2013 annual progress reports - OARDC

Transcription

NC-213
(The U.S. Quality Grains Research Consortium)
2013 ANNUAL PROGRESS REPORTS
Published: February 2014
MARKETING AND DELIVERY OF QUALITY GRAINS AND
BIOPROCESS COPRODUCTS
2013 Officers
Chair .............................................................................................................. Klein Ileleji, Indiana – Purdue University
Vice Chair............................................................................................... Senay Simsek, North Dakota State University
Secretary ........................................................................................ Rose P. Kingsly Ambrose, Kansas State University
Past Chair......................................................................................................... Carol Jones, Oklahoma State University
Industry Advisory Committee Chair............................................................................... Chuck Hill, AgriGold Hybrids
CSREES/USDA Representative .....................................................................Hongda Chen, National Program Leader,
Bioprocessing Engineering/Nanotechnology,
USDA-National Institute of Food and Agriculture
Administrative Advisor/Coordinator ....................................... F. William Ravlin, The Ohio State University/OARDC
Administrative Associate and Report Production ............................... Bill Koshar, The Ohio State University/OARDC
Participating Stations
Representatives
University of Idaho ....................................................................................................................................... Dojin Ryu*
University of Illinois ................................................................................................................................... Vijay Singh*
Grace Danao, Peter Goldsmith, Marvin Paulsen, Kent Rausch, Mike Tumbleson
Purdue University ............................................................................................................................. Richard Stroshine*
Klein Ileleji, Linda Mason
Iowa State University .................................................................................................................... Gretchen A Mosher*
Chard E. Hart, Charles Hurburgh, Jr., Angela Shaw
Kansas State University ..................................................................................................................... Kingsly Ambrose*
Subramanyam Bhadriraju, Dirk Maier, Tom Phillips, Praveen V. Vadlani
University of Kentucky .................................................................................................................... Michael Montross*
Sam McNeill
Michigan State University ..................................................................................................................... Perry K.W. Ng*
Mississippi State University .........................................................................................................................Haibo Yao*
University of Missouri ..................................................................................................................................Joe Parcell*
Montana State University .................................................................................................................. David K. Weaver*
University of Nebraska ............................................................................................................................... Devin Rose*
Pg. i - NC-213 – The U.S. Quality Grains Research Consortium
North Dakota State University................................................................................................................ Senay Semsek*
Clifford Hall, Kenneth Hellevang, Frank Manthey
The Ohio State University ................................................................................................................ F. William Ravlin*
Oklahoma State University ................................................................................................................... Brian D. Adam*
Patricia Rayas-Duarte, Carol Jones
Texas AgriLife Research ......................................................................................................................Tim J. Herrman*
Joseph Awika, Kyung M. Lee, Wei Li
University of Wisconsin .......................................................................................................... Sundaram Gunasekaran*
USDA, ARS, CGAHR, Manhattan, Kansas ............................................................................................. Mark Casada*
Paul Armstrong, Frank Arthur, Scott Bean, Thomas J. Herald
*Official Voting Representative. (Material on Participating Stations obtained from NIMSS Appendix E as of December 31, 2013.)
The Industry Advisory Committee
The NC-213 Industry Advisory Committee consists of at least five NC-213 stakeholder members recruited by and
voted on by the NC-213 Executive Committee to serve a two-year term each. This committee serves in an advisory
role to NC-213, its Executive Committee and its membership. In addition, the committee serves as a reviewer pool
for The Andersons Grant Review Committee, acts as a liaison between NC-213 researchers and the industry,
actively encourages existing industry stakeholders and recruits new industry stakeholders to participate in NC-213,
and provides active feedback regarding research agenda and results.
AgriGold Hybrids ............................................................................ Chuck Hill (Chair 2012 – Present) – 2010-Present
The Andersons, Inc. .......................................................................................................... Joe Needham – 2006-Present
Cargill ........................................................................................................................... Nick Friant – July 2007-Present
Foss Analytical AB...................................................................................................... Jan-Ake Persson – 2006-Present
Pioneer ............................................................................................................................. Morrie Bryant – 2012-Present
ROMER Labs, Inc. ......................................................................................................... Steve Nenonen – 2012-Present
Former committee members:
Consolidated Grain and Barge ................................................................................. James Stitzlein, Chair – 1997-2012
Illinois Crop Improvement .................................................................................................................... John McKinney
The Quaker Oats Company/PepsiCo .................................................................................................. A. Bruce Roskens
Pg. ii - NC-213 – The U.S. Quality Grains Research Consortium
Contents1
NC-213 Objective 1
To characterize quality attributes and develop systems to measure quality of cereals,
oilseeds, and bioprocess coproducts.
Diffusion and Production of Carbon Dioxide in Bulk Corn at Various Temperatures and Moisture Contents.
Danao, M.C., University of Illinois at Urbana ............................................................................................................... 1
Measurement, Documentation and Postharvest Processing for the Prevention of Postharvest Losses of
Soybeans and Corn.
Paulsen, M., University of Illinois at Urbana ................................................................................................................ 3
To Characterize Quality and Safety Attributes of Cereals, Oilseeds, and Processed Products, and to Develop
Related Measurement Systems.
Hurburgh, C.R., Iowa State University .......................................................................................................................... 6
Analysis of Masked Mycotoxins in Hard Red Spring Wheat.
Simsek, S., North Dakota State University .................................................................................................................... 8
Estimation of Physical Properties and Quality of Canola Seed using Non-Destructive Techniques.
Jones, C., Oklahoma State University ......................................................................................................................... 20
Elastic-plastic Deformation of Wheat Kernels: The Influence of Composition and Relationship with End
Properties.
Rayas-Duarte, P., Oklahoma State University ............................................................................................................. 21
Reduction of Mycotoxin Levels in Distillers Grains
Ileleji, K.E., Purdue University ................................................................................................................................... 22
Research Activity Funded by The Andersons, Inc. (Andersons Research Grant Program)
Improving Functionality of Wheat and Sorghum for Food Applications: Enhancing Process Efficiency and
Health Properties Targeting Tortillas and Flatbreads.
Awika, J., Texas A&M AgriLife Research ................................................................................................................. 30
Development of Surface-enhanced Raman Spectroscopy (SERS) and Liquid Chromatography-tandem Mass
Spectrometry (LC/MS-MS) methods for Rapid Detection of Mycotoxins in Food and Feed Matrices.
Lee, K.M., Texas A&M AgriLife Research ................................................................................................................ 33
Characterization of Sorghum Biomolecules and their Functionality and Relationships to Sorghum Utilization
and End-Use Qquality.
Bean, S.R., CGAHR, USDA-ARS, Manhattan, KS .................................................................................................... 38
Pg. iii - NC-213 – The U.S. Quality Grains Research Consortium
NC-213 Objective 2
To develop methods to maintain quality, capture value, and preserve food safety at key
points in the harvest to end product value chain.
Risk Assessment for the Food Safety Concerns of Mycotoxins in the Pacific Northwest under Climate
Variability.
Ryu, D., University of Idaho ....................................................................................................................................... 40
To Develop Efficient Operating and Management Systems that Maintain Quality, Capture Value, and
Preserve Food Safety in the Farm-to-User Supply Chain.
Mosher, G.A., Iowa State University........................................................................................................................... 41
Intrinsic Characteristics of Modified DDGS and Development of Effective Handling Strategies.
Ambrose, K., Kansas State University, Department of Grain Science and Industry ................................................... 43
Laboratory and Field Data for Pack-Factor Determination.
Montross, M., The University of Kentucky ................................................................................................................. 48
Hyperspectral Imaging Methodology to Measure Fungal Growth and Aflatoxin in Corn.
Yao, H., Geosystems Research Institute/ Mississippi Agricultural & Forestry Experiment Station
Mississippi State University ........................................................................................................................................ 52
Nanostructured Products for Management of Insects in Stored Grain.
Weaver, D., Montana State University ........................................................................................................................ 55
Identify Grain Attributes that Relate to Whole-wheat Pasta Quality.
Manthey, F.A., North Dakota State University ........................................................................................................... 57
Effectiveness and Profitability of Alternative Insect Control Strategies, and Evaluation of Alternative Supply
Chain Management and Traceability Technologies.
Adam, B., Oklahoma State University ........................................................................................................................ 58
Fungal Susceptibility Measurement using the Solvita Grain CO2 Respiration Test.
Stroshine, R.L., Purdue University .............................................................................................................................. 66
Developing New Stored Grain Pack Factors.
Casada, M., USDA-ARS-CGAHR, Manhattan, Kansas.............................................................................................. 67
Mechanistic Modeling of Grain Handling.
Casada, M., USDA-ARS-CGAHR, Manhattan, Kansas.............................................................................................. 69
Pg. iv - NC-213 – The U.S. Quality Grains Research Consortium
NC-213 Objective 3
To quantify and disseminate the impact of market-chain technologies on providing high
value, food-safe, and bio-secure grains for global markets and bioprocess industries.
To be a Multi-Institutional Framework for the Creation of Measurable Impacts Generated by Improvements
in the Supply Chain that Maintain Quality, Increase Value, and Protect Food Safety/Security.
Shepherd, H.E., Iowa State University ........................................................................................................................ 71
Creating Awareness on Grain Dust Explosion in Grain Handling and Processing Facilities through Worker
Training.
Ambrose, K., Kansas State University ........................................................................................................................ 75
GEAPS-KSU Grain Operations Distance Education and Professional Credentialing Program.
Maier, D.E., Professor & Head, Grain Science & Industry, Kansas State University ................................................. 77
1
Please note that some reports have more than one contributing institution and author. In the Contents, only the principal
investigator, along with their institution, is listed. Please refer to the individual report for a complete list.
Pg. v - NC-213 – The U.S. Quality Grains Research Consortium
NC-213 (The U.S. Quality Grains Research Consortium)
Objective 1
To characterize quality attributes and develop systems to
measure quality of cereals, oilseeds, and bioprocess
coproducts.
Objective 1
Title
Diffusion and Production of Carbon Dioxide in Bulk Corn at Various Temperatures and Moisture Contents.
By
Danao, M.C., University of Illinois at Urbana
Rausch, K.D.
Singh, V.
