
and adsorption
isosteric
heat of sorption
for matooke.
and
adsorption
isosteric heat
of sorption,
equation
4.3 was used to calculate
total desorption and adsorption isosteric heat of sorption for matooke.
𝑄𝑠𝑡 = 20870 𝑒𝑥𝑝 �
−𝑥𝑒
0.036
�+𝜆 ,
2
R = 0.987
4.3
The exponential relationships for both adsorption and desorption isotherm of
The exponential
relationships
for both adsorption
and desorption
describe w
matooke,
describe
well the dependences
of isosteric
heat of isotherm
sorptionofonmatooke,
the
dependences
of isosteric
heat of
sorption
on the equilibrium
moisture
content.
This was consisten
equilibrium
moisture
content.
This
was consistent
with previous
work
on fruits
previous
work on(Ciro
fruits et
andal.,
vegetables
(Ciro et
et al.,
2011;2012;
Seid & Henel, 2012;
and
vegetables
2008; Ayala
al., 2008;
2011;Ayala
Seidet&al.,
Henel,
Gálvez et al.,et2008).
It was Itobserved
from Figure
that there
was there
inverse
exponential relatio
Vega-Gálvez
al., 2008).
was observed
from4.8
Figure
4.8 that
was
inverse
exponential
relationship
theand
totalequilibrium
isosteric heat
of sorption
between
the total isosteric
heat between
of sorption
moisture
contentand
of matooke. The
equilibrium
moisture
content
of
matooke.
The
total
isosteric
heat
of
sorption
isosteric heat of sorption (Qst) increased with decreased equilibrium moisture content. The total iso
) increased
with
The total
(Qheat
of sorption
fordecreased
adsorption equilibrium
isotherm formoisture
matookecontent.
ranged from
4,586isosteric
– 2,386kJ/kg for equil
st
heat
of sorption
for dry
matooke
fromWhile
4,586the– total isosteric h
moisture
content for
fromadsorption
0.01 – 0.3 isotherm
(kg water/kg
matter) ranged
respectively.
2,386kJ/kg
equilibrium
moisture
contentranged
from 0.01
0.3 (kg
water/kg
sorption forfor
desorption
isotherm
for matooke
from –18,194
– 2,391
kJ/kgdry
for equilibrium mo
matter) respectively. While the total isosteric heat of sorption for desorption
content from 0.01 – 0.3 (kg water/kg dry matter) respectively. These results are comparable with
isotherm for matooke ranged from 18,194 – 2,391 kJ/kg for equilibrium
range of what was reported for bananas and plantain, the difference is attributed to the mo
moisture content from 0.01 – 0.3 (kg water/kg dry matter) respectively. These
dependence of moisture sorption isotherms. The moisture content used in obtaining the isosteric h
results are comparable within the range of what was reported for bananas and
sorption should be put in consideration when comparing with values in literature, Ciro et al., (2
plantain, the difference is attributed to the moisture dependence of moisture
reported (qst ≈1670 - 215 kJ/kg for 0.05 – 0.26 kg water/kg dry matter), Johnson & Brennan, (
sorption isotherms.
The moisture content used in obtaining the isosteric heat
reported (Q ≈ 4117 – 2665 kJ/kg (73 – 47 (kJ/g mol) for 0.02 - 0.18 kg water/kg dry matter).
of sorption stshould be put in consideration when comparing with values in
literature, Ciro et al., (2008), reported (qst ≈1670 - 215 kJ/kg for 0.05 – 0.26 kg
water/kg dry matter), Johnson & Brennan, (2000) reported (Qst ≈ 4117 – 2665
kJ/kg (73 – 47 (kJ/g mol) for 0.02 - 0.18 kg water/kg dry matter).
It has been reported by previous researchers that the total isosteric heat of
sorption for desorption isotherm is an estimate of the energy requirement
for dehydration (Kiranoudis et al., 1993; Pahlevanzadeh & Yazdani, 2004;
Kaymak-Ertekni & Gedik, 2004; Yazdani et al., 2006; Yang et al., 2012).
The energy required to dry matooke was obtained by integrating equation 4.3
which gives the total isosteric heats of sorption for desorption of matooke from
2012).
Establishment
of moisture
sorptionequation
isotherms
matooke
energy required to dry matooke
was obtained
by integrating
4.3for
which
gives the total
50
The
isosteric heats of sorption for desorption of matooke from 0.11 to 3.5 (kg water/kg dry-matter) using the
0.11
3.5 of
(kgvaporization
water/kg dry-matter)
latentof heat
of vaporization
pure55˚C
latenttoheat
of pure water using
at the the
average
the temperatures
in theofstudy
water
at the average of the temperatures in the study 55˚C (λ = 2,386.13kJ/
2,386.13kJ/kg).
kg).
�
3.�
�20870 𝑒𝑥𝑝 �
0.11
(λ =
−𝑥𝑒
� + 2386.13� 𝑑𝑥 = 8,124 𝑘𝐽⁄𝑘𝑔 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑟𝑒𝑚𝑜𝑣𝑒𝑑
0.036
Therefore, the energy required to dry matooke from 3.5 to 0.11 (kg water/
Therefore,
the energy
required to80
dry –matooke
0.11 (kg water/kg
dry-matter)
(equivalent 80 –
kg
dry-matter)
(equivalent
10 % from
wb)3.5
is to8,124kJ/kg
of water
removed.
10
%
wb)
is
8,124kJ/kg
of
water
removed.
Therefore,
the
total
energy
required
to
dry
kg of fresh
Therefore, the total energy required to dry 1 kg of fresh matooke from 801 –
matooke
from
80
–
10%
(wb)
is
5,768
kJ.
10% (wb) is 5,768 kJ.
4.4
Conclusion and Recommendations
4.4 Conclusion and Recommendations
This study has bridged the gap of lack of information on the sorption isotherm
study has bridged
the gap
of lack
of information
on the sorption
isotherminformation
of matooke. The
ofThis
matooke.
The study
has
therefore,
provided
very useful
on study has
therefore,
provided
very
useful
information
on
moisture
sorption
isotherm
of
matooke,
which
moisture sorption isotherm of matooke, which can be used for prediction can
of be used
for
prediction
of
quality,
design,
modeling
and
optimization
of
many
processes
such
as
drying,
quality, design, modeling and optimization of many processes such as drying, aeration
and storage. In addition, it is also important for predicting quality changes during packaging and storage
aeration
and storage. In addition, it is also important for predicting quality
of dried matooke.
changes during packaging and storage of dried matooke.
This study has specifically provided the following information on moisture
This studyisotherm
has specifically
provided the
on the
moisture
sorptionhave
isotherm
for matooke
sorption
for matooke
forfollowing
the firstinformation
time, and
following
been
for
the
first
time,
and
the
following
have
been
revealed:
revealed:
•
Matooke
Matookeisotherms
isotherms exhibited
type
II curves
behavior
which iswhich
characteristic
of foodstuffs.
exhibited
type
II curves
behavior
is characteristic
of foodstuffs.
The equilibrium moisture content decreases with increase in temperature at a given water activity
(a ), indicating that it becomes less hygroscopic at high temperatures giving a clear stability
•
The wequilibrium moisture content decreases with increase in temperature
at a given water activity (aw), indicating that it becomes less hygroscopic at
high temperatures giving a clear stability domain of matooke after drying
leading to extensive shelf life. This compliments to Muranga, (1998), who
attributed the extensive shelf life to low sugar and fat.
•
The GAB model best described all the adsorption and desorption moisture
isotherms for matooke. It is recommended to be used by the processors
to determine the moisture content during storage.
•
For microbiologically and shelf stability of dried matooke, it is recommended
to dry it to moisture content below or equal to 0.11 (kg water/kg dry matter)
equivalent to 10% (wb).
51
•
The storage condition should be below or equal to 30˚C, but due to
fluctuations in temperatures and humidity in Uganda because of wet and
dry seasons, it is recommended to seal the dried matooke to minimize
moisture intake.
•
The hysteresis phenomenon was exhibited by the moisture sorption
isotherms and it could be used as a food quality index. It is recommended
that further investigations on hysteresis at different temperatures be
carried out to establish the ranges of increased hysteresis being indicative
of reduced stability and reduced hysteresis being indicative of improved
stability of stored dry matooke.
•
The isosteric heat of sorption for both isotherms increased with decreased
moisture content. The total isosteric heat of sorption for adsorption
isotherm ranged from 4,586 – 2,386 kJ/kg for equilibrium moisture
content from 0.3 – 0.01 (kg water/kg dry-matter) respectively. While the
total isosteric heat of sorption for desorption isotherm ranged from 18,194
– 2,391 kJ/kg for equilibrium moisture content from 0.3 – 0.01 (kg water/
kg dry-matter) respectively.
•
The total isosteric heat of sorption for desorption isotherm is an estimate
of the energy requirement for breaking the intermolecular forces between
solid and moisture during the drying process to drying. Thus energy
required to dry matooke from 3.5 to 0.11 (kg water/kg dry-matter)
(equivalent 80 – 10 % (wb) is 8,124kJ/kg of water removed. Therefore,
the total energy required to dry 1 kg of matooke from 80 - 10% (wb) is
5,768kJ.
Drying Characteristics of Matooke
52
Chapter Five
Objective: Drying Characteristics of Matooke
5.1
Background
Hot-air drying is considered as one of the simplest, most appropriate and most
economical technology for processing of fruits and vegetables in developing
countries (Johnson et al., 1998; Baini and Langrish, 2007). Understanding
of phenomena involved in the drying process is a primary requirement when
using hot-air drying in order to predict the drying times, and the influence of
the processing parameters such as: air temperature, dew point temperature,
thickness (size) of the slices and air velocity on the drying characteristics
(Johnson et al., 1998). These processing parameters have been reported
to have a significant effect on the drying characteristics (Krokida et al.,
2003). Application of effective diffusivity method has been reported to be a
convenient and practical method for describing moisture changes during the
drying process (Kayacier et al., 2004; Baini and Langrish, 2007). In addition,
diffusion properties are important for understanding of the drying behavior of
food during drying and also for the design and optimization of dryers for specific
crops (Rasouli et al., 2011). In order to control and optimization of the drying
process for matooke it was important to understand the effect of processing
parameters on the drying characteristics. The objective of the investigations
described in this chapter was to establish the effect of processing parameters
on the drying characteristics of matooke and the specific objectives were to
determine the effect of:
1. Process parameters on drying characteristics.
2. Drying rate constant, effective moisture diffusivity and activation
energy.
3. Processing parameters on total color change (ΔE*).
4. Drying air temperature on surface product temperature.
5. Product temperature on the drying time.
5.2
53
Materials and Methods
The research dryer was developed in corporation with M/s Innotech
Ingeniersgesellschaft mbH, Altdorf (Böblingen), Germany and University
of Applied Science and Technology, Konstanz, Germany. It comprises of:
drying tower for both overflow and through flow unit, water conditioning
unit, humidifying unit, fan, pyrometer, digital camera and data-logger. The
processing parameters (independent variables): air velocity, drying air
temperature, dew-point temperature were set and continuously monitored
and data-logged during the drying process. Similarly, the dependent variable:
mass of samples were measured continuously using a precision balance and
product temperature were measured by a precision infrared pyrometer. The
dependent variables were monitored and automatically recorded by datalogged at a 10 sec interval during the drying process. The photographs of
the samples were taken by the digital camera at 30 min time intervals. Figure
5.1, shows both the pictorial view of major components of the research dryer
and Figure 5.2, shows the system layout of the research dryer. All matooke cv.
Mbwazirume (musa sp. AAA-EABH) samples used for the experiments were
at harvest maturity age of 17 weeks based from the findings of Chapter 3 of
this study.
Over flow duct
Humidifying
Unit
Manometer
Monitoring
scale
Figure 5.1 Pictorial view of the research dryer unit
Fan
54
Preparation of Samples
The fresh samples were harvested, from the marked banana as described in
Chapter 3 of this study, washed, peeled and sliced to specified uniform thickness
using slicing machine. The thicknesses of sliced samples were continuously
monitored by physical measuring using a Vanier Clippers to confirm the actual
thickness (Chapter 3). After slicing the samples were pretreated with 1%
sodium metabisulphite solution according to patent No. AP/P/2005/003308.
The dried sample were sealed in airtight bags and kept in the fridge at 4˚C
for subsequent tests in the laboratory. Samples for determining the pasting
properties and starch content were milled (Chapter 3). All experiments were
carried out at the Presidential Initiative on Banana Industrial Development
(PIBID) - Technology Business Incubator at Bushenyi, Uganda. Table 5.1
shows the measuring equipment used in these investigations.
Table 5.1 Measuring equipment
Type of
measurement
Equipment
Model
Manufacturer
Recording
experimental
data
Data-logger
Agilent
34970A,
Agilent
Technologies,
Inc.
Sample weight
Precision
balance
Sartorius
LA16001S
Sartorius AG,
Goettingen,
Germany
±0.01g
Capacity ≤12
kg
Product
temperature
Precision
infrared
pyrometer
Heitronics
Heitronics
Infrarot
Messtechnik
GmbH,
Wiesbden,
Germany
±0.05˚C
+0.7%
of temp
difference
between room
& object
KT15 II
Accuracy
Air temperature
Pt 100
Thermocouple
±0.02˚C
Dew pint
temperature
Pt 100
Thermocouple
±0.02˚C
The independent variables (processing parameters) were:
•
Air temperature (50, 60 and 70˚C)
•
Dew point temperature (15, 25 and 35˚C).
•
Air velocity 3, 4.5 and 6 m/s for the overflow mode
•
Slice thickness (3, 5 and 7 mm)
55
The dependent variables were:
•
Drying time (min).
•
Color of the sample.
•
Sample weight
The experiments were carried out in triplicates and the results were analyzed
using SPSS 16 for Windows to fit the experiment data.