Outputs
The effective diffusion coefficient of carbon dioxide (CO2) through bulk corn was determined at various
temperatures (10, 20, and 30°C) and moisture contents (14.0, 18.8, and 22.2% w.b.). The diffusion coefficient
measurements were conducted using a diffusion cell surrounded by a water jacket, which was used to control the
bulk corn temperature in the diffusion cell. A source term (CO2 respiration rate) was introduced in the diffusion
equation to account for CO2 production by corn during the diffusion process. Corn respiration rate increased when
temperature and grain moisture content increased. As respiration rate increased, it had a larger effect on the diffusion
pattern when measuring the effective CO2 diffusion coefficient. The effective CO2 diffusion coefficients through
bulk corn ranged between 3.10 x 10-6 and 3.93 x 10-6 m2/s, depending on temperature and moisture conditions. As
temperature increased from 10 to 30°C, the effective CO2 diffusion coefficient through bulk corn increased from
3.21 x 10-6 to 3.76 x 10-6 m2/s. As corn moisture content increased from 14.0 to 18.8% (w.b.), the effective CO2
diffusion coefficient through bulk corn decreased from 3.59 x 10-6 to 3.39 x 10-6 m2/s, respectively. There was no
difference observed in the effective CO2 diffusion coefficient when corn moisture content increased from 18.8 to
22.2%.
Outcomes/Impacts
Early detection of grain spoilage will reduce grain quantity and quality losses, decrease mycotoxin production in the
food chain, and avoid financial loss by applying timely storage management, such as aeration. It has been reported
that monitoring CO2 concentration in the headspace of the storage bin with a CO2 sensor can lead to earlier
detections of grain spoilage compared to temperature monitoring. CO2 monitoring in bulk grain in silo bags is even
more important since it is in indicator of whether hermetic conditions are being maintained. In order to further
develop effective and commercially feasible techniques for utilizing CO2 sensors for grain quality monitoring in
storage bins and silo bags, knowledge of movement of CO2 in bulk grain is necessary. This study provides values of
CO2 diffusivity through bulk corn at different temperatures and grain moisture contents, including estimates of CO2
production by corn respiration.
Pg. 1 - NC-213 – “The U.S. Quality Grains Research Consortium”
Objective 1
Publications
Huang, H., M.C. Danao, K.D. Rausch, and V. Singh. 2013. Diffusion and production of carbon dioxide in bulk corn
at various temperatures and moisture contents. J. Stored Prod. Res. 55:21-26.
Huang, H., M.C. Danao, K.D. Rausch, and V. Singh. 2013. Diffusion and production of carbon dioxide in bulk corn
at various temperatures and moisture contents. Lecture. ASABE Annual International Meeting, Kansas
City, MO. Paper No. 131619395.
Funding Source(s) and Amount(s)
ADM Institute for the Prevention of Postharvest Loss.
Contacts
Mary-Grace C. Danao; 376B Agricultural Engineering Sciences Bldg., 1304 W. Pennsylvania Ave., Urbana, IL
61801; 217-244-3925; e-mail: [email protected]
Objective 1
Title
Measurement, Documentation and Postharvest Processing for the Prevention of Postharvest Losses of Soybeans and
Corn.
By
Paulsen, M., University of Illinois at Urbana
Danao, M.C.
Rausch, K.D.
Singh, V.
Outputs
The ADM Institute for the Prevention of Postharvest Loss has the goal of reducing postharvest losses of grains and
oilseeds in many parts of the world with focus on India and Brazil. Harvest losses from eight combines in soybeans
and eleven combines in corn were measured in Goias and Mato Grosso states of Brazil in February and June 2012,
respectively. Work was done in cooperation with Universidade Federal de Vicosa, Universidade Federal de Goias,
and Universidade Federal de Mato Grosso. Loss measurements followed the Embrapa method of using 2.0 m2 for
loose kernels and 30 m2 for ears over the full width of the combine header. Yield estimates, and measurements for
total and header losses were replicated three times for each combine.
Soybean harvest moistures ranged from 11.0 to 19.5%. Estimated soybean yields at 13% moisture ranged from
2663 to 4861 kg ha-1. Pre-harvest losses ranged from 1.0 to 13.6 kg ha-1., Table 1. Total soybean combine losses
ranged from 47.4 to 260.5 kg ha-1 (1.2 to 5.5% of yield). All of the combines tested were operating at loss levels
higher than the Embrapa acceptable loss for soybeans of 0.75 bags (45 kg) ha-1. The headers were the largest
contributors to losses with 31 to 247 kg ha-1. Of the four contributors to header losses, loose stalk and lodged stalk
losses were negligible. Shatter losses were the primary contributor with 9 to 216 kg ha-1 and shatter losses had a
lower coefficient of variation in measurement than stubble losses. Shatter losses increased markedly as harvest
moistures decreased below 13%. Stubble losses ranged from 1.4 to 37 kg ha-1. A reduction in ground speed would
have enabled soybean cutter bars to run closer to the ground and further reduce stubble losses. An achievable
reduction in combine harvest losses of 2 bags (120 kg) ha-1 has an operator hourly value of U.S. $238 to 277 h-1.
Corn harvest moistures ranged from 16.0 to 30.6%. Corn yield at 14% moisture ranged from 6937 to 11,044 kg ha-1.
Pre-harvest ear losses ranged from 0 to 42 kg ha-1. Total corn combine losses ranged from 36.2 to 320.6 kg ha-1 (0.3
to 3.6% of yield), Table 2. Of this loss, header ear loss accounted for the largest portion with 0 to 237 kg ha-1.
Header/ separator kernel losses ranged from 34.4 to 120.0 kg ha-1. The highest header/ separator kernel losses could
have likely been reduced by driving slower. Rotor/ cylinder kernel losses ranged from 0 to 11.4 kg ha-1 and were
low, less than 0.1% of estimated yield. Header loose kernel losses ranged from 5.3 to 59.7 kg ha-1. The combine
operating in lodged corn had the highest header loose kernel losses, likely from lodged ears that were partially
shelled by the header. The second highest header loose kernel loss was from the combine with the highest ground
speed. Of the four combines with header loose kernel losses below 8.1 kg ha-1 all operated at speeds of 5.7 km h-1 or
less. Reduction in ground speed would benefit separation losses in corn. Not surprisingly, lodged corn increases
header ear losses by more than any other source of loss.
Objective 1
Table 1. Soybean combine harvest speed, rotor speed, splits, and combine losses adjusted to 13.0% moisture,
average of three replications (from Paulsen et al., 2013)
PreCombine
Combine
Rotor, Splits,
Total
Combine
Combine
Combine
harvest
speed,
rpm
%
loss,
loss,
loss,
loss,
loss,
km/ h
kg/ ha
kg/ ha
bags/ ha
% of yield
kg/ ha
S1
5.5
525
0.57
135.1
121.6
2.0
3.4
13.6
S2
4
550
1.10
151.0
1.8
149.1
2.5
3.5
S3
4.5
560
0.73
154.6
1.0
153.5
2.6
3.2
S4
4.5
610
2.25
217.1
5.6
211.6
3.5
4.4
S5
6.5
720
12.07
265.9
5.4
260.5
4.3
5.5
S6
5.5
720
0.58
50.1
2.7
47.4
0.8
1.2
S7
4.2
640
1.10
73.1
2.4
70.7
1.2
2.1
61.0
1.0
2.3
S8*
6
700
1.30
61.0
N/A
*Combine S8 could not be replicated three times due to sudden rainfall.
Table 2. Corn combine harvest speed and losses adjusted to 14.0% moisture, average of three replications. Total
combine loss = header ear loss + header/ separator kernel loss + rotor/ cylinder loss (from Paulsen et al., 2013).
Combine
ComTotal
Header
Header/
Rotor/
Header
Total
bine
ear loss,
ear loss,
separator
cylinder
loose kernel combine loss,
speed,
kernel loss,
kernel loss,
loss,
km/hr
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
C1
6.2
116.3
114.0
57.5
1.0
26.2
172.5
C2
5.0
28.9
28.9
36.8
3.2
22.1
68.9
C3
5.5
125.0
125.0
45.3
0.3
18.6
170.6
C4
5.5
0.0
0.0
34.4
6.8
6.7
41.3
C5
5.5
16.9
16.9
46.5
11.4
5.3
74.8
C6
4.5
0.0
0.0
35.7
0.4
8.1
36.2
C7
8.5
55.3
33.2
41.2
0.0
24.9
74.4
C8
C9
(lodged)
C10
8.9
38.4
N/A
120.0
0.0
27.2
116.2
6.0
6.5
254.6
68.3
237.1
68.3
83.4
33.5
0.0
0.0
59.7
14.6
320.6
101.8
C11
5.7
25.0
25.0
35.8
0.0
6.3
60.7
Objective 1
Outcomes/Impacts
Postharvest losses of grains, oilseeds, and pulses worldwide are higher than desired, with reports of up to 10 to 20%
in some countries. Such losses involve the entire food chain from harvest, gathering, drying, storage, transport, and
end use processing. Measurement and assessment of post-harvest losses is a necessary first step to affect a reduction
in post-harvest losses. Increasing world food supply by reducing post-harvest losses, rather than by increasing
production, makes a greater savings because all of the input costs used to produce saved grains are also captured.
Publications
Paulsen, M.R., Pinto A.C.F., D.G. de Sena Jr., R. Zandonadi, S. Ruffato, A.G. Costa, V.A. Ragagnin, and M-G.C.
Danao. Measurement of combine losses for corn and soybeans in Brazil. ASABE Paper No. 1570965,
presented at 2013 ASABE Annual International Meeting, Kansas City, MO Jul, 2013.
ADM Institute for the Prevention of Postharvest Loss.
Contacts
Marvin R. Paulsen; 338 Agricultural Engineering Sciences Bldg., 1304 W. Pennsylvania Ave., Urbana, IL 61801;
217-333-7926; e-mail: [email protected]
Objective 1
Title
To Characterize Quality and Safety Attributes of Cereals, Oilseeds, and Processed Products, and to Develop Related
Measurement Systems.
By
Hurburgh, C.R., Iowa State University
Rippke, G.
Hardy, C.N. (Graduate student.)
Bowers, E.