Figure 5.2: Complete system layout of the units for the research dryer.
Key: W: water conditioning unit, V: air fan, H: humidifier, C:
computer, D: data-logger, T: drying chamber, X: through
flow, U: overflow, H1 = heating unit (heaters), a: air
temperature, b, g: air velocity (hot air manometer), c: a i r
temperature, d: pyrometer, e: camera, f: balance, h: air
temperature, i : heaters, j:fan, k: water condition unit
Figure 5.3 shows the representation of the whole process of drying on
the psychrometric chart. The air is sucked by the fan at point A at ambient
conditions, it is cooled in the humidifying unit to point B, humidified to the
set dew point temperature point C, and it is then heated by heaters to a set
temperature D.
56
Figure 5.3: The drying process as represented on the psychrometric chart.
5.3
Results and Discussion
5.3.1 Effect of thickness on drying characteristics of matooke
Figure 5.4 shows the effect of thickness of the slices on the drying characteristics
of matooke. The investigations were carried out for slice thicknesses of 3, 5,
and 7 mm at air temperature of 60˚C, dew-point temperature of 15˚C and air
velocity of 4.5 m/s.
Figure 5.4
5
Mosture Content
(kg water/kg dry metter)
Thickness
4
3 mm
3
5 mm
7 mm
2
1
0
-
100
200
300
400
Time (min)
Figure 5.4 Effect of thickness on the drying characteristics of matooke (air
temperature 60˚C, dew-point temperature 15˚C and air velocity 4.5 m/s)
Figure 5.5
1.2
Figure 5.4
5
Thickness
Drying Characteristics
of Matooke
4
57
Mosture Content
(kg water/kg dry metter)
3 mm
5 mm
It was observed from
Figure 5.4 that thickness of the samples
has a significant
3
effect on the drying time, in that the drying time increased
with increase in
7 mm
2
thickness. This was
in line with previous researchers who reported that as
thickness increases, the diffusion path, becomes longer with increase in
1
thickness and vice
versa (Krokida et al., 2000; Nguyen and Price, 2007;
Islam et al., 2012). The drying time required to reach the recommended
0
microbiologically shelf-stable
4)300of 0.10 (kg
100product (Chapter
200
400 water/kg dry
Time
(min)
matter) were 115, 250 and 370 min for 3, 5 and 7 mm respectively.
Figure 5.5 shows the plot of moisture ratio of different thickness versus drying
time at constant air temperature of 60˚C, dew point temperature of 15˚C and
air velocity of 4.5m/s.
Figure 5.5
1.2
Thickness
Moisture Ratio (x/xo)
1
3 mm
5 mm
0.8
7 mm
0.6
0.4
0.2
0
0
100
200
300
400
Time (min)
Figure 5.5 Plot of moisture ratio of different thickness versus the drying time
(air temperature 60oC, dew point temperature 15oC, air velocity 4.5 m/s).
It was observed from Figure 5.5 that there was also a direct relationship
between thickness and moisture ratio, in that the moisture ratio increased with
increase in thickness at any given time during the drying process.
Figure 5.6 shows the effect of drying rate constant at different thickness on the
moisture content of matooke at constant air temperature of 60˚C, dew point
temperature of 15˚C, air velocity of 4.5 m/s.
58
Figure 5.6
0.1
Thickness
Drying Rate
(kg water/kg dry matter)/min
3 mm
0.08
Warm-up
5 mm
7 mm
Falling rate perids
0.06
2nd
1st
0.04
0.02
0
0
1
2
3
4
5
Moisture Content (kg water/kg dry matter)
Figure 5.6 Effect of drying rate constant of different thickness on the moisture
content of matooke (air temperature 60oC, dew point temperature 15oC, air
Figure
5.7
velocity 4.5
m/s)
Time (min)
Ln(Moisture Ratio)
0 it was observed that the drying of matooke takes place in three
From Figure 5.6
0
100
200
400
steps: the warm-up,
the two
falling rate
periods:300
the first falling
rate and second
falling rate. However,
the second falling rate was more pronounced on the 3
-1
mm curve. This was in agreement with the previous research on bananas and
plantains (Prachayawarakon
et al., 2008). Since drying dominantly took place
-2
during the first and second falling rate periods, it implied that diffusion was the
Thicknesscontrolling the moisture migration from the samples.
dominant mechanism
-3
The diffusion model could
fit the moisture content better than the empirical
3 mm
model for matooke
(Baini
and
Langrish, 2007).
5
mm
-4
7 mm
Figure 5.7 shows the plot of natural logarithm of moisture ratio at different
-5
thickness versus the drying time, at air temperature of 60˚C, dew point
temperature of 15˚C and air velocity of 4.5 m/s. The effective diffusivity and
drying rate constant were calculated from the slope of the plot of the natural
logarithm of moisture ratio versus time employing equations 2.12 and 2.15
respectively.
59
Figure 5.7
Time (min)
0
0
100
200
300
400
Ln(Moisture Ratio)
-1
-2
-3
Thickness
3 mm
-4
5 mm
7 mm
-5
Figure 5.7 Plot of natural logarithm of moisture ratio at different thickness
versus the drying time of matooke (air temperature 60˚C, dew point temperature
15˚C and air velocity 4.5 m/s).
Table 5.2 shows the effective diffusivity and drying rate constant of different
slice thickness (3, 5 and 7 mm) at air temperature 60˚C, dew point temperature
15˚C, and air velocity 4.5 m/s. The fitted plots are shown in Figures 5.1A and
5.2A (Appendix).
Table 5.2: Effective diffusivity and drying rate constant for matooke of
different thickness (air temperature 60˚C, dew point temperature 15˚C,
and air velocity4.5 m/s).
Thickness
(mm)
Drying Time
(min)
Drying Rate
Constant k
(/h)
Effective Diffusivity Deff (m2/s)
R2
3
115
2.10
5.32E-10
0.984
5
250
0.96
6.75E-10
0.987
7
370
0.60
8.27E-10
0.990
It was observed from Table 5.2 that there was an inverse relationship between
slice thickness and drying rate constant, in that the drying rate constant
decreased with increase in slice thickness. This was in line with previous
researchers on bananas and plantains (Johnson et al., 1998; Maskan, 2000;
Nguyen and Price, 2007; Islam et al., 2012). This trend is attributed to the
dependence of water removal from high moisture fruit on the migration of
60
water from internal sites of fruit cells towards the surface. Therefore, the
smaller the thickness, the shorter the pathway and the higher the drying rate
constant, thus taking a shorter drying time to reach the desired moisture
content and vice versa (Nguyen and Price, 2007). It was further observed
from Table 5.2, that there was a direct relationship between the thickness
and effective moisture diffusivity, implying that the effective moisture diffusivity
increased with increase in thickness. This was also in line with previous works
on bananas and plantains (Johnson et al., 1998; Nguyen and Price, 2007;
Rasouli et al., 2011; Islam et al., 2012). The increase in effective moisture
diffusivity with increased slice thickness is attributed to the pathway in that the
longer the pathway (thickness) the higher the effective moisture diffusivity and
vice versa (Nguyen and Price, 2007; Rasouli et al., 2011; Islam et al., 2012).
Even if the effective moisture diffusivity increases with thickness, but because
of the longer pathway for the moisture to move from the cells to the surface, it
takes longer time to reach the desired moisture content.
From practical point view, considering that 12 hours are available for working in
a day, the number of batches and amount of product which can be processed
in a day are given in Tables 5.3 for single layer drying and 5.4 for multi layer.
Table 5.3 Single layer drying
Thickness
(mm)
Time (h)
Capacity
(kg/m²)
Batches in
12h
Capacity/
day(kg/m²)
3
1.92
2.33
6
13.98
5
4.17
4.11
3
12.33
7
6.17
5.56
2
11.12
Table 5.4 Multi-layer drying (Thickness - 3mm)
Capacity/ batch
(kg/m²)
Time (h)
Batches in 12h
Capacity/ day
(kg/m²)
3.53
3.48
3
10.59
6.78
5.07
2
13.56
10.52
6.26
1
10.52
It was observed from Table 5.3 and 5.4 that for single layer drying, thickness
of 3 mm was the best. In actual practical terms of commercial processing,
61
thickness is considered in terms of equivalent loading capacities (kg/m²).
Considering the loading capacities for single layer drying and multi-layer
drying, it is more practical to run 2 batches than 6 batches per day. For
commercial processing of matooke, it is recommended to employ multi-layer
drying of loading capacity equal or less than 7 kg/m².
5.3.2 Effect of drying air temperature on drying characteristics
of matooke
Figure 5.8 shows the effect of drying air temperature on the drying
characteristics. The investigations were carried out for three different drying
air temperatures: 50, 60 and 70˚C at constant slice thicknesses of 3 mm, dewpoint temperature of 15˚C and air velocity of 4.5 m/s.
Figure 5.8
5
Drying air temperature
Moisture Content
4
50˚C
60˚C
3
70˚C
2
1
0
0
50
100
150
200
250
Time (min)
Figure 5.8 Effect of drying air temperature on the drying characteristics of
matooke (at dew point temperature 15oC, air velocity 4.5 m/s and thickness
3mm)
Figure 5.9
It was observed
from Figure 5.8 that there was an inverse relationship
1.2
between drying air temperature and drying time, in that the drying time
decreased with increase in drying air temperature. This was in agreement with
50˚C
previous research on bananas and plantain (Nguyen
and Price, 2007, Islam
0.8
60˚C
et al., 2012). This implied that the moisture removal increased with increase
in drying air temperature. Since drying is a simultaneously
heat and mass
70˚C
transfer process, the higher the drying air temperature, the higher the capacity
0.4
0
-
50
100
150
200
250
Figure 5.8
5
62
4
Moisture Content
50˚C
60˚C
to transfer heat to the drying product leading to increase
in moisture removal
3
70˚C
from the product. In addition, the higher the drying air temperature, the lower
the relative humidity in the stream of air. This implies increased capacity of
2
the air stream to pickup moisture from the product surface thus enhancing
moisture removal.1 All these factors coupled together resulted in reduced time
required to reach the desired moisture content at a higher air temperature and
vice versa as shown
in Figure 5.8. The drying time required to reach moisture
0
0
100
200
content of 0.1 (kg water/kg50dry matter)
was150
200, 115
and 90250min. for 50, 60
Time (min)
and 70˚C respectively.
Figure 5.9 shows the effect of moisture ratio at different drying air temperature
on drying time. The investigations were carried out at constant dew point
temperature of 15˚C, air velocity of 4.5 m/s and thickness of 3 mm.
Figure 5.9
1.2
50˚C
0.8
60˚C
70˚C
0.4
0
-
50
100
150
Time (min)
200
250
Figure 5.9 Plot of moisture ratio at different drying air temperature versus
drying time of matooke (dew point temperature 15oC, air velocity 4.5 m/s and
thickness 3mm)
It was observed from Figure 5.9, that there was an inverse relationship between
air temperature and moisture ratio in that the moisture ratio decreased with
increase in drying air temperature at a given drying time. This trend was
observed by previous researchers on bananas and plantains (Islam et al.,
2012).
Figure 5.10 shows the effect of drying rate constant at different drying air
temperature on moisture content, at constant dew point temperature of 15˚C,
air velocity of 4.5 m/s and thickness of 3 mm.
of Matooke
Figure 5.10
63
0.12
Drying Rate
50˚C
60˚C
0.08
70˚C
0.04
0
0
2
4
6
Figure 5.10 Effect of drying rate constant at different drying air temperature
on moisture
content of matooke (dew point temperature 15oC, air velocity4.5
Figure 5.11
m/s and thickness 3mm)
0
Time (min)
Ln(Moisture Ratio)
It was also observed
in Figure 5.10, 100.00
that the drying of matooke
200.00 took place in
Drying
air
temperature
three steps: the warm-up and two falling rate periods: the first falling rate and
-1
second falling rate. It was further observed from50˚C
Figure 5.10 that the drying
60˚C
rate constant for
matooke
was
directly
related
to
the
air temperature, in that
-2
70˚C
drying rate constant increased with increase in drying air temperature. This
was in line with
-3 the previous work done on plantains and bananas (Krokida
et al., 2000; Nguyen and Price, 2007; Rasouli et al., 2011; Islam et al., 2012).
This implied that
-4 the rate of moisture removal increased with temperature at
any given moisture until the product was relatively dry.
-5
drying air temperatures versus the drying time of matooke at constant dew
point temperature of 15˚C, air velocity of 4.5 m/s and thickness of 3 mm. Table
5.5 shows there was a direct relationship for both drying rate constant and
effective diffusivity with drying air temperature. This was in line with previous
researchers on bananas (Nguyen and Price, 2007). The fitted plots are shown
in Figures 5.3A and 5.4A in appendix A.
0
0
2
4
6
64
Figure 5.11
Time (min)
0
-
100.00
-1
Ln(Moisture Ratio)
200.00
50˚C
60˚C
-2
70˚C
-3
-4
-5
Figure 5.11 Plot of natural logarithm of moisture ratio at different drying air
temperatures versus the drying time of matooke (dew point temperature 15˚C,
air velocity 4.5 m/s and thickness 3 mm)
Table 5.5 Effective diffusivity and drying rate constant for matooke
at different drying air temperature (dew point temperature 15˚C, air
velocity 4.5 m/s and thickness 3mm)
Air
Temperature
(oC)
Drying Rate
Constant k (/h)
Effective
Diffusivity Deff
(m2/s)
R2
50
1.34
3.40E-10
0.988
60
2.10
5.32E-10
0.984
70
2.49
6.31E-10
0.984
Table 5.6 shows the comparisons of the results of effective diffusivity from
the study with what has been reported in literature on bananas and plantains.
However, though there some differences, the effective diffusivity of matooke
falls within the range reported for bananas and plantains ranging in the order
of 10-10 m2/s.