Outputs
Evaluation of NIRS instrumentation continued. Nine models of transmission analyzers and 9 models of reflectance
analyzers are now in our program. Corn, soybeans, soybean meal, distillers grains, and bakery meal are now
included in our databases. In 2013, Zeiss Corona X, JDSU MicroNIR, Perten Inframatic 9500, Bruins AgriCheck X,
and Thermo Antaris were calibrated for proximate analysis of corn, soybeans, wheat, DDGS, soybean meal and
bakery meal. The sample set optimization procedure was applied to the creation of smaller calibration sets for more
rapid development of useable calibrations.
The Grain Quality Lab set up a mycotoxin analysis service, using aflatoxin from the 2012 as the source of
development samples. As expected, our results from the 304 2012-crop samples collected by the Iowa Department
of Agriculture (IDALS) were close to the average of the original IDALS results. Also as expected, there was a 50%
coefficient of variation across the labs.
Outcomes/Impacts
Industry users of NIRS that used our assistance increased their operating efficiency, and quickly recouped the cost
of the NIRS units/calibrations in discounts or improvements in ingredient quality for suppliers.
Public availability of samples and calibration support has increased the pace of NIRS instrument development, to the
benefit of users and vendors alike.
Programs to manage mycotoxins in high volume situations, such as the US Corn Belt, must be designed to
progressively mitigate the inevitable variability of testing at any one sampling point.
Publications
C.L. Hardy. Developing a near infrared based quality control program for inbound ingredients at feed mills.
Presentation given at the NC-213 Annual Meeting, February, 2013.
N. Cao. Accuracy at reduced cost for near infrared measurements of grain quality. Presentation given at the NC-213
Annual Meeting, February, 2013
N. Cao. Calibration optimization and efficiency in near infrared spectroscopy. Doctoral dissertation. Iowa State
University, Department of Agricultural and Biosystems Engineering. May, 2013.
Objective 1
Esteve Agelet, Lidia, Paul R. Armstrong, Jasper G. Tallada, Charles R. Hurburgh. 2013. Discrimination of
Conventional and Roundup Ready Soybean Seeds. Differences between conventional and glyphosate
tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy. Food
Chemistry 141 (2013) 1895–190.
Cao, N., Rippke, G., Hurburgh, C. R. 2013. Effect of calibration subsets on standardization in NIR spectroscopy.
Oral Presentation for the 16th International Conference on Near Infrared Spectroscopy, La Grande-Motte
(France), Jun. 2-7, 2013.
Hurburgh, C. R. Economic considerations of NIR Spectroscopy. Oral Presentation for the 16th International
Conference on Near Infrared Spectroscopy, La Grande-Motte (France), Jun. 2-7, 2013.
Lidia Esteve Agelet and Charles R. Hurburgh. 2013. Limitations and Current Applications of Near Infrared
Spectroscopy for Single Seed Analysis. Talanta. (acc).
Awarded Grant(s) and Contract(s)
Iowa Extension 21 Program.
Various Industry contracts and service fees.
The United Soybean Board.
United States Department of Agriculture – Agriculture Research Service (2 programs).
Objective 1
Title
Analysis of Masked Mycotoxins in Hard Red Spring Wheat.
By
Simsek, S., North Dakota State University
Mohammed, M.M.
Anderson, J.
Outputs
Fusarium head blight (FHB) is a major fungal disease affecting several gramineous hosts, including wheat (Triticum
aestivum L.). Hard Red Spring (HRS) wheats of the Northern Great Plains of the United States and Western
Provinces of Canada are susceptible to scab, especially in years that have wetter than average growing seasons.
FHB infection often results in the production of several trichothecene mycotoxins including deoxynivalenol (DON)
and nivalenol (NIV), as well as, zearalenone (ZEA) and moniliformin (MON), all of which have a range of toxicity
to animals. DON is the most common mycotoxin produced by Fusarium. Plants are able to “detoxify” mycotoxins
such as DON by chemically modifying and/or including them in the plant matrix. These modified versions of the
toxins are called “bound mycotoxins”, also known as masked deoxynivalenol. One of the most common forms of
bound DON is DON- 3- glucoside. In this form, a glucose molecule has been attached to the DON molecule at
carbon 3. Recent studies have shown that masked DON in wheat is a cause for concern, and escapes detection by
routine analytical methods. The evidence that suggests masked DON may be released into the free form under some
food processing conditions, through enzymolysis in dough processing or in digestion, raises concerns that the
potential toxicity of samples is being under estimated.
This research is aimed at: 1) determining the prevalence of bound DON in commercial samples of wheat and 2)
determining bound DON and native DON in Fusarium graminearum infected wheat samples.
Materials and Methods:
For objective 1, the hard red spring (HRS) wheat from 2011 and 2012 HRS wheat crop survey samples were used as
raw material. A total of 441 and 436 samples were selected as wheat grader samples from the 2011 and 2012 HRS
wheat crop surveys, respectively, and used in this study. The samples were collected based on production data
obtained from the National Agricultural Statistics Service for the 16 regions in the 4 state HRS wheat growing
regions (Figure 1.1). The Montana (MT), North Dakota (ND), South Dakota (SD) and Minnesota (MN) state office
of the National Agricultural Statistics Service obtained wheat samples during harvest directly from growers either in
the fields or farm bins and local elevators. Samples from the 2011 Crop Survey represented a high FHB infection
and incidence of DON, while 2012 Crop Survey samples represented wheat with low FHB infection.
For objective 2, Different wheat lines ranging from moderately susceptible to susceptible to Fusarium Head Blight
(FHB) were analyzed. All lines were grown under two field screening during 2008, 2009 and 2010 in two locations
of Minnesota, USA. At the St. Paul location (StP), F. graminearun macronidia was applied by backpack sprayer at
the rate of 60 mL of a 100,000 conidia/mL per 2.4 m row at anthesis and 3-4 days later. At the Crookston location
(Crk), grain spawn inoculum was spread at the rate of 56 kg/ha at the jointing stage and a second application one
week later. Both nurseries were misted periodically overnight to maintain high humidity environments. The samples
were ground and conserved under refrigeration until their analysis.
The kernel quality based on non-grading factors consisted of the determination of the protein content (expressed in
12% moisture basis, Method 39.10.01), falling number expressed in seconds (Method 56.81.03), both approved
Objective 1
methods of the AACC [19] and thousand kernel weight determined on a 10 g sample of cleaned wheat (free of
foreign material and broken kernels) counted by electronic seed counter.
The wheat grade and class of the samples was determined by a licensed grain inspector for the Official United States
Standards for Grain. North Dakota Grain Inspection Service, Fargo, ND, provided grades for composite wheat
samples. The final grade of the samples was based on dockage (elimination of all material other than wheat),
shrunken and broken kernels and percent damaged kernels, test weight measured as pounds per bushel (lb/bu)
(Method 55-10, AACC) and percent vitreous kernels (percentage of kernels having vitreous endosperm), as well as
the summation of these defects referred to as total defects using an official procedure of USDA (United Stated
Department of Agriculture).
Free DON was determined using the procedure of Tacke and Casper (1996) with some modifications (Simsek et al
2012) to extract and derivatize the DON for analysis by GC-ECD, which is available in Dr. Simsek’s laboratories.
Free DON and bound DON will be determined using a liquid chromatrography – quadrapol-time-of-flight (LCQTOF) system, which is available in Dr. Simsek’s laboratories. The extraction of DON and bound DON for LCQTOF analysis will also be done according to the method of Tacke and Casper (1996). However, after extraction the
samples will be directly analyzed with the LC-QTOF, without further sample preparation. Samples for objective 1
were obtained from a regional wheat quality survey, while those for objective 2 were obtained from the North
American Wheat Scab Evaluation Nursery (collaboration with Dr. Jim Anderson and Mohamed Mergoum).
For objective 1, analysis of variance (ANOVA) was performed for individual years using the ‘MIXED’ procedure in
SAS (V 9.2, SAS Institute Inc., Cary, NC). The model for ANOVA was a nested fixed model in which region was
nested in state and city was nested in region. Least square mean values were estimated using the ‘LSMEAN’ option.
Correlation and regression was performed using ‘CORR’ and ‘GLM’ procedures in SAS, respectively.
For objective 2, analysis of variance (ANOVA) was performed individually for three year data. The ‘GLM’
procedure in SAS (V 9.2, SAS Institute Inc., Cary, NC) was used for ANOVA in which wheat line and location
were considered as fixed effects. The main effects of wheat line and location and their interaction were tested for
significance using the residual error terms. Correlation and regression was performed using ‘CORR’ and ‘GLM’
procedures in SAS, respectively.
Additional to these activities, PI oversees special projects for the ND Wheat Commission and US Wheat Associates.
These projects are the Annual Crop Quality Survey, Overseas Varietal Analysis Project and Export Cargo Sampling,
which are designed to help market the spring wheat crop and continue to improve the quality of spring wheat that is
produced. The data produced are used by USDA-Agricultural Statistics Service and many researchers from the
Agribusiness and Applied Economics Department, extension appointment researchers and many other staff who deal
with spring wheat.
PI also provides the primary quality oversight for the NDSU spring wheat breeding program lead by Dr. Mergoum.
Thousands of lines are crossed on an annual basis to obtain high quality and yielding, and competitive varieties for
the producers. PI has been invited to speak and/or present demonstrations to visiting trade teams or groups that are
sponsored by the ND Wheat Commission and/or the Northern Crops Institute.
Outcomes/Impacts
Objective 1
In this study, DON and D3G were measured using gas chromatographic (GC) and liquid chromatography-mass
spectrometry (LC/MS) in wheat samples collected during 2011 and 2012 in USA. Results indicate that the growing
Objective 1
region had a significant effect on the DON and D3G (P<0.0001). There was a positive correlation between both
methods (GC and LC-MS) used for determination of DON content. DON showed a significant and positive
correlation with D3G during 2011. Overall, DON production had an effect on D3G content and kernel damage, and
was dependent on environmental conditions during Fusarium infection.
The HRS wheat kernel quality of the 2011 and 2012 crop surveys is presented in Table 1.1. The values of percent
damage and percent total defects in 2011 crop survey were higher that 2012 crop survey. According to the HRS
wheat 2012 Regional Quality Report, there were differences in the environmental factors during planting, growing
and harvest of crops of both years.
Least square means values of DON analyzed with GC and LC-MS, D3G determined with LC-MS, and percent
damage kernels are given in Table 1.2. These variables showed significant differences between state and region
mean values, indicating that HRS wheat samples collected from different state or regions might have different levels
of DON and D3G. Samples collected from ND presented higher values of DON (both GC and LC-MS methods),
D3G, and percent damaged kernels than other states in both years. Samples from MT presented the opposite trend.
The analysis of variance (ANOVA) indicates that state and region had significant effects on variation in DON and
D3G content and percent damaged kernels in 2011 survey samples (Table 1.3). The ANOVA for 2012 survey
samples showed slightly different results (Table 1.3). During 2012 the percent damaged kernels was not affected by
the state, region or their interaction. The ANOVA on DON and D3G for both years indicates that the wheat growing
environment affects greatly the variations in DON and D3G content.
The correlation between GC and LC-MS methods used to analyze DON during 2011 and 2012 is shown in Figure
1.2a, b. The high coefficient of determination (R2) indicates strong, positive and significant correlation between
both methods in both years (Figure 1.2). When the GC and LC-MS methods for both survey 2011 and 2012 were
compared (Figure 1.2c), an R2 of 0.947 and mean square error (MSE) of 0.90 were found. To identify relationships
between mycotoxin contents and percent damaged kernels, linear and rank correlation coefficients were estimated
and given in Table 1.4. The DON values determined GC and LC methods also showed very high and positive
correlation for 2011 and 2012 data, individually (Table 1.4). These results mean that the LC method is as precise as
or better in evaluating DON concentration of HRS wheat lines when compared to the GC method.
The correlation between DON and D3G (Figure 1.3) in survey samples from 2011 and 2012 was significant with a
moderate R2=0.521. This means that the D3G production is related positively to the DON content and increasing
DON levels also increase the D3G level in wheat. The correlation among DON and D3G with damage kernel during
2011 and 2012 is given in Table 1.4 and Figure1. 4. The percent damaged kernels had very highly significant
correlation with GC-DON and LC-DON (P<0.001) in 2011; damage also had a very highly significant correlation at
P<0.01 with DON (GC and LC) in 2012. The positive correlations indicate that samples which were rated to have
higher percent damaged kernels had higher levels of DON in the sample. This was also shown in Figure 1.4a-b,
where the scatter plot between GC-DON and damage was depicted. D3G also had a significant (P<0.05) correlation
with percent damaged kernels in 2011 and 2012. This indicates that the D3G contributes to the damage in wheat
although this “masked” mycotoxin is a product of the plant defense mechanisms during the infection with Fusarium.
The results indicated that DON levels varied with the survey crop year and they have a relationship with the kernel
quality and D3G detected in wheat. Also, it was found that the growing state cause a larger effect on DON and D3G
but not on percent damaged kernels. The D3G levels were significantly correlated with the percent damaged kernels,
but at lower levels than the DON content. DON infection in wheat caused more effect on the kernel quality between
years analyzed. Otherwise, the ANOVA and correlation coefficient indicate that both GC and LC-MS can be used to
determine DON in HRS. However, due to the ease of the method (sample can be extracted and analyzed without
derivitization) and simultaneous determination of the D3G, LC-MS is more advantageous.
Objective 1
Objective 2
The aim of this work was to analyze DON and D3G content in different inoculated near-isogenic wheat lines grown
at two locations in Minnesota, USA during three different years. Regression analysis showed positive correlation
between DON content measured with LC and GC among wheat lines, locality and year.
The data obtained showed correlation between GC-DON, LC-DON and D3G. The analysis showed that there was a
significant correlation (R2=0.956, P<0.001) between both methods used to determine DON content in wheat (Figure
2.1). The same trend was observed in Hard Red Spring wheat of 2011 and 2012 Crop survey samples from Montana,
North Dakota, South Dakota and Minnesota, USA. So, this result indicates that it could be possible to use both LC
and GC, and get accurate results between them. However, the use of LC-MS is more convenient because of the lack
of derivitization step in sample preparation. Another advantage of the LC-MS is that it was possible to determine the
D3G content in wheat simultaneously with the DON determination. The correlation between these parameters is
shown in Figure 2. The coefficient of determination was moderate and significant (R2= 0.872). The equation model
obtained with this R2 value was a second-order curve. The D3G content rose as the DON content increased in
samples with DON content between 0-30 ppm.
Table 2.1 shows the means of DON (GC and LC) and D3G content of wheat lines grown in Minnesota collected
during 2008, 2009 and 2010. The values for 2008 ranged from 0.7-33.1, 0.1-33.9 ppm and 0.1-1.9 ppm for GCDON, LC-DON and D3G, respectively. Overall, the mycotoxin contents for 2009 were lower and ranged from 0.526.5 ppm, 0.0-23.6 ppm and 0.0-3.0 ppm, for GC-DON, LC-DON and D3G, respectively. The analysis of variance
(ANOVA) for DON and D3G in the samples for individual years is showed in Table 2.2. During 2008, DON (GC
and LC) and D3G contents were not statistically related to the main effects (Line and Location (Loc)) or their
interaction (Line x Loc). In 2009, it was observed that Loc and the interaction between Line x Loc on GC-DON
showed significant differences; while the Line and Loc on LC-DON were statistically significant. This means that
between both methods of DON determination, growing location has the main effect on DON content among
samples. Concerning the D3G, during 2009 only the Loc had a significant effect. On the other hand, during 2010,
the main effects were significantly related to the DON (both GC and LC) and D3G content in the samples, whereas
the interaction between factors was not significant. These results indicated that genetic and environmental conditions
play an important role in the DON and D3G production in 2010.
The Pearson and Spearman’s correlations were used to determine the correlation between DON and D3G in wheat
grown at two localities of Minnesota, and are shown in Table 2.3. During 2008 the ANOVA did not show any
significant effect of the Loc between these two parameters. However, the Spearman’s correlation showed positive
correlation coefficients with significant levels (P< 0.05, 0.01 and 0.001) among DON (GC and LC) and D3G from
Crookston and St. Paul (Table 2.3). In 2008 for samples grown at Crookston and St. Paul, the GC-DON showed a
very highly significant (P<0.001) correlation with LC-DON and D3G. In 2009, a significant correlation among GCDON and LC-DON between localities was seen (Table 2.3). With respect to D3G, the Pearson correlation indicated
that there were no significant correlation with GC-DON and LC-DON from Saint Paul. However, the Spearman
correlation found a coefficient correlation of 0.57 (P<0.001) and 0.44 (P<0.01) for GC-DON and LC-DON,
respectively. This may be related to the trend (second order curve) observed among the DON and D3G content
among localities and years of study obtained in Figure 2.2. The low significance level could be due to the different
kind of inoculum used to infect the wheat lines, differences in the growth stage development of the plant when the
inoculum was applied and the differences in the weather conditions between Crookston and St. Paul during the three
years of study.
In conclusion, there was a positive and high correlation between GC and LC methods for DON determination
among year, locality and wheat line. The relationship between DON and D3G fit a second order curve, indicating
that the tolerance of the wheat lines to the Fusarium infection has a relationship to the ability of the wheat line to
convert the DON to D3G during the detoxification process. Also, the most important factor affecting the DON and
Objective 1
D3G formation is the locality, which may be due to differences in gene expression of the wheat line in different
environmental conditions and its response to different inoculum and development stage of the wheat during the
inoculation process.
According to the Council for Agricultural Science and Technology the annual cost to the United States because of
the DON corruption of food crops is $637 million in 2003. In another research, direct losses to wheat producers in
United States owing to Fusarium Head Blight is approximated as about $260 million in a year and total economic
losses for all small grains in period of 1998-2000 is $ 2.7 billion.
Fig 1.1. Distribution of hard red spring (HRS) wheat samples from the 2011 and 2012 Crop Surveys from Montana,
North Dakota, Minnesota and South Dakota. A, B, C, D, E and F: regions in which the samples were collected from
each state. The numbers inside the parenthesis represent the number of samples taken, from left to right: 2011 Crop
Survey and 2012 Crop Survey
Table 1.1. Mean, standard deviation (SD), minimum (MIN) and maximum (MAX) values for wheat kernel quality
characteristics for 2011 and 2012 crop survey samples.
Factors Dockage (%)a Shrunken & brokena Damage (%)a DHV (%)a a
Test weight (lbs/bu) a
Total defects (%) Protein (12% mb)b b
Falling # (sec) b
1000 KWT (g) 2011 2012 Mean SD MIN MAX Mean SD MIN MAX 1.1 1.6 0.5 75.8 60.0 2.1 14.8 386.3 26.7 1.4 1.4 0.8 21.9 2.4 1.7 1.3 44.1 4.1 0.0 0.0 0.0 5.0 52.5 0.1 10.2 226.0 16.8 13.4 10.0 10.6 99.0 65.7 13.1 18.6 621.0 40.0 1.1 1.2 0.1 74.5 60.9 1.3 14.6 429.3 29.1 1.2 1.1 0.2 28.4 1.9 1.1 1.4 48.8 4.1 0.0 0.0 0.0 2.0 53.9 0.0 10.3 238.0 16.6 13.3 10.1 2.8 99.0 65.1 10.1 20.1 654.0 44.1 a) Grading factors used to evaluate the kernel quality to ensure general standards of acceptance in flour or
semolina production. b) Non-grade factors used to evaluate the kernel quality. mb= moisture basis.
Objective 1
Table 1.2. Least square mean values for DON, D3G and damage kernel in 2011 and 2012 survey.
2011 2012 State GC‐DON LC‐DON D3G MN MT ND SD Region MN‐A MN‐B MT‐B ND‐A ND‐B ND‐C ND‐D ND‐E ND‐F SD‐B 1.35 ** 0.03 2.80*** 1.35** 1.07 1.63* 0.00 3.89*** 3.77*** 2.91*** 1.08 2.19** 3.00** 2.41*** 1.74** 0.03 3.15*** 1.72* 1.21 2.27** 0.00 4.26*** 4.32*** 3.36*** 1.10 2.54** 3.34*** 3.20*** 0.04 0.00 0.24*** 0.12 0.01 0.06 0.00 0.31*** 0.30*** 0.30*** 0.02 0.09 0.39*** 0.29** Damage (%) 0.35** 0.03 0.63*** 0.36* 0.23 0.47* 0.05 0.75*** 0.69*** 0.61*** 0.61*** 0.66*** 0.49** 0.57** GC‐DON LC‐DON D3G Damage (%) 0.89 0.18 1.93*** 0.37 0.82 0.96 0.04 6.91*** 2.48*** 0.66 0.34 0.71 0.47 0.22 0.78 0.18 1.71*** 0.33 0.72 0.84 0.05 5.97*** 2.22*** 0.60 0.35 0.72 0.38 0.23 0.128** 0.090 0.142*** 0.085 0.124* 0.133 0.176** 0.295*** 0.174*** 0.060 0.094 0.096 0.133 0.097 0.05 0.00 0.06*** 0.03 0.08* 0.02 0.00 0.15*** 0.12*** 0.06 0.01 0.02 0.01 0.00 *, **, and *** means significant differences (H0: least square mean=0) at P˂0.05, 0.01, and 0.001, respectively.