65
Table 5.6: Comparisons of current results with literature
Effective Moisture Diffusivity (m²/s)
Author
1.25E-10 to 2.19E-10,
Islam et al., (2012)
3.16E-10 to 18.01E-10
Johnson et al., (1998)
3.40E-10 - 6.31E-10
Results of the study (Kawongolo, 2013)
4.0E-10 to 7.0E-10
Phoungchandang and Woods, (2000)
4.6E-10 to 13.0E-10
Dandamrongrak et al., (2002)
6.61E-11 to 2.41E-10
Karim and Harlader, (2004)
Calculation of the Activation Energy
Figure 5.12 shows the plot of natural logarithm of effective diffusivity versus
the reciprocal of drying air temperature, at constant dew point temperature
of 15˚C, air velocity of 4.5 m/s and thickness of 3 mm. The dependence of
effective diffusivity (Deff) was described by Arrhenius as given in equation 2.16.
The activation energy was calculated from the slope equation 2.18 (Doymaz,
2007). The activation energy (Ea) for matooke in 28.5 kJ/mol (equivalent
to1,605 kJ/kg). The activation energy of matooke lies within the general range
of 12.7 – 110 kJ/mol for food materials (Zogzas et al., 1996, Babalis and
Drying characteristics
74
Belessiotis,
2004).for matooke
1/(abs. Tempeature) (1/K)
0.0029
0.003
0.0031
0.0032
Ln(Deff)
-21.2
y = -3433.2x - 11.135
R² = 0.9456
-21.6
-22
Figure5.12
5.12 Plot
of of
natural
logarithm
of effective diffusivity
versus the
reciprocal of
drying airthe
temperature
Figure
Plot
natural
logarithm
of effective
diffusivity
versus
reciprocal
˚
(dew point temperature 15 C, air velocity 4.5 m/s and thickness 3 mm) ˚
of drying
air temperature (dew point temperature 15 C, air velocity 4.5 m/s and
thickness 3 mm)
The activation energy has been reported to be the energy required to break the intermolecular bonding
and bring the molecule to the surface where it acquires the latent heat of evaporation and it evaporates
from the surface (Demirel and Turhan, 2003; Baini and Langrish, 2007). This indicates that the activation
energy (Ea) is analogous to the net isoseteric heat of sorption for desorption isotherms (qst). The
activation energy (Ea) for matooke (1,605kJ/kg) falls within the range of net isoseteric heat of sorption for
66
The activation energy has been reported to be the energy required to break
the intermolecular bonding and bring the molecule to the surface where it
acquires the latent heat of evaporation and it evaporates from the surface
(Demirel and Turhan, 2003; Baini and Langrish, 2007). This indicates that the
activation energy (Ea) is analogous to the net isoseteric heat of sorption for
desorption isotherms (qst). The activation energy (Ea) for matooke (1,605kJ/
kg) falls within the range of net isoseteric heat of sorption for desorption (qst)
for moisture content ranging from 0.05 to 0.1 (kg water/kg dry matter) which is
(5,204 - 1,298 kJ/kg) respectively. However, the net isoseteric heat of sorption
for desorption isotherms (qst) at 0.1 (kg water/kg dry matter), (the moisture
considered in the drying experiments), (1,298 kJ/kg) is smaller than the
activation energy Ea (1,605 kJ/kg). This was in line with what was observed
by Demirel and Turhan, (2003), who reported that the comparison between Ea
and qst suggests that moisture molecules travels effectively in liquid form in the
slices during the drying process.
5.3.3 Effect of dew point temperature on drying characteristics
of matooke
Figure 5.13 shows the effect of dew temperature on the drying characteristics.
The investigation was carried out at three different dew point temperatures:
15, 25 and 35˚C, at constant slice thicknesses of 3 mm, air temperature of
60˚C and Figure
air velocity
of 4.5 m/s.
5. 13
5
Dew point temperature
Moisture Content
4
15˚C
25˚C
3
35˚C
2
1
0
-
50
100
Time (min)
150
200
Figure 5.13 Effect of dew point temperature on the drying characteristics of
matooke (air temperature 60˚C, air velocity 4.5 m/s and thickness 3mm).
Figure 5.14
5
4
67
Moisture Content
Figure 5. 13from Figure 5.13 that the dew point temperature had low
It was observed
effect on the time5 of drying; however, there was direct relationship between
point temperature
dew point temperature and drying timeDew
in that
the drying time increases with
4
increase in dew point temperature. The direct relationship
between dew point
15˚C
temperature and drying time could be attributed
to high relative humidity
25˚C
3
at higher dew point temperature in the stream
35˚C of air which decreased the
capacity of the air stream to pickup moisture from the product surface, hence
2
reducing moisture removal. As a result the time required to reach the desired
moisture content at higher dew point temperature increased and vice versa
1
as shown in Figure 5.13. The drying time required to reach moisture content
of 0.1 (kg water/kg dry matter) were approximately 115, 135 and 145 min. for
0
50
100
150
200
15, 25 and 35˚C respectively.
Time (min)
Figure 5.14 shows the plot of moisture ratio of different dew point temperatures
versus drying time of matooke at constant air temperature of 60˚C, air velocity
of 4.5 m/s and thickness of 3 mm.
Figure 5.14
5
Moisture Content
4
15˚C
25˚C
3
35˚C
2
1
0
-
50
100
Time (min)
150
200
Figure 5.14 Plot of moisture ratio at different dew point temperature versus
drying time of matooke (air temperature 60˚C, air velocity 4.5 m/s and thickness
3mm).
It was observed from Figure 5.14 that there was a direct relationship between
dew point temperature and moisture ratio in that the moisture ratio increased
with increased dew point temperature at any given drying time during drying.
68
Figure 5.15 shows the effect of drying rate constant of different dew point
temperature temperatures on moisture content, at constant air temperature of
60˚C, air velocity of 4.5m/s and thickness of 3 mm.
Figure 5.15
0.08
Drying Rate
15˚C
0.06
25˚C
35˚C
0.04
0.02
0
0
1
2
3
4
5
Figure 5.15
Effect of drying rate constant at different dew point temperature on
Figure 5.16
the moisture content (air temperature 60˚C, air velocity 4.5 m/s and thickness
Time (min)
3 mm).
0
-
50
100
150
200
Ln(Moisture Ratio)
It was observed from Figure 5.15 that the drying rate constant for matooke
-1
was inversely related to the dew point temperature, in that the drying rate
constant decreased with increased dew point temperature.
-2
dew point temperatures
versus drying time, at constant air temperature of
-3
point temperature
60˚C, air velocity ofDew
4.5m/s
and thickness of 3 mm.
-4
-5
15˚C
25˚C
35˚C
0
0
1
2
3
4
5
69
Figure 5.16
Time (min)
0
-
50
100
150
200
Ln(Moisture Ratio)
-1
-2
-3
-4
-5
15˚C
25˚C
35˚C
Figure 5.16 Plot of natural logarithm of moisture ratio at different dew point
temperatures verses drying time (air temperature 60˚C, air velocity 4.5 m/s
and thickness 3mm).
Table 5.7 shows there was a direct relationship between drying rate constant
and drying air temperature. Also, a direct relationship was observed between
effective diffusivity and thickness. This was consistent with Rasouli et al.,
(2011); Nguyen and Price, (2007). The fitted plots are shown in Figures 5.5A
and 5.6A in appendix A.
Table 5.7 Effective diffusivity and drying rate constant for matooke at
different dew point temperatures (air temperature 60˚C, air velocity 4.5
m/s and thickness 3mm).
Dew Point
Temperature (oC)
Drying Rate
Constant,
k (/h)
Effective Diffusivity
Deff (m2/s)
R2
15
2.10
5.32E-10
0.984
25
1.76
4.45E-10
0.988
35
1,72
4.36E-10
0.972
5.3.4 Effect of air velocity on drying characteristics of matooke
Figure 5.17 shows the effect of air velocity on the drying characteristics. The
investigations were carried out at three different airflows: 3, 4.5 and 6 m/s
at constant: thicknesses of 3 mm, air temperature of 50˚C and dew point
temperature of 15˚C.
70
Figure 5.17
4
Air velocity
Moisture Content
(kg water/kg dry mmatter)
3 m/s
3
4.5 m/s
6m/s
2
1
0
-
100
200
300
Time (min)
400
500
Figure 5.17 Effect of air velocity on the drying time of matooke (air temperature
of 50oC, dew point temperature of 15oC and the thickness of 3 mm).
Figure 5.18
It was observed from Figure 5.17 that there was an inverse relationship
1.2
between air velocity
and drying time in that the drying time decreased with
velocity
increased airflow. The inverse relationship Air
between
air velocity and drying
time could be attributed
to
the
fact
that
drying
is
a
simultaneously
heat and
3 m/s
0.9
4.5 m/s
mass transfer process, the higher the air velocity of air stream the greater
6 m/s
the capacity of air stream to pickup moisture from the product surface hence
0.6
enhancing moisture removal. In addition, at high air velocity, the air stream
has a higher heat transfer coefficient. As a result, the time required to reach
0.3
the desired moisture
content at higher air velocity reduced and vice versa as
shown in Figure 5.17. The drying time required to reach moisture content of
0.1 (kg water/kg 0dry matter) was approximately 420, 200 and 92 min for 3, 4.5
0
100
200
300
400
500
and 6 m/s respectively.
Time (min)
Figure 5.18 shows the plot of moisture ratio at different air velocity versus
drying time at constant air temperature of 50˚C, dew point temperature of 15˚C
and the thickness of 3mm.
Time (min)
Figure 5.18 of Matooke
71
1.2
Air velocity
3 m/s
0.9
4.5 m/s
6 m/s
0.6
0.3
0
0
100
200
300
400
500
Time (min)
Figure 5.18 Plot of moisture ratio at different air velocity versus drying time
(thicknesses of 3mm, air temperature 50˚C and dew point temperature 15˚C)
It was observed from Figure 5.18 that there was an inverse relationship
between air velocity and moisture ratio in that the moisture ratio decreased with
increased air velocity at any given drying time during drying. This indicated
that as the air velocity increased, the moisture removal increased.
Figure 5.19 shows the effect of drying rate on the moisture ratio of matooke
at different air velocity at thicknesses of 3mm, air temperature 50˚C and dew
Figure 5.19 15˚C.
point temperature
Drying Rate
0.08
Air velocity
0.06
3 m/s
4.5 m/s
6 m/s
0.04
0.02
Constant drying
rate period
0
0
1
2
3
4
Figure 5.19 Effect of drying rate on the moisture ratio of matooke at different
air velocity at thicknesses of 3mm, air temperature 50˚C and dew point
temperature 15˚C
Figure 5.20
Time (min)
0
0
100
200
300
400
500
Figure 5.19
0.08
Drying Rate
72
Air velocity
0.06
It was observed from Figure3 m/s
5.19 it was observed that drying took place in
4.5 m/s
three steps: the warm-up and two falling rate periods. At lower air velocity
6 m/s
0.04
(3 m/s), it was observed
that there was a constant drying rate period up to
approximately a moisture content of 2 (kg water/kg dry matter), then the falling
rate period was observed.
It was further observed from Figure 5.19, that there
0.02
was a direct relationship between the dryingConstant
rate drying
constant and air velocity.
rate period
This implies that the rate of moisture removal increased with increase in air
0
velocity at any given moisture
content.
0
1
2
3
4
Figure 5.20 shows plot of natural logarithm of moisture ratio at different air
velocities versus drying time, at constant thicknesses of 3mm, air temperature
of 50˚C and dew point temperature of 15˚C.
Figure 5.20
Time (min)
0
0
100
200
300
400
500
Ln(Moisture Ratio)
-2
-4
Air velocity
-6
3 m/s
4.5 m/s
6m/s
-8
Figure 5.20 Plot of natural logarithm of moisture ratio at different air velocities
versus drying time (drying air temperature 50˚C, dew point temperature 15˚C
and the thickness 3mm).
The slope was calculated from the slope of the linear segment (Rasouli et al.,
2011), and the results obtained from the slope as given in Table 5.8, show that
the effective diffusivity of matooke increased with increase in air velocity as
shown in Figure 5.38, (Nguyen and Price, 2007). The fitted plots are shown in
Figures 5.7A and 5.8A in appendix A.
73
Table 5.8 Effective diffusivity and drying rate constant for matooke
at different air velocities (drying air temperature 50˚C, dew point
temperature of 15˚C and the thickness of 3mm).
Air velocity (m/s)
Drying Rate
Constant
k (/h)
Effective
Diffusivity Deff
(m2/s)
R2
3
0.594
1.50E-10
0.995
4.5
1.344
3.40E-10
0.988
6
1.614
4.09E-10
0.991
5.3.5 Effect of processing parameter on color of matooke
Table 5.7 shows the effect of processing parameters on the total color
difference (ΔE*). The total color difference (ΔE*) of dried matooke was
calculated by subtracting the color of the dried matooke from the color of the
fresh matooke (Baini and Langrish, 2007). The total color difference (ΔE*)
was calculated form equation 2.4 using the Visual-Basic-based program for
analysis of images (Sturm and Hofacker, 2009).
It was observed for the Table 5.7, that there was an inverse relationship
between the effects of drying parameters on the total color difference (ΔE*).
The results of total color difference (ΔE*), was lowest for effect of thickness
was 7 mm, but it took the longest time to dry. This was followed by the effect of
air velocity which was 6 m/s, and lastly for the effect of drying air temperature
was 70˚C.