Regions with no significant correlation are not shown. Values of DON and D3G are in ppm (parts per million).
Table 1.3. Mean square values of state (ST), region (Rga) and county (CTY) on DON, D3G and damaged kernel
in 2011 and 2012 survey.
Source Year 2011 ST Rga (ST) CTY (Rga*ST) Residual Year 2012 ST Rga (ST) CTY (Rga*ST) Residual Degrees of Freedom 3 12 100 320 3 12 100 320 Mean Square GC‐DON 221.3*** 28.0** 8.9 9.7 151.2*** 104.3*** 14.5 12.0 LC‐DON 282.8*** 40.4*** 13.2 14.0 111.2*** 72.8*** 11.3 10.9 D3G 2.15*** 0.43* 0.19 0.24 0.15*** 0.12 0.07 0.07 *, **, and *** means F values are significant at P˂0.05, 0.01, and 0.001, respectively.
Damage 10.19*** 0.45 0.44 0.70 0.22** 0.12*** 0.04 0.04 Objective 1
Fig 1.2. Correlation GC-DON and LC-MS DON content values. a) 2011 survey samples; b) 2012 survey samples
and c) 2011 and 2012 survey samples combined. *** Significantly different from 1 at P<0.001.
Table 1-4. Pearson linear and Spearman rank correlation coefficients between DON, D3G and damage kernel for
regions
Variables 2011 2012 GC‐DON LC‐DON D3G Damage GC‐DON LC‐DON D3G Damage 2011 GC‐
LC‐DON D3G DON Linear correlation ‐ 0.99*** 0.91*** 0.99*** ‐ 0.91*** 0.94*** 0.92*** ‐ 0.89*** 0.89*** 0.87*** 0.71** 0.73** 0.53* 0.72** 0.74** 0.53* 0.42NS 0.39NS 0.42NS 0.61* 0.62* 0.58* Rank correlation Damage 2012 GC‐
DON LC‐DON D3G Damage 0.91*** 0.91*** 0.74** ‐ 0.62* 0.66** 0.34NS 0.57* 0.63** 0.59* 0.47NS 0.52* ‐ 0.99*** 0.41NS 0.72** 0.64** 0.59* 0.47NS 0.53* 1.00*** ‐ 0.37NS 0.69** 0.53* 0.50NS 0.41NS 0.40NS 0.83*** 0.83*** ‐ 0.28NS 0.68** 0.65** 0.47NS 0.54* 0.79*** 0.79*** 0.60* ‐ *, **, and *** means correlation coefficient is significant at P˂0.05, 0.01, and 0.001, respectively. NS: Not
significant (P≥0.05).
Objective 1
Fig 1.3. Correlation between DON and D3G levels in survey samples from 2011 and 2012; where ***, and *
indicate that regression coefficients are significant at P<0.001 and P<0.05, respectively
Fig 1.4. Correlation between GC-DON and damage levels in survey samples from a) 2011 and b) 2012.
Figure 2.1. Correlation between GC-DON and LC-DON (combined 2008.2009 and 2010). *** Significantly
different from 1 at P<0.001.
Objective 1
Table 2.1. Means of GC-DON, LC-DON and D3G of wheat samples collected during 2008-2010 in Crookston, St.
Paul and Minnesota (MN).
Year 2008 Location Crookston St. Paul MN 2009 Crookston St. Paul MN 2010 Crookston St. Paul MN a
Min (n=22) Max (n=22) Average (n=22) Min (n=22) Max (n=22) Average (n=22) Min (n=44) Max (n=44) Average (n=44) Min (n=35) Max (n=35) Average (n=35) Min (n=35) Max (n=35) Average (n=35) Min (n=70) Max (n=70) Average (n=70) Min (n=88) Max (n=88) Average (n=88) Min (n=90) Max (n=90) Average (n=90) Min (n=178) Max (n=178) Average (n=178) GC‐DONa 0.7 21.4 7.2 0.7 33.1 9.8 0.7 33.1 8.5 5.0 25.4 13.7 0.5 26.5 5.4 0.5 26.5 9.5 1.9 19.5 7.4 0.1 12.6 4.3 0.1 19.5 5.9 LC‐DONa 0.1 24.2 5.7 0.7 39.5 11.1 0.1 39.5 8.4 0.0 25.7 11.9 0.2 21.0 4.8 0.0 25.7 8.3 1.7 20.2 7.9 0.2 11.5 4.2 0.2 20.2 6.1 D3Ga 0.3 1.8 1.1 0.1 1.9 0.9 0.1 1.9 1.0 0.0 3.8 2.1 0.0 1.5 0.5 0.0 3.8 1.3 0.4 2.6 1.3 0.0 1.5 0.5 0.0 2.6 0.9 in ppm (parts per million); n= number of lines in each set.
Objective 1
Table 2.2. ANOVA table for DON and D3G of wheat samples for 2008-2010
Year Traits Source DF 2008 2009 2010 GC‐DON LC‐DON D3G GC‐DON LC‐DON D3G GC‐DON LC‐DON D3G Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error Line Loc Line*Loc Error 21 1 21 12 21 1 21 12 21 1 21 12 34 1 34 72 34 1 34 72 34 1 34 72 89 1 87 88 89 1 87 88 89 1 87 88 Sum of Squares 2787.7 96.6 994.0 235.6 3369.6 418.7 1169.0 191.7 12.7 0.9 3.3 0.4 5749.3 1374.3 1612.9 1068.5 5314.4 1013.4 1004.5 1639.7 27.9 49.3 11.2 9.0 3472.5 441.7 338.4 294.6 3479.0 650.1 426.8 419.0 48.1 33.1 11.6 11.8 Mean square 132.7 96.6 47.3 19.6 160.5 418.7 55.7 16.0 0.60 0.88 0.16 0.03 169.1 1374.3 1612.9 1068.5 156.3 1013.4 29.5 22.8 0.82 49.28 0.33 0.13 39.0 441.7 3.9 3.3 39.1 650.1 4.9 4.8 0.54 33.09 0.13 0.13 F value Pr > F 2.8 2.0 2.4 2.9 7.5 3.5 3.8 5.6 4.7 3.6 29.0 3.2 5.3 34.3 1.3 2.5 149.0 2.6 10.0 113.6 1.2 8.0 132.5 1.0 4.0 247.4 1.0 0.0111 0.1678 0.059 0.0095 0.0122 0.015 0.0016 0.0275 0.004 0.0002 < 0.0001 < 0.0001 < 0.001 < 0.001 0.177 0.0048 < 0.0001 0.000 < 0.0001 < 0.001 0.242 < 0.0001 < 0.0001 0.497 < 0.0001 < 0.0001 0.497 Pg. 17 - NC-213 – “The U.S. Quality Grains Research Consortium”
Objective 1
Table 2.3. Pearson and Spearman’s correlation coefficients between DON and D3G of two localities of Minnesota
for 2008-2010.
Year 2008 Crk GC‐DON Stp GC‐DON Crk LC‐DON Stp LC‐DON Crk D3G Stp D3G Year 2009 Crk GC‐DON Stp GC‐DON Crk LC‐DON Stp LC‐DON Crk D3G Stp D3G Year 2010 Crk GC‐DON Stp GC‐DON Crk LC‐DON Stp LC‐DON Crk D3G Stp D3G Crk GC‐DON Stp GC‐DON
Pearson correlation ‐ 0.61 **
0.58 ** ‐ 0.93 *** 0.60 ** 0.49 * 0.91 *** 0.71 *** 0.48 * 0.55 ** 0.92 *** Spearman correlation Crk GC‐DON Stp GC‐DON
Pearson correlation ‐ 0.63 ***
0.74 *** ‐ 0.71 *** 0.50 ** 0.65 *** 0.90 *** 0.52 ** 0.57 *** 0.57 *** 0.77 *** Spearman correlation Crk GC‐DON Stp GC‐DON
Pearson correlation ‐ 0.73 ***
0.67 *** ‐ 0.77 *** 0.54 *** 0.63 *** 0.87 *** 0.65 *** 0.46 *** 0.42 *** 0.69 *** Spearman correlation Crk LC‐DON
Stp LC‐DON
Crk D3G Stp D3G
0.90 ***
0.65 ** ‐ 0.51 * 0.68 *** 0.58 ** 0.56 **
0.96 *** 0.59 ** ‐ 0.46 * 0.87 *** 0.73 *** 0.45 * 0.56 ** 0.47 * ‐ 0.51 * 0.58 **
0.88 *** 0.56 ** 0.90 *** 0.59 ** ‐ Crk LC‐DON
Stp LC‐DON
Crk D3G Stp D3G
0.71 ***
0.53 ** ‐ 0.45 ** 0.56 *** 0.49 ** 0.62 ***
0.96 *** 0.52 ** ‐ 0.44 ** 0.69 *** 0.47 ** 0.30 NS 0.66 *** 0.24 NS ‐ 0.56 *** 0.57 ***
0.76 *** 0.50 ** 0.75 *** 0.41 * ‐ Crk LC‐DON
Stp LC‐DON
Crk D3G Stp D3G
0.85 ***
0.62 *** ‐ 0.57 *** 0.55 *** 0.34 ** 0.67 ***
0.89 *** 0.63 *** ‐ 0.45 *** 0.61 *** 0.71 *** 0.56 *** 0.69 *** 0.51 *** ‐ 0.44 *** 0.45 ***
0.72 *** 0.41 *** 0.67 *** 0.48 *** ‐ Crk: Crookston, Stp: Saint Paul, DON: deoxynivalenol, D3G: deoxynivalenol-3-glucoside
Objective 1
NS: No significant. *, **, and *** means correlation coefficient is significant at P˂0.05, 0.01, and 0.001,
respectively
Figure 2.2. Correlation between LC-DON and D3G values (combined 2008, 2009 and 2010). *** Significantly
different from 1 at P<0.001.
Publications
Ovando-Martínez, M., Ozsisli, B., Anderson, J.A., Whitney, K.L., Ohm, J.B., Simsek*, S. 2013. Analysis of
deoxynivalenol and deoxynivalenol-3-glucoside in Hard Red Spring Wheat inoculated with Fusarium
graminearum. Toxins. 5: 2522-2532.
Simsek*, S., Ovando-Martínez, M., Ozsisli, B., Whitney, K.L., Ohm, J.B. 2013. Occurrence of deoxynivalenol and
deoxynivalenol-3-glucoside in hard red spring wheat grown in the USA. Toxins. 5: 2656-2670.
North Dakota Wheat Commission.
Title: Specialty Wheat Research, Agency: North Dakota Wheat Commission, Amount: $30,000
Objective 1
Title
Estimation of Physical Properties and Quality of Canola Seed using Non-Destructive Techniques.
By
Jones, C.L., Oklahoma State University
Outputs
We tested methods using reflectance based sensing to determine quality of canola seed. Rancidity, FFA, extraneous
material, green seeds and erucic acid content were the qualities that were tested.
Outcomes/Impacts
Change in knowledge
--A flat-bed scanner can be used to detect foreign material in canola.
--NIR techniques can be used to estimate rancidity in canola using correlations with FFA and peroxide content.
--Erucic acid content can be estimated using NIR techniques.
--With the growth of the canola industry in the south, new techniques for grading are sought to speed up the process
of determining rancidity and grade of seed for market.
Publications
Jones, C. and G. Dilawari. 2013. Non-destructive estimation of free fatty acid content and peroxide value using NIR
spectroscopy in canola seed. Journal of Infrared Spectroscopy Status: accepted for publication.
Dilawari, G. and C. Jones. 2013. Quantification of dockage in canola using flatbed scanner. Transactions of the
ASABE 56(5):1-7.
Jones, C. L., 2013. Canola Aeration in Flat Storage Design for Zero Change Emissions, Producers Cooperative Oil
Mill, Oklahoma City, OK.
Storing Canola in Oklahoma Summer Conditions, Ag Expo, Oklahoma City, OK, December 4, 2013
Objective 1
Title
Elastic-plastic Deformation of Wheat Kernels: The Influence of Composition and Relationship with End Properties.
By
Rayas-Duarte, P., Oklahoma State University
Outputs
We performed physical tests of stress relaxation and creep recovery on wheat kernels and dough. We also studied
the influence of specific key proteins (high and low molecular weight glutenin subunits) composition on the
rheological properties as well as quality indicators of wheat and dough. Our work is based on the hypothesis is that
protein material contributes largely to the viscoelastic properties of wheat kernels as well as dough.
Outcomes/Impacts
Change in knowledge
Refined protocols for testing the evaluation of deformation properties on wheat kernels and dough that have
potential for wheat breeding laboratories.
Made critical comparisons that allowed to understand the influence of protein composition on the deformation
properties of wheat kernels and dough.
Found that there is value on the test performed on wheat kernels if more data is collected to confirm our findings.
Proposed to document similar properties on a more diverse genetic pool of wheat samples for comparison.
It is of interest to the research community and wheat breeders to acquire as much knowledge of the physical
properties and deformation of the structure of wheat kernels as possible as well as their relationship with protein
composition and end product characteristics.
Stress-relaxation tests can be applied to a variety of agricultural products and in particular to kernels. Such tests
reveal how fast the material dissipates stress. This is important in understanding their properties and behavior.
Publications
Figueroa, J. D. C., Hernández, Z. J. E., Rayas-Duarte, P., and Peña, R. J. 2013. Stress relaxation and creep recovery
tests performed on wheat kernels versus doughs: Influence of glutenins on rheological and quality
properties. Cereal Foods World 58(3):139-144. http://dx.doi.org/10.1094/CFW-58-3-0139.
Objective 1
Title
Reduction of Mycotoxin Levels in Distillers Grains.
By
Ileleji, K.E., Purdue University, West Lafayette, IN
Stroshine, R.L.
Shi, H. (Graduate Student)
Simsek, S., North Dakota State University, Fargo, ND
Siegel, V., Office of Indiana State Chemist (Collaborator)
Outputs
The specific objectives of this NC-213 Team Project are to: (1) determine the effect of pre-cleaning shelled corn on
mycotoxin reduction prior to processing into ethanol and distillers grains, (2) determine the effect of solvent
extraction on mycotoxin reduction in co-product streams (WDG and CDS) and the final DDGS product and (3)
determine the effect of ozone, aqueous sodium bisulfite, and microwave treatments, and their combinations on
mycotoxin reduction in co-product streams (WDG and CDS) and final DDGS product.
One approach to reducing mycotoxin levels in co-products is removal of some of the infected kernels by means of
pre-cleaning (Objective 1). This can be accomplished using differences in the properties of good kernels versus
kernels infected with mycotoxin producing fungi. Properties currently being investigated are kernel dimensions and
shape (sphericity), kernel density, and color sorting. Preliminary tests were conducted on kernels from the 2009
harvest season that were infected with Gibberella zea (which produces deoxynevalenol). Properties measurements
were made on samples infected with Aspergillus flavus (which produces aflatoxin) collected during the 2012 harvest
season. The major, intermediate, and minor diameters were measured with a caliper and kernel densities were
measured with a micropycnometer similar in construction to a device used for a study of wheat kernels conducted at
the Grain Marketing and Postharvest Research Center in Kansas (Martin et al., 1997).
Kernel Properties Measurements
The micropycnometer measures the kernel volume by liquid displacement. During initial tests, alcohol was used as
the liquid displaced. However, it dissolved the plastic ring that sealed the chamber enclosing the small moving
piston and it also dissolved other plastic components in the device. Consequently, red gauge oil, which was used in
GMPRC device, was selected. It has low volatility and low specific gravity (0.826), and very little of the oil is
absorbed by the corn kernel. Tests with glass spheres about the same size as soybean seeds and of known
(measured) diameter indicated that the pycnometer could measure seed volumes within 4% of the calculated
volume. It appeared as though much of the variance in these volume determinations could be attributed to
imperfections in the shape of the glass spheres. Therefore, a more precise check of the accuracy of the device used
four precision ball bearings (Table 1). According to the manufacturer, the diameter of the four Precision ball
bearings was 0.3125 inches ± 0.00003 inches (0.1%). We measured the volume of each precision ball bearing three
times with the micropycnometer. Test results show that the maximum difference between measured volume using
our device and the volume calculated from the diameter was less than or equal to 1%. The coefficient of variation
for repeated measurement was less than 0.1% (Table 1).
Objective 1
Table1. Volume calibration for micropynometer using precision ball bearings.
N
PBB* Diameter
(mm)
1
7.942
PBB nominal
volume
(mm3)
262.294
Measured Volume
(mean ± std)
(mm3)
262.563±0.175
Percent
difference
(%)
0.1%
0.07%
2
7.942
262.294
263.279±0.050
0.4%
0.02%
3
7.935
261.601
263.695±0.251
0.8%
0.10%
4
7.935
261.601
264.178±0.200
1.0%
0.08%
CV
(%)
Note: PBB means precision ball bearing, CV means coefficient of variance
During the fall of 2012 and winter of 2013, tests were conducted on a 29 bushel corn lot purchased from Brenneman
Farms near Clayton, Indiana. The corn contained relatively high levels of aflatoxin (ca. 40 ppb) and the sample that
was obtained from one of the farm’s bins had a very high level of fines. The corn was transported from the farm to
Purdue using a seed box.
Tests were conducted on a representative sample taken from the 29 bushels using a deep bin grain probe (thief
probe) for the purpose of determining the physical properties of the corn kernels. The primary purpose of these
measurements was to determine whether it would be possible to separate moldy kernels from the good kernels on
the basis of differences in these properties. Properties determined included dimensions of individual seeds (major,
intermediate, and minor diameter) and kernel densities of some of these same kernels.. The diameters were
measured using a calipers and the sphericity of each kernel was calculated using these dimensions. The samples
were subdivided into smaller samples using a Boerner Divider. Dimensions of a total of 115 moldy and 131good
corn kernels were determined and they micropycnometer was used to determine the density of 47 moldy and 57
good kernels. Test results are summarized in Table 2.
Moldy kernels
Good kernels
Table2. Physical properties for moldy and good corn kernels.
Size
Sphericity
Intermediate
Minor Diameter
Major Diameter
Diameter
(mm)
(mm)
(mm)
10.551±1.261
7.963±0.928
5.969±0.966
0.757±0.093
12.016±1.226
7.842±0.854
5.337±0.891
0.644±0.079
Density
(g/cm3)
1.147±0.101
1.215±0.092
The results shown in Table 2, indicate that the moldy kernels tend to have shorter major diameter, slightly longer
intermediate diameter, and slightly shorter minor diameter, moldy kernels also tended to be rounder and lower in
density. A two- tail t-test (assuming equal variance between the moldy and good kernels) at alpha level 0.05
revealed there were statistically significant differences in the major diameter, minor diameter, sphericity, and
density between the moldy and good kernels, with P values of <0.0001, <0.0001, <0.0001, and 0.0005, respectively.
However the P value for intermediate diameter was 0.28, indicating difference between the moldy and good kernels
was not statistically significant. These test results indicate that it should be possible to separate moldy kernels from
good kernels on the basis of differences in size, shape, and density.
Objective 1
Tests at a Commercial Seed Corn Processing Facility
Discussions with people working in commercial grain processing facilities indicate that significant reduction in
mycotoxin levels can be achieved using a combination of screen cleaning, density sorting, and color sorting. The
effectiveness of screen cleaning and gravity table (density) sorting in reducing aflatoxin levels was evaluated in the
Becks Hybrids inbred seed corn processing facility in Sharpesville, Indiana. The gravity table was an LMC - Lewis
M. Carter Manufacturing Company http://www.lmcarter.com/gravity-separators/. This facility has the capability of
sorting relatively small volumes of shelled corn using a screen.