74
Table 5.9 Effect of processing parameters on total color difference
(ΔE*)
Drying
parameter
Effect of air
temperature
on total color
change
Processing Parameters
Total
color
Dew point
Air
Thickness
Air
difference
(mm)
temperature temperature velocity(m/s)
(ΔE*)
(˚C)
(˚C)
50
15
4.5
3
7.751
60
15
4.5
3
7.103
70
15
4.5
3
3.948
Effect of
dew point
temperature
on total color
change
60
15
4.5
3
7.103
60
25
4.5
3
6.869
60
35
4.5
3
5.701
Effect of air
velocity on
the total color
change
Effect of
thickness on
the total color
change
50
50
50
15
15
15
3.0
4.5
6.0
3
3
3
8.879
7.751
2.897
60
60
60
15
15
15
4.5
4.5
4.5
3
5
7
7.103
4.968
1.968
5.3.6 Summary of effects of processing parameters
Table 5.10 show the summary of effects of processing parameters: drying
air temperature, dew point temperature, air velocity and thickness on drying
time, drying rate constant (k), effective diffusivity, Deff, total color difference,
ΔE*. The desirable responses are: shortest drying time, highest drying
rate constant, highest effective diffusivity and smallest color change. The
processing parameter which fulfills all the four mentioned criteria would be
considered as the best, thus the combination of those processing parameters
would give the optimum processing parameters of matooke. From Table 5.10,
it was observed that drying air temperature of 70˚C and air velocity of 6 m/s
fulfill all the four criteria, thickness of 3 mm and dew point temperature fulfill
three of the criteria. This implies that the of combination of: 70˚C, 6 m/s, 3mm
and 15˚C would be the optimum processing parameters for drying matooke.
Investigation
Effect of air
temperature (˚C)
Effect of dew
point temperature
(˚C)
Effect of air
velocity (m/s)
Effect of size
(slice thickness)
(mm)
Processing Parameters (Independent Variables)
Dependent Variables (Responses)
Air
Temperature
(˚C)
Dew Point
Temperature
(˚C)
Air velocity
(m/s)
Thickness
(mm)
Time
(min)
Drying rate
Constant,
k (/h)
Effective
Diffusivity,
Deff (m2/s)
Total Color
Difference,
ΔE*
50
15
4.5
3
200
1.34
3.40E-10
7.75
60
15
4.5
3
115
2.10
5.32E-10
7.10
70
15
4.5
3
90
2.49
6.31E-10
3.95
60
15
4.5
3
115
2.10
5.32E-10
7.10
60
25
4.5
3
135
1.76
4.45E-10
6.87
60
35
4.5
3
145
1.72
4.36E-10
5.70
50
15
3.0
3
420
0.59
1.50E-10
8.90
50
15
4.5
3
200
1.34
3.40E -10
7.75
50
15
6.0
3
92
1.61
4.09E-10
2.90
60
15
4.5
3
115
2.10
5.32E-10
7.10
60
15
4.5
5
250
0.96
6.75E-10
4.97
60
15
4.5
7
370
0.60
8.27E-10
1.97
Table 5.10 Summary of effects of process parameters
75
76
5.3.7 Effect of drying air temperature on surface product
temperature of matooke
Knowledge of the product temperature during drying is important to evaluate
the influence of the drying process on physicochemical and quality attributes.
This is because the reactions between food components are often accelerated
during drying leading to significant reduction in quality and nutritional value.
Drying characteristics for matooke
86
The reaction rates are strongly affected by the temperature and moisture
content5.3.7
of Effect
foodofduring
(Labuza,
1972).
5.21
shows the effect of
drying airdrying
temperature
on surface
productFigure
temperature
of matooke
air temperature
on surface product temperature at air velocity of 4.5 m/s dew
Knowledge of the product temperature during drying is important to evaluate the influence of the
point temperature
15˚C andand
thickness
of 3mm.
drying process on of
physicochemical
quality attributes.
This is because the reactions between
food components are often accelerated during drying leading to significant reduction in quality
nutritional
value. The reaction
rates are strongly
affected
by the temperature
and moisture
Figure and
5.21
Development
of infrared
surface
product
temperature
with drying
of food
during
(Labuza, 1972).
the effect of
temperature
on point
time atcontent
a given
set
airdrying
temperature
of Figure
70˚C5.21
at shows
air velocity
ofair4.5
m/s dew
surface product temperature at air velocity of 4.5 m/s dew point temperature of 15˚C and
temperature
of 15˚C and thickness of 3mm.
thickness of 3mm.
8
Temperature (˚C)
60
6
Air Temp
Product Temp
40
4
MC
20
2
0
80
0
-
50
Time (min)
100
150
Figure 5.21 Development of infrared surface product temperature with drying time at a given set
It was observed
5.21,
that
was
a direct
relationship
air temperaturefrom
of 70˚CFigure
at air velocity
of 4.5
m/sthere
dew point
temperature
of 15˚C
and thicknessbetween
of
surface3mm.
product temperature and air temperature at any given drying time,
in that the surface product temperature increased with increase in drying
air temperature.
At the onset of the drying process, the surface product
It was observed from Figure 5.21, that there was a direct relationship between surface product
temperature
of and
matooke
was atmuch
lower
the
air temperature
due to the
temperature
air temperature
any given
dryingthan
time, in
thatset
the surface
product temperature
increased
with increase
in drying airAs
temperature.
At the onset of the
drying
process, the
effect of
evaporative
cooling.
drying continued,
the
moisture
at surface
the surface
product temperature of matooke was much lower than the set air temperature due to the effect of
decreased
and the internal resistance to moisture transport increased, thus
evaporative cooling. As drying continued, the moisture at the surface decreased and the internal
the evaporation
zone transport
movedincreased,
from the
intozone
themoved
material.
resistance to moisture
thus surface
the evaporation
from theThis
surfaceimplied
the material.
This implied
that the heat necessary
had to be further
that theintoheat
necessary
for evaporation
had to for
beevaporation
further transferred
from the
transferred from the surface into the material to evaporate the moisture, in order to accomplish
77
surface into the material to evaporate the moisture, in order to accomplish
the phenomena of drying as being a simultaneously heat and mass transfer
process. This temperature gradient was required for heat and mass transfer
to take place. As the moisture content of product continued to decrease, the
product temperature also increased until the set air temperature was reached.
This implied that when the product temperature became relatively constant,
the samples were relatively dry suggesting that it could have attained the
equilibrium moisture content at the set air temperature. It was noted that, the
gelatinization temperature of matooke is 72˚C, glass transition temperature
is approximately less than67˚C, (Muranga 1998), however, from the results it
was observed that the surface product temperature at the end of drying, on
average was approximately less than 67˚C, implying that the glass transition
temperature was not exceeded.
5.3.8 Drying
Effect
of surface product temperature on the drying
characteristics for matooke
88
time of matooke
Effect
of surface
temperature
on the product
drying time temperature
of matooke
Figure 5.3.8
5.22
shows
theproduct
effect
of surface
on the drying
temperature.
Figure 5.22 shows the effect of surface product temperature on the drying temperature.
8
Air Temp
Temperature (˚C)
80
Product Temp
6
MC (db)
60
4
40
2
20
0
100
0
-
50
100
Time (min)
150
200
Figure 5.22 Development of air temperature with drying time for a given set infrared surface
product temperature of 50˚C at air velocity of 4.5 m/s dew point temperature of 15˚C and
Figure 5.22 Development of air temperature with drying time for a given set
thickness of 3 mm.
infrared surface product temperature of 50˚C at air velocity of 4.5 m/s dew
point temperature of 15˚C and thickness of 3 mm.
It was observed from Figure 5.22, that the air temperature shot up during the first region of drying,
It was when
observed
from
Figurereached
5.22, the
that
air temperature
shotstarted
up during
the product
temperature
set the
temperature,
the air temperature
decreasing
tending
to
the
set
product
temperature.
The
drying
air
temperature
becomes
the first region of drying, when the product temperature reached the set
relatively constant but slightly above the set product temperature, because the temperature
gradient required for continuation of the drying process.
The first region when the air temperature shot could be attributed to the high moisture content of
78
temperature, the air temperature started decreasing tending to the set product
temperature. The drying air temperature becomes relatively constant but
slightly above the set product temperature, because the temperature gradient
required for continuation of the drying process.
The first region when the air temperature shot could be attributed to the high
moisture content of the product that it required high energy to evaporate the
moisture from the product and also to overcome the effect of evaporative
cooling effect. The effect of setting the product temperature, reduced the
drying time by half. Comparing the results of drying when air temperature
was set at 50˚C, and when the product temperature was set at 50˚C, with
other processing parameters kept constant at air velocity 4.5 m/s, dew point
temperature 15˚C and thickness 3mm. The drying time for set air temperature
was approximately 200 min, while that for the set product temperature was
approximately 100 min. This indicated that by setting the product temperature,
it reduced the drying time by 50%; in other words, setting the product
temperature the drying process automatically turned itself to stepwise (stepdown) drying process. This was similar to step-wise drying reported by Chua
et al., 2001, which significantly reduced the drying time to reach the desired
moisture content with improved product color.
5.4
79
Conclusions and Recommendations.
Conclusions
This study has bridged the gap of lack of information on drying behavior
of matooke. Both thickness and dew point temperature showed a direct
relationship with drying time, while the air temperature and air velocity showed
an inverse relationship with drying time.
The effect of size (thickness) on drying time showed that the drying time
required reaching the recommended microbiologically shelf-stable product
from the moisture sorption isotherm studies of 0.1 (kg water/kg dry matter)
at air temperature of 60˚C, dew point temperature of 18˚C and air velocity
of 4.5 m/s were: 115, 250 and 375 min for 3, 5 and 7 mm respectively. It
was recommended that for single-layer drying, the slice thickness = 3 mm
was suitable for drying of matooke. However, from practical point of view, by
considering the difference in loading capacities between single layer drying
(2.33 kg/m2 taking 1.92h to dry) and multi-layer drying (6.78 kg/m² taking 5.07
h to dry), and the number of batches per day (12 working hours). It is more
practical to run 2 batches (multi-layer drying) than 6 batches (single-layer
drying) in a 12h.
The effect of drying air temperature on drying time at dew point temperature
of 18˚C, thickness of 3 mm and air velocity of 4.5 m/s were, the drying time
was: 200, 115 and 90 min for 50, 60 and 70oC respectively. The effect of dew
point temperature on drying time at air temperature of 60˚C, thickness of 3mm
and air velocity of 4.5 m/s, the drying time was: 115, 135 and 140 min for
15, 25 and 35˚C respectively. The effect of air velocity on drying time at air
temperature of 50˚C, thickness of 3mm and dew point temperature of 18˚C,
the drying time was: 420, 200 and 92 min for 3, 4.5 and 6 m/s respectively. In
contrast to Krokida et al., (2000), air velocity also has a significant effect on
drying characteristics of matooke.
The drying of matooke takes place in three steps: the warm-up and the two
falling rate periods; the first falling rate and second falling rate. The drying
rate constant was inversely related to thickness and dew point temperature,
while it was directly related to air velocity and air temperature. The drying rate
constant for all processing parameters ranged from 0.6 – 2.1/h. The effective
diffusivity for all processing parameters ranged from were in the range of
80
directly related to thickness, air velocity and air temperature while it was
inversely related to dew point temperature. The effective diffusivity ranged
from 1.43E-10 - 3.86E-10 m2/s.
The activation energy (Ea) has been reported to be the minimum energy that
must be supplied to break water-solid and/or water-water interactions and
to move the water molecule from one point to another in solid (Demirel and
Turhan, 2003). The results show that activation energy (Ea) for matooke was
16.3kJ/mol. (1,605kJ/kg). Comparing the activation energy (Ea) with the net
isosteric heat of desorption (qst), Ea (1,605kJ/kg) was higher than qst (1,298)
at 0.1 (kg water/kg dry matter) (the moisture content used in the drying
analyses). This suggests that moisture molecules travels effectively in liquid
form in matooke slice during the drying process.
The total color difference (ΔE*) between the fresh and dry samples, showed
an inverse relationship between the effects of processing parameters on the
total color difference (ΔE*), lowest was for the effect of thickness at 7 mm, but
it took the longest time to dry. This was followed by effect of air velocity at 6
m/s, then effect of drying air temperature at 70˚C.
In the drying system controlled by setting the drying air temperature,
product temperature increased with drying time, tending to the set drying air
temperature. However, the product temperature did not exactly equal to the
drying air temperature because of temperature gradient was required for heat
and mass transfer to take place. Whereas, in the drying system controlled
by setting product temperature, the drying air temperature shot up to high
as possible (Figure 5.23). The drying system controlled by setting product
temperature, the drying time was reduced by 50% of that obtained for setting
air drying temperature. This implied that the drying system controlled by
product temperature, automatically turned itself into stepwise (step-down)
drying system.
Recommendations
Considering all the effect of processing parameters on drying characteristics
of matooke showed that the of combination of drying air temperature 70˚C,
air velocity 6 m/s, thickness 3mm and dew point temperature 15˚C would be
the optimum processing parameters for drying matooke.
81
For commercial processing of matooke to employ multi-layer drying of loading
capacity equal or less than 7 kg/m², drying air temperature 70˚C, air velocity 6
m/s, thickness 3mm and dew point temperature 15˚C
More investigation is required to develop a drying system which is controlled
by product temperature instead of drying air temperature.
Further investigation on the effect of surface product temperature in association
with case hardening should be carried out.
Optimization of processing parameters for Matooke
82
Chapter Six
Objective: Optimization of processing parameters for
Matooke
6.1
Background
Drying is a major food processing operation and is classified as simultaneous
heat and mass transfer process (Madamba et al., 1994). Drying comprises a
series of processes which contribute to the final product and have significant
impact on the physicochemical and quality attributes (characteristics) of the
dried product (Krokida et al., 2000). During drying, in addition to moisture
removal, there are many physicochemical reactions which occur and can
lead to various types of quality deterioration (Krokida et al., 2000). The
physicochemical and quality attributes considered in this investigation which
could be affected during the drying process and are critical to the functionality
of flour were: starch content and pasting properties (final and peak viscosity).
The final viscosity and peak viscosity are correlated to final sample quality
(Kolawole et al., 2012; Iglett et al. 2012; Osungbaro et al., 2010; Ikegwu et
al., 2009; Niba et al. 2002). In the production of dried foodstuffs, minimizing
drying cost is the most critical factor. However, the conditions which produce
minimum costs are likely not to give the desired quality characteristics. Sturm
et al., (2012) reported that many industrial drying processes were found
experimentally, but their validity has not been evaluated since. Optimization of
the drying operation leads to an improvement in the quality of the dried product,
a reduction in the cost of processing as well as optimizing the throughput
(Mudamba et al., 1994). This is because the design and optimization of dryers
used for food crops is dependent on the thermal and physical properties of the
specific crops (Rasouli et al., 2011). Therefore, there was need for optimizing
the process parameters with reference to the physicochemical and quality
characteristics of matooke. It has been reported by previous researchers
that drying temperature, relative humidity and velocity of drying air have
significant effect on the drying kinetics and quality of the dried product (Baini
& Langrish, 2007; Sturm et al., 2012; Islam et al., 2012). However, their effect
83
on physicochemical and quality characteristics of dried matooke needed to be
established. Banana flour in general has been reported to have good prospects
and health benefits, but has not been fully utilized, could be due to high costs
associated with its conversion to flour and limited research highlighting the
potential and beneficial properties (Islam et al., 2012). Therefore, the objective
of this study was to optimize the processing parameters in relation to the
physicochemical properties and quality of the matooke and the following were
the specific objectives:
•
To establish the effect of the process parameters on individual responses
(drying time, final viscosity and starch content).
•
To optimize the process parameters with respect to drying time final
viscosity and starch content
•
To validate the optimum process parameters for drying.
6.2
Materials and Methods
The materials and methods employed for this study were as explained in
section 5.2, and the samples used for the determination of starch content and
pasting properties were kept at 4˚C. The dry matooke was milled to flour using
the starch mill as explained in chapter 3. The final viscosity, peak viscosity
and starch content were used for the optimization process. Response
Surface Methodology (RSM) techniques were employed for analysis using
Design-Expert version 8, software (Stat-Ease, Inc., Minneapolis, USA). All
experiments were carried out in triplicates.
 Independent Variables (Factors)
 Drying Temperature (50, 60 and 70˚C)
 Dew-point Temperature (15, 25 and 35˚C)
 Air velocity (3, 4.5, 6 m/s)
 Dependent Variables (Responses)
 Drying time to reach moisture content of 10% (wb)
 Final Viscosity (RVU)
 Peak Viscosity (RVU)
 Starch Content (%)
84
The data analysis employed the Response Surface Methodology (RSM)
which is one of the experimental designing methods which can surmount
the limitations of conventional methods collectively and has an advantage
that it reduces the number of experimental trials needed to evaluate multiple
parameters and their interactions (Noordin et al., 2004; Sturm et al., 2012;
Ramkrishna & Susmita, 2012).The Design-Expert version 8 software was used
and employed Response Surface Methodology (RSM), to generate the design
and optimize the drying conditions for matooke. The generated experimental
design was considered to be efficiently applicable when the G Efficiency was
greater than 60% (Myers, 2009). Analysis of variance (ANOVA) was employed
to determine the significance of the relationship between the independent and
dependent variables, for which was judged by the coefficient of determination
(R-squared and adjusted R-squared) and p-value (p<0.05). The model and
its coefficients were considered significant when the R-squared and adjusted
R-squared were greater than 0.900 and p-value (p<0.05) which indicated that
the model was statistically significant at α-level of 0.05. The model was valid
when the lack of fit was not significant (p>0.05) which indicated a good fit, a
low coefficient of variance (C.V.% <10) which indicated that the experiments
performed were highly reliable, and adequate precision (Adeq Precision > 4)
which indicated that the model as fitted was adequate for predicting (Noordin
et al., 2004; Ramkrishna & Susmita, 2012).
6.3.1 Establishing the effect of the process parameters on
individual responses
The design of the experiment for establishing the effect of the process
parameters on individual responses (Noordin et al., 2004) was generated by
D-Optimality shown in Table 6.1.
85
Table 6.1 Experimental design
Factors
Responses
Run
Air
Temperature
(˚C)
Dew Point
Temperature
(˚C)
Time
(min)
Final
Peak
Starch
Viscosity Viscosity Content
(RVU)
(RVU)
(% db)
1
60
25
134
250.13
388.83
82.92
2
60
35
136
264.08
394.81
82.81
3
50
15
200
258.71
394.82
83.29
4
50
35
158
258.67
393.31
83.30
5
60
35
130
261.88
392.57
82.21
6
70
35
128
278.08
411.42
84.68
7
70
15
90
266.83
408.92
83.73
8
50
35
157
258.67
410.17
82.34
9
60
25
138
263.22
408.92
83.81
10
50
15
172
242.30
409.54
81.13
11
70
15
84
273.71
409.23
83.22
12
60
15
104
265.08
409.39
83.23
13
70
25
125
264.13
409.31
83.62
14
50
15
175
257.58
407.42
82.46
15
70
35
127
269.42
412.55
82.01
16
50
25
210
303.25
408.36
82.12
The G Efficiency = 86% indicated that the experimental design was efficient for
analysis. The experiments for establishing the effect of processing parameters
on the individual responses were carried out at the same air velocity of 4.5
m/s, thickness of 3mm as recommended from the drying characteristics
study (Chapter 3) and using matooke at harvest maturity age of 17 weeks as
recommended optimum maturity window (Chapter 3).
Table 6.1 shows the results of the dependent variable (drying time to reach
moisture content of 0.10 (kg water/kg dry matter) which were obtained for the
above set conditions. From the ANOVA analysis (Table 6.2), it was observed
that the model and model terms were significant implying that the process
parameters had a significant effect on the drying time.
86
Table 6.2 ANOVA for Response Surface Quadratic Model analysis of
variance table for drying time
Source
Model
x1-Air Temp
x2-Dew P.Temp
x1x2
x12
x22
Residual
Lack of Fit
Pure Error
Cor Total
R-Squared
Adj R-Squared
C.V. %
Sum of
Squares
17863.00
12085.12
548.12
2325.38
1516.79
1489.31
1076.00
558.33
517.67
18939.00
df
5
1
1
1
1
1
10
3
7
15
Mean
Square
F
Value
p-value
Prob > F
3572.60
12085.12
548.12
2325.38
1516.79
1489.31
107.60
186.11
73.95
33.20
112.32
5.09
21.61
14.10
13.84
< 0.0001 Significant
< 0.0001
0.0476
0.0009
0.0038
0.0040
not significant
2.52
0.1419
0.9432
0.9148
7.32
The model for drying time was significant (R2 = 0.943 and p<0.0001). Lack
of fit was not significant (p>0.1) indicated a good fit that the model was valid.
A low coefficient of variance (C.V.% = 7.32) indicated that the experiments
performed were highly reliable. Adequate precision (Adeq Precision = 17.82
> 4) indicated that the model as fitted was adequate for predicting.
The results of the dependent variables (final viscosity) were obtained for
the above set of processing parameters given in Table 6.1. From ANOVA
analysis (Table 6.3), it was observed that the model and model terms were
not significant implying that the processing parameters had no effect on the
final viscosity.
87
Table 6.3: ANOVA for Response Surface Quadratic Model analysis of
variance table for final viscosiy (pasting properties)
Source
Model
x1-Air Temp
x2-Dew P.Temp
x1x2
Residual
Lack of Fit
Pure Error
Cor Total
R-Squared
Adeq Precision
C.V. %
Sum of
Squares
df
Mean
Square
190.17
108.18
40.09
15.07
2467.81
2150.41
317.40
2657.98
3
1
1
1
12
5
7
15
63.39
108.18
40.09
15.07
205.65
430.08
F
Value
p-value
Prob > F
0.31
0.53
0.19
0.073
0.8190
0.4822
0.6667
0.7912
not significant
9.49
0.0051
significant
45.34
0.0715
1.398
5.42
The model for final viscosity was not significant (R2 = 0.0715 and p>0.05).
The lack of fit was significant (p<0.01), implying that the model was not
valid. Adequate precision (Adeq Precision = 1.4 less than 4) indicated
that the model as fitted was not adequate for predicting. However, a low
coefficient of variance (C.V.% = 5.42) indicated that the experiments were
highly reliable. It also showed that all model terms: air temperature and dew
point temperature, were not significant since p-value were greater than 0.05
(p<0.05). This could be attributed to the fact that the set temperature were
below the gelatinization temperature of 72˚C (Muranga, 1998). This was an
indication that the processing parameters did not have a significant effect on
final viscosity (pasting properties).
The results of the dependent variables (peak viscosity) were obtained for
the above set of processing parameters given in Table 6.1. From ANOVA
analysis (Table 6.4), it was observed that the model and model terms were
not significant implying that the processing parameters had no effect on the
peak viscosity.
88
Table 6.4: ANOVA for Response Surface Quadratic Model analysis of
variance table for peak viscosity (pasting properties)
Source
Sum of
Squares
df
Mean
Square
F
Value
p-value
Prob > F
Model
363.20
5
72.64
1.03
0.4502
x1-Air Temp
137.85
1
137.85
1.96
0.1922
x2-Dew P. Temp
21.86
1
21.86
0.31
0.5899
x1
5.12
1
5.12
0.07
0.7930
x22
191.66
1
191.66
2.72
0.1302
0.07
0.7993
0.74
0.5589
2
x1x2
4.81
1
4.81
Residual
704.89
10
70.49
Lack of Fit
170.47
3
56.82
Pure Error
53.41
7
76.43
1068.08
15
Cor Total
R-Squared
Adj R-Squared
CV %
not significant
not significant
0.340
0.010
2.08
The model for final viscosity was not significant (R2 = 0.34 and p>0.05). The
lack of fit was significant (p<0.01), implying that the model was not valid. The
low coefficient of variance (C.V.% = 2.08) indicated that the experiments were
highly reliable. It also showed that all model terms: air temperature and dew
point temperature, were not significant since p-value were greater than 0.05
(p<0.05). This was also an indication that the processing parameters did not
have a significant effect on peak viscosity (pasting properties).
Table 6.1 shows the results of the starch content which were obtained for
the set of processing parameters. From ANOVA analysis (Table 6.5), it was
observed that the model and model terms were not significant implying that
the process parameters had no effect on the starch content, since the set
temperatures were below the gelatinization temperature of 72˚C.
89
Table 6.5 ANOVA for Response Surface Quadratic Model analysis of
variance table for starch content.
Source
Model
x1-Air Temp
x2-Dew P. Temp
Residual
Lack of Fit
Pure Error
Cor Total
Sum of
Squares
2.82
2.81
3.358E-3
8.42
1.31
7.10
11.23
R-Squared
C.V. %
Adeq Precision
0.2507
0.97
3.016
df
2
1
1
13
6
7
15
Mean
F
p-value
Square
Value
Prob > F
1.41
2.17
0.1532 not significant
2.81
4.34
0.0576
3.358E-3 5.186E-3
0.9437
0.65
0.22
1.01
0.22
0.9596 not significant
The model for starch content was not significant (R2 = 0.251 and p>0.05).
Also the model terms were not significant, which indicated that the processing
parameters had no significant impact on starch content. Adequate precision
(Adeq Precision = 1.4 less than 4) indicated that the model as fitted was not
adequate for predicting. However, the lack of fit was significant (Prob>F)
implying that the model was valid although it was not significant. A low
coefficient of variance (C.V.% = 0.97) indicated that the experiments were
highly reliable.
Considering Tables 6.3, 6.4 and 6.5 showing the effect of processing
parameters on physicochemical properties of matooke flour, pasting
properties (final and peak viscosities) and starch content. It was observed that
the processing parameters had no significant effect on the physicochemical
properties of matooke flour. This could be attributed to the fact that the drying
air temperatures were below the gelatinization temperature of 72˚C (Muranga
1998).
6.3.2 Optimization of process Parameters
The D-Optimal experimental design used in drying process optimization is
shown in Table 6.6.The independent variables were: air temperature, dew
point temperature and airflow. The dependent variables (responses) were
90
drying time to reach moisture content of 0.1 (kg water/kg dry matter). The
objective for the optimization was therefore to minimize the drying time and
maximize starch content and peak viscosity. The G Efficiency = 62% indicated
that the experimental design was efficient for analysis.
Table 6.6 Drying time for different set drying conditions of matooke
Run
Air Temperature
(˚C)
Dew Point
Temperature (˚C)
Air velocity
(m/s)
Time (min)
1
50
25
6
168.75
2
70
35
3
250.50
3
50
35
6
212.00
4
60
35
3
315.00
5
70
35
6
103.50
6
50
35
3
401.00
7
50
15
6
117.00
8
70
15
3
205.00
9
60
25
6
117.30
10
50
35
6
260.00
11
50
25
3
365.00
12
50
15
4.5
204.67
13
70
35
6
129.67
14
70
15
6
83.30
15
70
15
3
205.00
16
50
15
3
328.00
17
70
15
6
80.00
18
60
15
4.5
154.00
19
70
25
4.5
125.00
20
70
25
4.5
125.00
The criteria for optimization was to minimize drying time and keeping in range
the drying air temperature and air velocity was used to determine the suitable
processing parameters.
The ANOVA analysis (Table 6.7), showed that the model for drying time is
significant (R2 = 0.9876 and p<0.0001). The lack of fit was not significant
91
(p>0.1) indicated a good fit and that the model was valid. A low coefficient of
variance (C.V.% = 7.43) indicated that the experiments were highly reliable.
Adequate precision (Adeq Precision = 32.44 greater than 4) indicated that
the model as fitted was adequate for predicting. It also showed that all model
terms were significant since p-value was less than 0.05 (p<0.05).