The first process to be evaluated was a screen cleaner. As noted above, the sample contained a very high proportion
of fine material. In many samples with high levels of mycotoxins, the fine material contained high level of the
toxins. One possible explanation is that the kernels infected by fungi are more fragile and tend to break into small
pieces during combine harvesting. The corn was passed through the cleaner several times using different screen
sizes. During the first pass through the cleaner, a 13/64 inch round hole sieve (the smallest sieve available) was used
to remove fine material from the sample. The screen was very close in size to the 12/64 inch round hole sieve used
to determine the percent fine material during grading of a sample by a licensed grain inspector. The fine material
collected was passed through a Boerner divider multiple times until a relatively small but representative sample of
the fine material was available for mycotoxin testing. A representative sample of the “good” corn was collected by
periodically passing a can beneath the outlet stream from the cleaner as it dropped into a seed box from a flexible
tube coming from the cleaner.
After the test with the 13/64 inch round hole sieve was completed, the smaller kernels and remaining pieces of
kernels were separated from the bulk grain mass using a 17/64 inch round hole sieve. After all of the corn lot had
passed through the screen cleaner and the representative samples had been obtained from the two streams coming
from the cleaner, the fine material was added back to the good corn and the corn lot was passed through the cleaner
a second time. After each test, the corn from each of the grain streams was weighed and the weights were recorded.
About 10% of the original sample weight was removed using the13/64 inch sieve. The weight fractions removed by
the two passes through the cleaner with the17/64 sieve installed were 3.64% and 3.16%, respectively.
After separating the samples based on size, the samples were recombined, and passed through a Gravity Table to
remove the lower density kernels from the higher density kernels. The plant manager adjusted the side tilt, shaking
speed and airflow rate of gravity table in order to attain the best possible separation according to his experience. The
gravity table test was also replicated so that an indication of the consistency of the separation method could be
obtained. The lighter kernels were collected in a bag at the outlet of the separation stream and the size of the sample
was reduced by multiple passes through a Boerner divider. At the end of each test, the weight fractions of the two
streams were determined and recorded.
Smaller sized representative samples were obtained by passing the original sample through a Boerner divider
multiple times. The density for the original sample and the densities of the lighter and heavier kernels were
determined using the micropycnometer described above where it was used for laboratory tests. The results of the
micropycnometer tests for density are summarized in Table 3. Weight fractions of light kernels for the two tests
were 5.67% and 6.32%, respectively.
Objective 1
Table 3. Average density of samples separated from the screening and gravity table tests.
Sample
Density(g/cm3)
Original
1.212±0.065
13/64 Sieve overs
1.213±0.058
17/64 Sieve overs
1.221±0.063
17/64 Sieve passes
1.206±0.101
Gravity table light kernels
1.118±0.162
Gravity table heavy kernels
1.218±0.070
Data in Table 3 indicate that the average density of the overs from the 17/64 sieve cleaner was only slightly higher
than the density of the material that passed through the 17/64 sieve (0.015g/cm3). These observations mean that the
density difference between large and small kernels is not statistically significant. However there was a substantial
difference in density between samples separated by gravity table (0.1g/cm3). This suggests that the gravity table was
effective in separating the light kernels from the heavy kernels. To further characterize the effectiveness of the
gravity table, the density distributions were obtained for the original sample, the higher density kernels, and the
lower density kernels (Figs 1, 2 and 3). Cumulative percentages of density for the original sample and from the
samples of lower density and higher density kernels are plotted in figure 4.
Fig 1. Original sample
Fig 2. Distribution of Higher density kernels
Objective 1
Fig 3. Lower density kernels
Fig 4. Cumulative percent of kernels with given densities.
The data in the figures indicate that the density of the original sample and higher density kernels approximately
follow a normal distribution, whereas the density distribution of light kernels samples is skewed toward the left. The
density distribution of the original sample falls in between that of the higher density and lower density kernels
(Figure 4) and is closely related to that of the higher density kernels. This was not expected since light kernels
removed only comprised around 6% of the total weight. Although there was considerable overlap between density
distributions of higher and lower density corn kernels, there were still significant differences among the cumulative
percentages of the higher and lower density kernels. The percentage of higher density kernels increased dramatically
around a kernel density of 1.15g/cm3, making it potentially a good point to separate the kernels according to density.
To evaluate the effect of the three separation processes on aflatoxin levels in the streams coming from the
equipment, representative samples were tested for aflatoxin using an EnviroLogix test kit and Quickscan reader
(EnviroLogix Inc., Portland, Maine). Preliminary tests results for several of the samples are presented in Table 4.
Table 4. Aflatoxin levels in separated samples.
Separation Methods
Sample
Screen cleaner
13/64 sieve overs(kernels)
13/64 inch round sieve
13/64 sieve fines
Screen cleaner,
17/64 sieve overs
17/64 inch round sieve (run1)
17/64 sieve passes
Gravity table (run 2)
GT #2 heavy kernels
GT #2 light kernels
*Below the level of detection of the instrument.
AF level(ppb)
6.2
7.2
1818
1313
5.5
6.2
389.5
393.6
<LO
2.9
Q*
779
820
Objective 1
As is obvious from the results shown in Table 4, the fine material in this corn lot contained extremely high levels of
aflatoxin, indicating that removing the fines from the sample could greatly reduce the overall levels of aflatoxin in
the corn.. In addition, the portion of the sample that passed through the17/64 sieve had much higher aflatoxin levels
than the overs. The lower density kernels from gravity table also had higher aflatoxin levels than the higher density
kernels. Thus, for this corn lot, both the screen cleaner and the gravity table were effective in reducing levels of
mycotoxins. Aflatoxin levels of the remaining samples will be determined later.
Evaluation of Optical sorting:
The potential for using optical sorting for reducing the mycotoxin loads was investigated using a laboratory optical
sorter available in Dr. Simsek’s laboratory at North Dakota State University. The samples tested were collected
during the 2009 harvest season when there were high levels of Deoxynevalenol as a result of the growth of
Gibberela zea (an ear rot) on the corn prior to harvest. A one kg subsample was taken from each of two larger corn
samples each containing kernels infected with Gibberella zea and having approximately 10 to 15 ppm
Deoxynevalenol. Approximately 100 normal and 100 discolored kernels were visually selected from the 1 kg
samples and used to calibrate the sorter. Then the two 1 kg samples were passed through the sorter multiple times
until visual observation indicated few if any discolored kernels remained in the “normal” sample. Attributes of
samples prior to sorting and the “normal” and “discolored” samples were determined. Test weight was measured
using a Dickey-John GAC 2100b. Approximately 200 g of each sample was ground in a hammermill for laboratory
analyses of moisture, ash, protein, and lipid contents. The color (CIE LAB scale) of the corn meal was also
measured using a Minolta CR420 colorimeter. Mycotoxin levels (Deoxynevalenol, 15 Acetyl-deoxynevalenol, and
Zearalenone) were determined using gas chromatography (mass spectrometry detector). Results of the tests are
summarized in Table 5. After color sorting, the mycotoxin levels were 3.5 to 17 times higher in the “discolored”
samples compared to the “normal” samples. Color sorting had little if any effect on moisture, test weight, protein
and ash levels. The lipid levels in the two “normal” samples were lower than the levels in the discolored samples.
For CIE color measurements the “L” values indicate lightness (closer to 100) or darkness (closer to 0), the “a”
values indicate redness (+) or greenness (-) and the “b” values indicate yellow color (+) or blue color (-). The results
in Table 1 indicate that the discolored kernels were slightly darker (lower “L”), slightly more red (a closer to 0) and
not as yellow (lower b value).
Although significant reductions in mycotoxin levels were achieved, a relatively high proportion of “discolored”
kernels were removed from the samples and multiple passes were needed to achieve thorough sorting. It seems
unlikely that industry would find removal of such high proportions of the kernels cost effective. Therefore,
additional color sorting tests are planned in which only one or two passes through the sorter will be used and in
which fewer kernels with “red streak” are included in the sample used to “train” the sorter to recognize “discolored”
kernels.
Objective 1
Table 5. Results of color sorting for removal of mold damaged corn kernels (DWB = dry weight basis).
Units
Moisture
%
Test Weight lbs/bu
Protein
%
DWB
Ash
%
DWB
Lipid
%
DWB
L
Color
a*
b*
DON
ppm
15 ADON
ppm
Zearalenone ppm
Sample 1
Sample 2
Unsorted Normal Discolored
Unsorted Normal Discolored
9.60
54.40
9.70
54.40
9.30
54.90
9.50
55.10
9.70
55.60
9.50
55.80
8.91
8.92
8.92
8.68
8.68
8.70
1.23
1.24
1.22
1.23
1.21
1.23
1.40
0.73
1.25
1.57
1.11
1.21
84.39
-0.79
24.89
9.10
3.10
0.40
84.47
-1.15
25.51
2.10
1.70
0.00
82.90
-0.11
23.71
33.60
6.20
1.00
84.21
-0.82
24.00
14.20
5.00
2.00
85.34
-1.31
25.07
2.40
2.40
0.00
83.06
-0.34
23.17
41.50
9.70
2.80
Treatment of DDGS for reduction of mycotoxins
Work on the third objective has focused primarily on the effect of ozone, aqueous sodium bisulfite, and microwave
treatments, and their combinations on mycotoxin reduction in co-product streams (WDG and CDS) and the final
DDGS product. We are currently developing a protocol for ozone treatment of wet distiller’s grains using the PK-2
system developed by Klockow and Keener (2009) and can ionize a sealed package of air with an electrode gap of 10
cm. It is based on a dielectric barrier discharge (DBD) with plate electrodes comprised of insulated conductors
connected to a power unit with specifications of 130 kV at 20 mA and 60Hz. Ionization can generate significant
amounts of reactive molecules, including ozone concentrations above 1% in a few minutes. A 12” by 12” cryovac
bag filled with about 50 g of distillers wet grains with solubles (DWGS) was placed between the electrode of the
PK-2 device for treatment at voltage conditions of 70kV with 3.2 cm electrode gap (PK-2). In order to optimize
treatment times for maximum ozone generation, three test times were investigated: 120 s, 240 s and 360 s.