Table 6.7. ANOVA for Response Surface Quadratic Model analysis of
variance table
Source
Sum of
Squares
Model
x1-Air Temp
x2-Dew P. Temp
x3-Air Flow
x1x2
x1x3
x2x3
x12
x22
x32
Residual
Lack of Fit
Pure Error
Cor Total
1.717E+005
51221.41
14786.18
96196.24
2616.68
3272.26
150.50
227.44
359.88
1359.28
2153.95
654.07
1499.88
1.738E+005
C.V. %
R-Squared
Adj R-Squared
Pred R-Squared
Adeq Precision
7.43
0.988
0.977
0.952
32.436
Mean
Square
df
9
1
1
1
1
1
1
1
1
1
10
5
5
19
F
Value
19074.32
88.55
51221.41 237.80
14786.18
68.65
96196.24 446.60
2616.68
12.15
3272.26
15.19
150.50
0.70
227.44
1.06
359.88
1.67
1359.28
6.31
215.40
130.81 0.44
299.98
p-value
Prob > F
< 0.0001 Significant
< 0.0001
< 0.0001
< 0.0001
0.0059
0.0030
0.4227
0.3284
0.2252
0.0308
0.8082
Not significant
Final Model Equation in terms of actual factors:
Y=1381.36-18.4*x1+5.99*x2-1.62*x3-0.15*x1*x2+7.9E-3*x1*x3+1.7e-32
2
2
*x3+0.1*x1 +0.1* x2 +5.9E-4*x3
*x
2
Where:Y= Drying time to reach moisture content of 10% (db).
x1
= Drying Air Temperature (˚C)
x2 = Dew Point Temperature (˚C)
x3
= Air velocity(m/s)
6.1
92
Figure 6.1B – 6.3B (appendix B) show the response surfaces of the effect
of air velocity, drying air temperature and dew point temperature on drying
time indicating the predicted time. Figure 6.4B – 6.6B (appendix B) show
the response surfaces of the effect of drying air temperature and dew point
temperature on final viscosity, peak viscosity and starch content indicating the
predicted values respectively.
6.3.3 Optimum processing parameters for drying matooke
Therefore, optimization showed that optimum processing parameters for
matooke were:
Drying Air Temperature = 69.58˚C
Dew Point Temperature = 18˚C
Air velocity
= 6 m/s (overflow mode)
Thickness = 3 mm (as recommended in Chapter 3)
The drying air temperature can be rounded to 70˚C and air velocity to 6m/s.
Considering the quality attribute (total color change), there was minimum
color change with air velocity of 6m/s. The product temperature development
during drying for both drying processes: controlled by drying air temperature
and another one controlled by product temperature does not reach the drying
air temperature because of the temperature gradient required for continued
drying (Chapter 5). It was concluded that, 70˚C was the optimum drying air
temperature for drying matooke as given in Table 6.8.
Table 6.8: Optimum drying conditions/parameters for matooke (Musa
sp, AAA-AEHB)
Parameter
Optimum Conditions
Drying air temperature (˚C)
70
Dew point temperature (˚C)
18
Air velocity (m/s)
6
93
6.3.4 Validation of the optimum processing parameters for
matooke
Table 6.9 shows the comparison between the predicted and actual experimental
values. The validation of the optimum drying conditions investigated by
applying the following optimum drying conditions: thickness 3 mm, drying air
temperature 70˚C, dew point temperature 18˚C and air velocity 6 m/s. This
confirms that those optimum conditions predicted by the model are the best
for the drying of matooke.
Table 6.9. Comparison between the predicted values and actual values.
Response
Actual
Predicted
Time (min)
84.08
77.80
Final viscosity (RVU)
243.21
272.34
Peak viscosity (RVU)
396.17
411.12
Starch content (% db)
83.46
83.65
The optimum drying air temperature of 70˚C is greater than the recommended
drying air temperature by Muranga, (1998) of 50˚C for drying matooke and yet
the results of starch content (%db) are within the same range. Also the results
for pasting properties are within the same range as reported by Muranga et al.,
2010 (Table 10). In addition, the results were in line with Islam et al., (2012),
who reported that appropriate conditions of dehydration enhance the drying
performance and drying air temperature of 65˚C gave faster drying of green
bananas without any irregular burning or stake burning. Also Leite et al., (2007)
dried bananas at 60 and 70˚C, reported that the chemical composition was not
affected by drying conditions. In addition, considering the results of product
temperature development during the drying controlled product temperature,
the drying temperature went up to 90˚C without affecting the physicochemical
properties (Chapter 5). This implies that air drying temperature has no effect
on the physicochemical properties as long as the product temperature is
below the gelatinization temperature of matooke starch (72˚C).
94
Table 6.10 Comparison of current results of this study with previous
research
Model
Prediction
Validation Exp.
results
Source (Muranga,
et al.,2010)
Drying air temperature (˚C)
70
70
50
Final viscosity (RVU)
272.34
243.21
244.7
Peak viscosity (RVU)
411.12
396.17
375.92
Starch content (%db)
83.65
83.46
ND
6.4
Conclusions and Recommendations
The optimum processing parameters for single-layer drying of matooke were
determined to obtain the minimum drying time, maximum starch content, final
viscosity and peak viscosity. The drying air temperature and air velocity were
kept within the range, the thickness was maintained at 3 mm (as recommended
in Chapter 5, and the average dew point temperature of ambient at Bushenyi
was used. The optimum processing parameters for single-layer drying of
matooke are the same as those obtained in Chapter 5 for the analysis of
drying behavior. For which the desirable responses were: shortest drying time,
highest drying rate constant, highest effective diffusivity and smallest color
change. The processing parameter which fulfills all the four mentioned criteria
would be considered as the best, thus the combination of those processing
parameters would give the optimum processing parameters of matooke.
The processing parameters do not have a significant effect on the
physicochemical and functional properties, and quality attribute as long as
the product temperature does not exceed 72˚C (gelatinization temperature of
matooke).
Therefore, the optimum processing parameters for single-layer drying of
matooke are: Thickness 3mm, air temperature 70˚C, dew point temperature
18˚C and air velocity 6m/s overflow mode.
General Conclusions and Recommendations
95
Chapter Seven
Banana is an important crop in Uganda; it is a staple food for more than 70%
of Uganda’s population and contributes to about 42% of household income in
rural areas. The most common type of banana is locally called matooke, Musa
sp triploid acuminate genome group (AAA-EAHB).
The study was carried out using a research dryer in the laboratory employing the
overflow mode. The results can be applied directly for commercial processing
of bananas (matooke), nevertheless, verification for online processing plant is
recommended.
This study has bridged the gap of lack of accurate information on the harvest
maturity window for commercial processing of matooke into flour. The
physiological maturity age of matooke (mbwazirume cv. Musa sp. AAA EAHB)
at Bushenyi located at the Latitude: 0.59°S, Longitude: 30.21° E and Altitude
1570 m is 21 weeks. The optimum harvest maturity window for commercial
processing of matooke at Bushenyi is between 15-21 weeks. Matooke
harvested within the above optimum harvest maturity window can be used
for processing raw matooke flour referred to as Raw Tooke Flour (RTF) of a
consistent quality on the market for use in food and non food industries.
In case of natural disaster like storm, matooke from the age of 12-15 weeks
can be processed into matooke flour since at that age it already has starch
content above 80% (db). The matooke from maturity age between 3 – 12
weeks can be processed for flour for people who require less starch in their
diet.
In addition, matooke harvested beyond the above maturity window after 21
weeks is recommended for processing Instant Tooke Flour (ITF). This is
because beyond the recommended harvest maturity window, it would be in
the initial stages of starch loss which plays a major role in textural changes in
ripening which is good for ITF.
96
maturity age for matooke and the combined model of finger weight and
pulp peel ratio is recommended for commercial processors. Matooke cv
mbwazirume (Musa sp. AAA-EAHB) at Bushenyi.
Theoretically the results of the study can be used elsewhere. Nevertheless,
it is recommended that similar work be carried out in the matooke growing
areas to determine the applicable models for those particular regions since
development of matooke and bananas in general is affected by location
(Robinson & Saúco, 2010)
This study has provided information for the first time on the moisture sorption
isotherm of matooke. The results of this study on moisture sorption isotherm
of matooke can be used for prediction of quality, design, modeling and
optimization of many processes such as drying, aeration and storage. In
addition, it is also important for predicting quality changes during packaging
and storage of dried matooke.
Matooke isotherms exhibited type II curves behavior which is characteristic of
foodstuffs as reported in literature (Johnson & Brennan, 2000; Yan et al., 2008).
The equilibrium moisture content decreases with increase in temperature at
a given water activity (aw), indicating that matooke becomes less hygroscopic
with increase in temperatures giving a clear stability domain of matooke after
drying leading to extensive shelf life. This complements Muranga, (1998), who
attributed the extensive shelf life to low sugar and fat. The GAB model best
described all the adsorption and desorption moisture isotherms for matooke,
this was consistent with literature on bananas and plantain. The GAB monolayer
has been reported to be indicative for optimum moisture content for storage
conditions for hydrated foods. For the case of matooke, the GAB monolayer
for adsorption isotherm at 30˚C was 0.087 (kg water/kg dry matter), which was
below the recommended safe water activity level of 0.6. The water activity
level of 0.6 corresponds to equilibrium moisture content of 0.11 (kg water/kg
dry matter) on the adsorption isotherm at 30˚C. Therefore, for commercial
processing of matooke in order to obtain a microbiologically shelf-stable dry
matooke, it is recommended to dry matooke to moisture content below or
equal to 11 (kg water/kg dry matter) equivalent to 10% (wb).
97
The hysteresis phenomenon was exhibited by the moisture sorption isotherms
for matooke. It was reported that in food preservation and storage technology,
the microbiological stability of stored food item depend more on water activity
than total moisture content (Caurie, 2007). Therefore, hysteresis could be used
as a food quality index. It is therefore recommended that further investigations
on hysteresis of matooke at different temperatures be carried out to establish
the optimum range of temperatures safe storage, since increased hysteresis
being indicative of reduced stability and reduced hysteresis being indicative of
improved stability of stored dry matooke.
The isoteric heat of sorption for both adsorptions increased with decreased
moisture content, however, desorption was higher than the adsorption and this
was in line with literature on bananas and plantains (Ciro et al., 2008; Johnson
& Brennan, 2000). It was reported that isosetric heat of sorption for adsorption
isotherm is a measure for the energy released during adsorption and that
for desorption as the energy required for breaking the intermolecular forces
between the molecules of water vapor and the surface of adsorbent. Thus
the ioseteric heat of sorption is considered as indicative of the intermolecular
attractive forces between the sorption sites and the water vapor. The total
isosteric heat of sorption for adsorption isotherm for matooke ranged from
4,586 – 2,386.39 kJ/kg for equilibrium moisture content from 0.3 – 0.01 (db)
respectively. While the total isosteric heat of sorption for desorption isotherm
for matooke ranged from 18,194 – 2,391 kJ/kg for equilibrium moisture content
from 0.3 – 0.01 (db) respectively.
This study has revealed that the minimum energy required for drying matooke
from 3.5 (kg water/kg dry matter) (80%wb) to 0.11 (kg water/kg dry matter)
(10%wb) is equal to 8,124 kJ/kg of water removed. Implying that the minimum
energy required for drying of 1 kg of fresh matooke from 80 - 10(% wb) is
5,793 kJ.
This study has bridged the gap of lack of information on drying behavior
of matooke. Both thickness and dew point temperature showed a direct
relationship with drying time, while the air temperature and air velocity showed
an inverse relationship with drying time to reach the desired moisture content.
The effect of processing parameters on the drying characteristics showed that
air velocity rate, drying air temperature and thickness had a significant effect,
98
while dew point did not have significant effect on the drying characteristics of
matooke. The drying of matooke takes place in three steps: the warm-up and
the two falling rate periods; the first falling rate and second falling rate. This
was in line with the literature on bananas and plantains (Prachayawarakon et
al., 2008).
For all processing parameters, the drying rate constant ranged from 0.59 –
2.49 /h, and effective diffusivity ranged from 1.5E-10 – 8.27E-10 m2/s. The
activation energy (Ea) has been reported to be the minimum energy that must
be supplied to break water-solid and/or water-water interactions and to move
the water molecule from one point to another in solid. The results show that
activation energy (Ea) for matooke was 16.3kJ/mol (1,605kJ/kg). Comparing
the activation energy (Ea) with the net isosteric heat of desorption (qst), Ea
(1,605kJ/kg) was higher than qst (1,297.62) at 0.1 (kg water/kg dry matter) (the
moisture content used in the drying analyses). This suggests that moisture
molecules travel in liquid form in matooke slices as reported in literature
(Demirel & Turhan, 2003).
The total color difference (ΔE*) between the fresh and dry samples, showed an
inverse relationship between the effects of processing parameters on the total
color difference (ΔE*). The total color difference (ΔE*), was lowest for effect
of thickness was 7 mm, but it took the longest time to dry. It was followed by
the effect of air velocity which was 6 m/s, and lastly for the effect of drying air
temperature which was 70˚C.
In the drying system controlled by setting the drying air temperature,
product temperature increased with drying time, tending to the set drying
air temperature. However, the product temperature did not exactly equal
to the drying air temperature. Whereas, in the drying system controlled by
setting product temperature, the drying air temperature shot up to high as
possible, for example when the product temperature was set at 50˚C, the
air drying temperature shot up to 90˚C and decreased tending to the set
product temperature with increase in drying time. In this case also, product
temperature became constant and nearly equal to air temperature which
was an indication that the samples had dried and attained the equilibrium
moisture content at the set air temperature. The drying system controlled by
setting product temperature, the drying time was reduced by 50% of setting air
99
drying temperature. This implied that the drying system controlled by product
temperature, automatically turned itself into stepwise (step-down) drying
system. It was therefore recommended to carry out further investigation to
develop a drying system which is controlled by product temperature instead of
drying air temperature. Further investigation on the effect of surface product
temperature in association with case hardening should be carried out.
The processing parameters did not have a significant effect on the
physicochemical (starch content) and functional properties (peak and final
viscosity) of matooke. The optimum drying air temperature of 70˚C is greater
than the recommended drying air temperature by Muranga, (1998) of 50˚C
for drying matooke and yet the results of starch content (%db) are within the
same range. This implies that drying matooke using air drying temperature
of 70˚C gives the same product at a reduced drying time and hence the most
appropriate. Considering the development of product temperature during
drying, (drying systems, one controlled by setting drying air temperature and
one controlled setting the product temperature) and the fact that the processing
parameters do not have influence on the physicochemical and functional
properties of matooke. This suggests that any drying air temperature can be
used for drying as long as the product temperature does not exceed 68˚C (the
onset of gelatinization for matooke).
Therefore, the optimum processing parameters for single-layer drying of
matooke are: thickness = 3mm, air temperatures 70˚C, dew point temperature
18˚C and air velocity 6 m/s overflow mode.
However, from practical point of view, by considering the difference in
loading capacities between single layer drying (2.33 kg/m2 taking 1.92h to
dry) and multi-layer drying (6.78 kg/m² taking 5.07 h to dry), and the number
of batches per day (12 working hours). It is more practical to run 2 batches
(multi-layer drying) than 6 batches (single-layer drying) in a 12h. It is therefore
recommended for commercial processing of matooke to employ multi-layer
drying of loading capacity equal or less than 7 kg/m², thickness 3 mm, air
temperatures 70˚C, dew point temperature 18˚C and air velocity 6m/s overflow
mode.
100
7.1 Recommended Procedure for Commercial Processing
Matooke Flour
•
Matooke located at the Latitude: 0.59°S, Longitude: 30.21° E and Altitude:
1570 m. should be harvested at maturity age between 15 – 21 weeks.
•
The finger weight model was recommended for farmers to estimate
harvest maturity for matooke and the combined model of finger weight
and pulp peel ratio is recommended for industrial use.
•
Recommended Standard Procedure for Commercial Processing Matooke
Flour (Raw Tooke Flour) (Modification of the Patent No. AP/P/2005/003308
as follows:
Multi-layer drying:
7.2
–
De-finger the fingers from the bunch
–
Peel the fingers
–
Pre-treat with 1% Sodium Meta-bisulphate solution for 3 – 5
minutes
–
Slice thickness should be equal to 3 mm
–
Pre-treatment in 1% Sodium Meta-bisulphate for 3- 5 minutes
–
Drain the slices
–
Load the trays and load the trays in the dryer
–
Drying air temperature between 65 - 70˚C
–
Dew point temperature of ambient conditions between 16 – 22˚C
–
Air velocity rate equal to 6 m/s
–
Loading capacity should not exceed 7 kg/m2.
–
Drying time equal to 6 h ± 20 min
Recommendations for future work
•
The maturity experiments can be carried out for different cultivars and
for other climatic zones to determine the respective optimum maturity
window.
•
Similar studies are carried out for different cultivars and in other matooke
growing areas to validate the developed models and/or improve them for
those particular regions since development of matooke and bananas in
general is affected by location.
101
•
There is need for research to develop electronic or magnetic sensors/units
which can be used to estimate maturity age by employing the developed
models for estimating the harvest maturity window.
•
The hysteresis phenomenon was exhibited and since it could be used
as a food quality index, it is recommended that further investigations of
hysteresis phenomenon at different temperatures to establish the ranges
of increased hysteresis being indicative of reduced stability and reduced
hysteresis being indicative of improved stability of stored product.
•
There is need for more work to be carried out to optimize energy
requirement for the above recommended parameters.
•
Research on developing a drying system which can be operated by
setting the product temperature instead of the conventional method of
stepwise drying.
•
Research on developing equipment for non destructive determination
of maturity age of matooke using the combined maturity model (finger
weight combined with pulp/peel ratio.
•
Further investigation on the effect of surface product temperature in
association with case hardening should be carried out.
102
Summary
Summary
Cooking banana is one of the most important crops in Uganda; it is a staple
food and source of household income in rural areas. The most common
cooking banana is locally called matooke, a Musa sp triploid acuminate
genome group (AAA-EAHB). It is perishable and traded in fresh form leading
to very high postharvest losses (22-45%). This is attributed to: non-uniform
level of harvest maturity, poor handling, bulk transportation and lack of value
addition/processing technologies, which are currently the main challenges for
trade and export, and diversified utilization of matooke.
Drying is one of the oldest technologies employed in processing of agricultural
produce. A lot of research has been carried out on drying of fruits and
vegetables, but little information is available on matooke. Drying of matooke
and milling it to flour extends its shelf-life is an important means to overcome
the above challenges.
Raw matooke flour is a generic flour developed to improve shelf stability of
the fruit and to find alternative uses. It is rich in starch (80 - 85%db) and
subsequently has a high potential as a calorie resource base. It possesses
good properties for both food and non-food industrial use. Some effort has
been done to commercialize the processing of matooke but there is still limited
information on its processing into flour. It was imperative to carry out an indepth study to bridge the following gaps: lack of accurate information on the
maturity window within which matooke for processing into flour can be harvested
leading to non-uniform quality of matooke flour; there is no information on
moisture sorption isotherm for matooke from which the minimum equilibrium
moisture content in relation to temperature and relative humidity is obtainable,
below which the dry matooke would be microbiologically shelf-stable; and lack
of information on drying behavior of matooke and standardized processing
parameters for matooke in relation to physicochemical properties of the flour.
The main objective of the study was to establish the optimum harvest maturity
window and optimize the processing parameters for obtaining standardized
microbiologically shelf-stable matooke flour with good starch quality attributes.
This research was designed to: i) establish the optimum maturity harvest
window within which matooke can be harvested to produce a consistent quality
of matooke flour, ii) establish the sorption isotherms for matooke, iii) establish
Summary
103
the effect of process parameters on drying characteristics of matooke, iv)
optimize the drying process parameters for matooke, v) validate the models
of maturity and optimum process parameters and vi) standardize process
parameters for commercial processing of matooke.
Samples were obtained from a banana plantation at Presidential Initiative
on Banana Industrial Development (PIBID), Technology Business Incubation
Center (TBI) at Nyaruzunga – Bushenyi in Western Uganda. A completely
randomized design (CRD) was employed in selecting the banana stools from
which samples for the experiments were picked. The cultivar Mbwazirume
which is soft cooking and commonly grown in Bushenyi was selected for the
study. The static gravitation method recommended by COST 90 Project (Wolf
et al., 1985), was used for determination of moisture sorption isotherms. A
research dryer developed for this research. All experiments were carried out
in laboratories at TBI.
The physiological maturity of matooke cv. mbwazirume at Bushenyi is 21
weeks. The optimum harvest maturity window for commercial processing of
matooke flour (Raw Tooke Flour - RTF) at Bushenyi is between 15-21 weeks.
maturity for matooke and the combined model of finger weight and pulp peel
ratio is recommended for commercial processors.
Matooke isotherms exhibited type II curve behavior which is characteristic of
foodstuffs. The GAB model best described all the adsorption and desorption
moisture isotherms. For commercial processing of matooke, in order to obtain
a microbiologically shelf-stable dry product. It is recommended to dry it to
moisture content below or equal to 10% (wb). The hysteresis phenomenon
was exhibited by the moisture sorption isotherms for matooke. The isoteric
heat of sorption for both adsorptions and desorption isotherms increased with
decreased moisture content. The total isosteric heat of sorption for matooke:
adsorption isotherm ranged from 4,586 – 2,386 kJ/kg and desorption isotherm
from 18,194– 2,391 kJ/kg for equilibrium moisture content from 0.3 – 0.01
(db) respectively. The minimum energy required for drying matooke from 80 –
10% (wb) is 8,124 kJ/kg of water removed. Implying that the minimum energy
required for drying of 1 kg of fresh matooke from 80 - 10% (wb) is 5,793 kJ.
104
Summary
The drying of matooke takes place in three steps: the warm-up and the two
falling rate periods. The drying rate constant for all processing parameters
ranged from 5,793 kJ and effective diffusivity ranged from 1.5E-10 - 8.27E-10
m2/s. The activation energy (Ea) for matooke was 16.3kJ/mol (1,605 kJ/kg).
Comparing the activation energy (Ea) with the net isosteric heat of sorption for
desorption isotherm (qst) (1,297.62) at 0.1 (kg water/kg dry matter), indicated
that Ea was higher than qst suggesting that moisture molecules travel in liquid
form in matooke slices.
The total color difference (ΔE*) between the fresh and dry samples, was lowest
for effect of thickness of 7 mm, followed by air velocity of 6 m/s, and then drying
air temperature at 70˚C. The drying system controlled by set surface product
temperature, reduced the drying time by 50% compared to that of a drying
system controlled by set air drying temperature. The processing parameters
did not have a significant effect on physicochemical and quality attributes,
suggesting that any drying air temperature can be used in the initial stages
of drying as long as the product temperature does not exceed gelatinization
temperature of matooke (72˚C).
The optimum processing parameters for single-layer drying of matooke are:
thickness = 3 mm, air temperatures 70˚C, dew point temperature 18˚C and air
velocity 6 m/s overflow mode. From practical point of view it is recommended
that for commercial processing of matooke, to employ multi-layer drying of
loading capacity equal or less than 7 kg/m², thickness 3 mm, air temperatures
70˚C, dew point temperature 18˚C and air velocity 6 m/s overflow mode.
Zusammenfassung
105
Zusammenfassung
Kochbananen sind eine der wichtigsten Anbauprodukte in Uganda, sie sind
Grundnahrungsmittel und Einkommensquelle in ländlichen Gebieten. Die
bekannteste Kochbanane, umgangssprachlich matooke genannt, ist die Musa
sp triploid acuminate genome group (AAA-EAHB). Sie ist verderblich und wird
im frischen Zustand gehandelt, so dass sehr hohe Nachernteverluste auftreten
(22-45%). Gründe dafür sind: Ernte bei nicht einheitlichem Reifegrad, wenig
schonende Aufbewahrung, Transport in großen Einheiten und vor allem
findet keine Weiterverabeitung bzw. Konservierung/Trocknung statt, was die
Grundlage für Handel, Export und vielfältige Nutzung von matooke wäre.
Trocknung ist eine der ältesten Technologien in der landwirtschaftlichen
Produktionskette. Es gibt zahlreiche Forschungsarbeiten zur Trocknung
von Obst und Gemüse, aber wenig ist über die Trocknung von matooke
bekannt. Trocknung von matooke und anschließendes Vermahlen diente der
Wertsteigerung und –erhaltung und würde die zuvor genannten Nachteile
kompensieren.
Rohes matooke -Mehl wurde ursprünglich entwickelt, um die Lagerstabilität
der verarbeiteten Früchte zu verbessern und alternative Nutzungen zu
finden, es hat einen hohen Stärkeanteil (80 - 85% db) und ist somit eine
kalorienreiche Nahrungsquelle. Es hat gute Eigenschaften sowohl für
den Nahrungsmittelbereich, als auch für den non-food Bereich. Einige
Bemühungen wurden unternommen, um die Weiteverarbeitung von matooke
auszuweiten, aber Wenig ist über die Verarbeitung zu Mehl bekannt. Es war
somit notwendig detailliertere Studien in die Wege zu leiten, um folgende
Lücken zu füllen: genaue Kenntnisse über das optimale Reifefenster zur Ernte
von matooke, um gleichmäßige und qualitativ hochwertiges Mehl zu erhalten;
es gibt keine Informationen über des Sorptionsverhalten von matooke, aus
dem das Feuchtegleichgewicht in Abhängigkeit von Temperatur und relativer
Luftfeuchte zu ermitteln ist, bei dem getrocknete matooke mikrobiologisch
stabil gelagert werden kann; außerdem gibt es keine Informationen über das
Trocknungsverhalten von matooke sowie standarisierte Prozessparameter
von matooke in Bezug auf physiochemisch Eigenschaften des Mehl.
Das Hauptanliegen dieser Forschungsarbeit besteht darin, ein optimales Reifeund Erntefenster zu definieren, so wie optimale Weiterverarbeitungsparameter
zu bestimmen, die ein standarisiertes mikrobiologisch einwandfreies Mehl
106
Zusammenfassung
mit guten Stärke Eigenschaften garantieren. Diese Arbeit hat folgende Ziele
: i) ein optimales Reifefenster zu definieren, innerhalb dessen matooke
geerntet werden kann um gleichbleibendes matooke Mehl zu erhalten, ii)
die Sorptionsisothermen für matooke aufzuzeigen iii) die Auswirkungen
verschiedener Trocknungsvarianten von matooke aufzeigen, vi) die
Trocknungsparameter optimieren v) Wertungen der verschiedenen Modelle
zu Reifegrad und Verarbeitungsschritten erstellen vi) Standardkriterien für
eine kommerzielle Weiterverarbeitung von matooke zu definieren.
Das Probenmaterial stammt ausschließlich von der Bananen Plantage der
“Presidential Initiative on Banana Industrial Development (PIBID), Technology
Business Incubation Center (TBI) at Nyaruzunga – Bushenyi” in West Uganda.
Nach einem vollständig randomisierter Entwurf (completely randomized
design (CRD) wurden die Bananen Stauden für die Versuche beerntet.
Für die Untersuchungen wurde der weichkochende Kultivar Mbwazirume,
der in Bushenyi weitverbreitet ist, ausgewählt. Zur Bestimmung der
Sorptionsisothermen wurde die Methode COST 90 Project (Wolf et al., 1985)
eingesetzt. Es wurde ein eigens entwickelter Versuchstrockner verwendet.
Alle Versuche wurden in den Laboren des TBI durchgeführt.
Die physiologische Reife von matooke cv. mbwazirume aus Bushenyi ist nach
21 Wochen erreicht. Das optimale Erntefenster zur Weiterverarbeitung als
matooke Mehl (Raw Tooke Flour - RTF) in Bushenyi liegt zwischen 15 – 21
Wochen. Zur Ermittlung der Erntereife von matooke wird den Landwirten
die Ermittlung des Fingergewichts empfohlen, während den kommerziellen
Weiterverarbeitern das kombinierte Modell Fingergewicht und Verhältnis
Fruchtfleisch zu Schale empfohlen wird.
Matooke Isothermen deuten auf den Typ II hin, welcher charakteristisch
für Nahrungsmittel ist. Am besten beschreibt das GAP Modell die Sorption
und Desorption der Isothermen. Um ein mikrobiologisch stabiles und
kommerziell handelsfähiges Lagerprodukt zu erhalten wird empfohlen
unter einen Restwassergehalt von 10 % (wb) zu trocknen. Die erhaltenen
Sorptionsisothermen für matooke weisen auf das Hysterese Phänomen
hin. Umso geringer die Restfeuchte, umso höher ist die isoterische
Sorptionswärme sowohl für Adsorptions- als auch für Desorptionsisothermen.
Die gesamte isoterische Sorptionswärme für matooke setzt sich wie folgt
zusammen: die Adsorptionsisotherme schwanken zwischen 4,586 – 2,386
Zusammenfassung
107
kJ/kg und die Desorptionsisothermen zwischen 18,194– 2,391 kJ/kg für ein
Feuchtigkeitsgleichgewicht von 0.3 – 0.01 (db). Um matooke von 80 auf
10% (wb) runter zu trocknen ist die Energie von mindestens 8,124 kJ/kg zu
entfernendes Wasser notwendig. Was wiederum bedeutet, dass die Energie
von mindestens 5,793 kJ notwendig ist um 1 kg frisches matooke von 80 auf
10 % (wb) zu trocknen.
Die Trocknung von matooke erfolgt in drei Schritten: das Aufwärmen, gefolgt
von zwei abfallende Temperaturstufen. Die konstante Trocknungsrate für
alle Prozessparameter variiert zwischen 5,793 kJ und der tatsächliche
Diffusionskoeffizient liegt zwischen 1.5E-10 - 8.27E-10 m2/s. Die
Aktivierungsenergie (Ea) für matooke ist 16.3kJ/mol (1,605 kJ/kg). Vergleicht
man die Aktivierungsenergie (Ea) mit der isoterische Sorptionswärme von
Adsorptions- als auch von Desorptionsisothermen (qst) (1,297.62) bei 0.1
(kg Wasser/kg TS), so wird klar, dass Ea größer als qst ist, was vermuten
lässt, dass sich die Feuchtigkeitsmoleküle in matooke Scheiben im flüssigen
Zustand befinden.
Die geringsten Farbänderungen (ΔE*) treten bei einer Schichtdicke der
Scheiben von 7 mm, einer Umluftgeschwindigkeit im Trockner von 6 m/s,
und einer Trocknungstemperatur von 70 °C auf. Eine Reduzierung der
Trocknungszeit um 50 % kann erreicht werden, wenn die Oberflächentemperatur
des Produktes anstatt die Lufttemperatur im Trockner als Grundlage für die
Steuerung des Trockners genutzt wird. Es sind weder physiochemische noch
qualitative Einbußen zu erwarten, wenn die Temperatur in den ersten Stufen
der Trocknung unter 72 °C liegt und somit keine Verkleisterung des Produktes
stattfindet.
Die optimalen Prozessparameter bei der einlagigen Trocknung von matooke
sind : Scheibendicke = 3 mm, Lufttemperatur 70˚C, Taupunkt Temperatur
18˚C und Luftströmung über dem Produkt von 6 m/s. Bei der kommerziellen
Trocknung von matooke wird eine mehrschichtige Trocknung mit einer
Beladung des Trockners von weniger oder gleich 7 kg/m² empfohlen und die
o.g. Prozessparameter beizubehalten, Scheibendicke = 3 mm, Lufttemperatur
70˚C, Taupunkt Temperatur 18˚C und Luftströmung über dem Produkt von 6
m/s.
108
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Appendix A
122Appendix A
130
Appendix A
130
Appendix
Appendix A A
Appendix A
1.6
1.6
3 mm
Moisture
RatioRatio
(x/xo(x/x
)
Moisture
o)
1.2
3 mm
1.2
5 mm
5 mm
0.8
7 mm
0.8
7 mm
0.4
y = 1.4156e-0.035x
R² = 0.9844
y = 1.4156e-0.035x
R² = 0.9844
y = 1.3592e-0.016x
R² = 0.9873
y = 1.3592e-0.016x
R² = 0.9873
y = 1.228e-0.01x
R² = 0.9902
y = 1.228e-0.01x
R² = 0.9902
0.4
0
0
0
100
200
300
400
0
100
Time
200(min)
300
400
Time (min)
Figure 5.1A Plot of moisture ratio (experimental and predicted) of different
Figure 5.1A
Plot of moisture
ratio (experimental
predicted) of
thickness versus the
thickness
versus
the drying
time of and
matooke
atdifferent
air temperature
60odrying
C, dew
o
o
time of5.1A
matooke
temperature
60 C, dew point
temperature
C and airthickness
velocity 4.5m/s.
Figure
Plot at
of air
moisture
(experimental
and
predicted) 15
of different
versus the drying
point
temperature
15oCratio
and
air
velocity
4.5m/s.
o
o
time of matooke at air temperature 60 C, dew point temperature 15 C and air velocity 4.5m/s.
Time (min)
0
100
Time (min)
200
300
400
0
100
200
300
400
0
0
-2
Ln(Moisture
Ratio)
Ln(Moisture
Ratio)
-2
-4
-4
-6
-6
-8
-8
-10
-10
3 mm
3 mm
5 mm
5 mm
7 mm
7 mm
y = -0.0347x + 0.3476
R² = 0.9844
y = -0.0347x + 0.3476
R² = 0.9844
y = -0.0164x + 0.3069
R² = 0.9873
y = -0.0164x + 0.3069
= 0.9873
y = R²
-0.01x
+ 0.2054
R² = 0.9902
y = -0.01x + 0.2054
R² = 0.9902
Figure 5.2A Plot of Logarithm of moisture ratio at different thickness versus
the drying time of matooke at air temperature 60oC, dew point temperature
15oC and air velocity 4.5m/s.
Appendix A
131
Figure 5.2A Plot of Logarithm of moisture ratio at different thickness versus the drying time of matooke at
Appendix
AppendixAA
131123
o
o
air temperature 60 C, dew point temperature 15 C and air velocity 4.5m/s.
Moisture Ratio
(x/xo)Ratio (x/xo)
Moisture
Figure 5.2A Plot of Logarithm of moisture ratio at different thickness versus the drying time of matooke at
1.6
o
o
air temperature 60 C, dew point temperature 15 C and air velocity 4.5m/s.
y = 1.1057e-0.022x
50˚C
R² = 0.9882
1.6
1.2
y = 1.3527e-0.035x
60˚C
-0.022x
y = 1.1057e
50˚C
R² = 0.9838
R² = 0.9882
1.2
0.8
70˚C
60˚C
0.8
0.4
-0.035x
1.3527e-0.04x
yy==1.3318e
0.9838
R²R²==0.9852
70˚C
y = 1.3318e-0.04x
R² = 0.9852
0.40
-
100
Time (min)
200
300
0
Figure 5.3A Plot of moisture
ratio (experimental and predicted) of different drying air temperature versus
100
200
300
o
the drying
timePlot
of matooke
at dew point temperature
15 C,
air velocity 4.5m/s
thickness 3mm
Figure
5.3A
of
moisture
ratio
(experimental
and and
predicted)
of different
Time
(min)
drying air temperature versus the drying time of matooke at dew point
Figure 5.3A Plot of moisture ratio (experimentalTime
and(min)
predicted) of different drying air temperature versus
temperature 15oC, air velocity 4.5m/s and
thickness 3mm
o
the drying time of matooke
at dew point temperature 15 C, air velocity 4.5m/s and thickness 3mm
0
0
100
200
Time (min)
Ln(Moisture Ln(Moisture
Ratio)
Ratio)
0
-1
0
100
200
70˚C
50˚C
-1
-2
60˚C + 0.1717
y = -0.0224x
R² = 0.9884
70˚C
-2
-3
y = -0.0415x + 0.3355
R² = 0.9841
-4
-3
y = -0.0415x + 0.3355
R² = 0.9841
-4
50˚C
60˚C
y = -0.0224x + 0.1717
R² = 0.9884
y = -0.035x + 0.3021
R² = 0.9838
y = -0.035x + 0.3021
R² = 0.9838
Figure 5.4A Plot of Logarithm of moisture ratio at different drying air temperature
versus the drying time of matooke at dew point temperature 15oC, air velocity
4.5m/s and thickness 3mm
Figure 5.4A Plot of Logarithm of moisture ratio at different drying air temperature versus the drying time of
o
matooke at dew point temperature 15 C, air velocity 4.5m/s and thickness 3mm
Appendix A
124
1.6
1.2
15˚C
y = 1.4169e-0.035x
R² = 0.984
25˚C
y = 1.3612e-0.029x
R² = 0.9884
35˚C
y = 1.3729e-0.029x
R² = 0.9724
0.8
0.4
0
-
50
100
150
200
Time (min)
Figure 5.5A Plot of moisture ratio (experimental and predicted) of different
Figure
5.5A temperature
Plot of moisture ratio
andofpredicted)
of different
point temperature
dew
point
on (experimental
drying time
matooke
at airdew
temperature
60oon
C, air
o
drying time of matooke at air temperature 60 C, air velocity 4.5m/s and thickness 3mm.
velocity
Appendix A4.5m/s and thickness 3mm.
133
Time (min)
0
Ln(Moisture Ratio)
-
50
100
150
200
-2
15˚C
-4
25˚C
35˚C
-6
y = -0.035x + 0.3484
R² = 0.984
y = -0.0293x + 0.3084
R² = 0.9882
y = -0.0287x + 0.3169
R² = 0.9724
Figure 5.6A Plot of Logarithm of moisture ratio at different dew point
o
Figure 5.6A Plot of
moisture
at different
temperature on
time velocity
of
temperature
onLogarithm
dryingoftime
of ratio
matooke
at dew
air point
temperature
60drying
C, air
o
matooke at air temperature 60 C, air velocity 4.5m/s and thickness 3mm.
4.5m/s
and thickness 3mm.
Appendix
Appendix
AA
134 125
134
Appendix A
2.4
2.4
Moisture
Moisture
Ratio
Ratio
(x/x(x/x
o) o)
y = 1.0073e-0.009x
-0.009x
= 0.9816
y =R²
1.0073e
R² = 0.9816
3 m/s
3 m/s
2
2
1.6
1.6
4.5 m/s
4.5 m/s
1.2
1.2
6m/s
6m/s
0.8
0.8
y = 1.2212e-0.02x
-0.02x
= 0.9879
y =R²1.2212e
R² = 0.9879
y = 2.1568e-0.05x
-0.05x
= 0.7941
y =R²2.1568e
R² = 0.7941
0.4
0.4
0
0 0
0
100
100
200
300
200Time (min)300
400
400
500
500
Time (min)
Figure
Plot
of moisture
ratio (experimental
and predicted)
of drying
different
Figure5.7A
5.7A Plot
of moisture
ratio (experimental
and predicted) of different
air velocity on the
time air
o drying time
o
o
Figure
5.7A
Plot
moisture time
ratio
predicted)
of
air thickness
velocity
velocity
on atthe
drying
ofdew
matooke
at airoftemperature
of on
50
C, dew point
C,
point and
temperature
15different
C and the
of the
3mm.
of
matooke
air of
temperature
of 50(experimental
o
o
of matooke at air temperature
of 50 C, dew point temperature of 15 C and the thickness of 3mm.
temperature
of 15oC and
the thickness of 3mm.
Ln(Moisture
Ln(Moisture
Ratio)
Ratio)
0
0 0
0
-1
-1
-2
-2
Time (min)
Time (min)
100
100
200
200
3 m/s
3 m/s
4.5 m/s
4.5 m/s
6 m/s
6 m/s
-3
-3
300
300
y = -0.0099x + 0.133
R² = 0.9946
y = -0.0099x
+ 0.133
R² = 0.9946
y = -0.0224x + 0.0848
R² = 0.9879
y = -0.0224x
+ 0.0848
R² = 0.9879
y = -0.0269x + 0.0831
R² = 0.9908
y = -0.0269x
+ 0.0831
R² = 0.9908
Figure 5.8A Plot of Logarithm of moisture ratio at air velocity on the drying time
of matooke at air temperature of 50oC, dew point temperature of 15oC and the
thickness of 3mm.
126
Appendix B
Appendix B
Figure 6.1B Response surface showing the effect of air temperature and dew
point temperature on drying time
Figure 6.2B Response surface showing the effect of air temperature and air
velocity on drying time. (Air velocity (m/s): 3, 4.5 & 6 are represented by air
flow (m3/h): 400, 600 & 800 respectively.)
Appendix B
127
Figure 6.3B Response surface showing the effect of air velocity and dew point
temperature on drying time. (Air velocity (m/s): 3, 4.5 & 6 are represented by
air flow (m3/h): 400, 600 & 800 respectively).
point temperature on pasting properties (final viscosity).
128
Appendix B
point temperature on pasting properties (peak viscosity).
point temperature on starch content

Optimization of processing technology for commercial

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