Measurements of ozone concentration in the package prior to and after ozonation was determined by measuring
methylene blue (MB) discoloration. In future tests, concentrations of other enclosed gases such as carbon monoxide
and hydrogen peroxide will be determined in the package prior to and after ozonation using a portable Drager
Measurement System as well as peroxide strips. Fungal counts were determined on samples of ozonated distillers
wet grains (DWG) samples by plating samples on Plate Count Agar (PCA). The control sample was plated at 10-4
dilution. After the incubation, colony forming units (CFUs) were counted and data was reported as CFUs per gram.
This specific preliminary test was not focusing on mycotoxin reduction after ozonation, but rather the effect of
reduction of fungal levels by ozonation. Table 6 shows that there was a single log reduction achieved compared to
the control. It is expected that a 2 to 3 log reduction is required to achieve a “good reduction”. Therefore,
adjustments may need to be made in order to increase the reduction. After establishing the protocol for ozonation,
we will conduct tests focusing on treating DWGS with known mycotoxin levels to determine the efficacy of
ozonation as a means of reducing mycotoxin levels in DWGS and DDGS.
Objective 1
Table 6. Analysis of APC results compared with the change in color from no load MB test in Air for samples
in the field.
Sample
Control
Treatment 1 (120 sec)
Microbial Count
(Log(CFU/g))
5.75
4.78
4.75
4.98
Log Reduction
0
0.97
1.00
0.77
% discouloration of
MB (%)
3.95
87.25
97.13
98.12
Outcomes/Impacts
Reducing the levels of mycotoxins in distiller’s grains will provide ethanol plants another means of managing the
impact of outbreaks of mycotoxins to DDGS, and thus increase the marketability of DDGS co-products. The
importance and relevance of this study were demonstrated by the 2009 and 2012 growing seasons in which a
significant amount of the corn grown in Indiana (and other Midwestern states) contained mycotoxins
(Deoxynevalenol in 2009 and Aflatoxin in 2012). When corn is processed into ethanol, the levels of mycotoxins in
the DDGS co-product are generally three times the levels in the incoming corn. Therefore, reduction of mycotoxin
levels by removal of some of the infected kernels could significantly reduce levels of mycotoxins in the DDGS to
below the threshold for livestock feed. A general indication of the effectiveness of screen cleaning, density sorting,
and color sorting will allow processors to estimate the cost associated with achieving various levels of reduction.
Alternative approaches involving the use of solvent extraction to reduce mycotoxins in the co-product stream
(objective 2) or treatments that destroy the mycotoxins present (objective 3) are also being investigated. When
information on the effectiveness of these methods has been obtained and the appropriate treatment protocols have
been developed it will permit processors to select the best method of reducing mycotoxin levels of corn processed in
their facilities.
References
Klockow, P.A. and K. M. Keener. 2009. "Safety and quality assessment of packaged spinach treated with a novel
ozone-generation system," LWT - Food Science and Technology 42(2009): 1047-1053
Martin, C., T.J. Herrman, T. Loughin, and S. Oentong. 1997. Micropycnometer Measurement of Single-Kernel
Density of Healthy, Sprouted, and Scab-Damaged Wheats. Cereal Chemistry 75(2):177-180.
Contacts
Klein E. Ileleji (Tel (765) 494-1198) and Richard Stroshine (Tel 765-494-1192)
Department of Agricultural and Biological Engineering,
Purdue University, 225 South University Street
West Lafayette, IN 47906
Fax: (765) 496-1115 Emails: [email protected] and [email protected]
Objective 1
Title
Improving Functionality of Wheat and Sorghum for Food Applications: Enhancing Process Efficiency and Health
Properties Targeting Tortillas and Flatbreads.
By
Awika, J., Texas A&M AgriLife Research
Herrman, T.
Rooney, L.W.
Outputs
The high demand for wheat flour tortillas (sales exceeded $ 6 billion in the US in 2012) and other flatbreads has
resulted in the need for rapid, accurate and cost-effective means to predict tortilla making performance for large
number of early generation wheat lines. Currently, the most reliable approach is to process tortilla which is
laborious, time consuming, expensive and requires large sample size (2 kg wheat). At the same time, the popularity
of tortillas as a dietary staple has created a growing need for healthier product offerings that meet consumer sensory
expectations, while contributing to chronic disease prevention. Unfortunately most tortillas in the market have poor
nutritional profile; high in rapidly digesting starch and fat, and low in dietary fiber and bioactive phytochemicals.
Thus there is a strong need to develop wheat with the right protein functionality for tortillas and to improve tortilla
quality to meet both sensory and nutritional expectations of consumers.
This study used a multivariate discriminant analysis to predict tortilla quality using kernel, flour and dough
properties. A discriminant rule was used to classify wheat lines for suitability in making good quality tortillas.
Wheat varieties from Texas (n = 186) were evaluated for kernel (hardness, diameter, and weight), flour (protein
content, fractions and composition), dough (compression force, extensibility and stress relaxation from TA-XT2i)
and tortilla properties (diameter, rheology and flexibility). First three principal components explained 62% of
variance. Canonical correlation analysis revealed significant correlation between kernel and tortilla properties (r=
0.83), kernel hardness contributed the highest to this correlation. Flour and tortilla properties were highly correlated
(r = 0.88), Glutenin to Gliadin ratio (Glu:Gli) contributed highest to this correlation and can predict tortilla
flexibility and deformation modulus. Dough and tortilla properties were significantly correlated (r = 0.91). Logistic
regression and stepwise variable selection identified an optimum model comprised of kernel hardness, Glu:Gli,
dough extensibility and compression force as the most important variables. Cross-validation indicated an 83%
prediction rate for the model. This emphasizes the feasibility and practicality of the model as a wheat quality
screening tool using variables that are easily and quickly measured.
Phenolic compounds from sorghum bran are known to have beneficial health properties and may be useful for
improving health attributes of tortillas. Impact of sorghum brans with different phenolic profiles on dough rheology,
starch digestibility, and phenolic profile of wheat flour tortillas fortified with 25% (baker’s) bran from wheat and
white, brown, and black sorghum were investigated. Dough compression and stress relaxation were measured using
a TA.XT2i texture analyzer. Total (TS), rapidly digestible (RDS), slowly digestible (SDS), and resistant starch (RS)
fractions were evaluated in tortillas using in vitro digestion. Phenolic profile was determined by UV-Vis
Spectroscopy and HPLC. Dough force to compress increased from 105 N (control) to 170-263 N with the addition
of bran. Dough with black sorghum bran required the largest force to compress (263 N) and lowest relaxation time
(1.3 s) compared to other bran treatments (1.5-1.6 s), suggesting increased water absorption. As expected, addition
of bran significantly decreased TS and RDS compared to control (P<0.05). Bran decreased the RDS from 61%
(control) to 49-51% (db). There was no significant difference in RDS among the brans. Brown sorghum bran
Objective 1
tortillas had significantly higher SDS (11.3%) than the other brans (6.38-8.15%); this may be due to high levels of
proanthocyanidins in brown sorghum bran. RS was not significantly affected. Addition of brown and black bran
significantly increased (P<0.05) the total phenolic content of tortillas to 43.3 and 52.4 (mg GAE/g), respectively,
compared to 9.46 (wheat bran) and 12.09 (white sorghum bran). Brown and black sorghum bran utilization in wheat
flour tortillas may beneficially affect the starch digestibility while increasing polyphenol content.
Outcomes/Impacts
The predictive model represents an important step in wheat quality improvement for flatbread market. The
predictive tests require use of small sample size (≤ 10% compared to traditional methods) and are much less time
(1/5th) and labor (1/4th) intensive compared to traditional tortilla flour testing, thus represent major cost saving.
Wheat flour tortilla is a $ 6 billion market in the USA, and growing rapidly. Specialty wheat development targeting
this market will significantly improve market opportunities for farmers as well as processing efficiency for
manufacturers.
Wheat with optimized functionality for tortillas will enable easier development of healthy tortilla products with
acceptable sensory appeal.
Identifying impact of sorghum polyphenol composition and processing on flatbread nutritional and sensory quality
will result in innovative sorghum utilization to improve health attributes (e.g., reduced impact on blood sugar and
calorie intake) of baked goods.
This research topic resulted in 2 national research awards by Dr. Awika’s graduate students in the past year,
emphasizing its relevance to the scientific community and the public.
Publications
William L. Rooney, Lloyd W. Rooney, Joseph Awika, and Linda Dykes. 2013. Registration of Tx3362 sorghum
germplasm. Journal of plant Registration, 7, 104-107.
Ojwang, L.O., L. Yang, L. Dykes, J. M. Awika. 2013. Proanthocyanidin profile of cowpea (Vigna unguiculata)
reveals catechin-O-glucoside as the dominant compound. Food Chemistry. 139, 35-43.
Nderitu, A. M., L. Dykes, J. M. Awika, Minnaar, A., Duodu, K. G. 2013. Phenolic composition and inhibitory effect
against oxidative DNA damage of cooked cowpeas as affected by simulated in vitro gastrointestinal
digestion. Food Chemistry. 141, 1763-1771.
Objective 1
Frederico Barros, L. Dykes, J.M. Awika, L. W. Rooney. 2013. Accelerated solvent extraction of phenolic
compounds from sorghum brans. Journal of Cereal Science 58, 305-315.
Hachibamba, T., L. Dykes, J. M. Awika, Minnaar, A., Duodu, K. G. 2013. Effect of simulated gastrointestinal
digestion on phenolic composition and antioxidant capacity of cooked cowpea (Vigna unguiculata)
varieties. International Journal of Food Science and Technology 48, 2638-2649
Frederico Barros, Joseph M. Awika, Lloyd W. Rooney. 2013. Effect of molecular weight profile of sorghum
proanthocyanidins on resistant starch formation. Journal of the Science of Food and Agriculture. (in press)
DOI: 10.1002/jsfa.6400
Awarded Grant(s) and Contracts(s)
Funding for the project obtained from: Bayer Crop Science, Texas Wheat Producers Board, Howard Buffett
Foundation, Texas A&M AgriLife Research.
Objective 1
Title
Development of Surface-enhanced Raman Spectroscopy (SERS) and Liquid Chromatography-tandem Mass
Spectrometry (LC/MS-MS) methods for Rapid Detection of Mycotoxins in Food and Feed Matrices.
By
Lee, K.M., Texas A&M AgriLife Research
Herrman, T.J.
Li, W.
Outputs
Rapid and sensitive surface-enhanced Raman spectroscopy (SERS) for aflatoxin detection was employed for
development of the spectroscopic method to classify and quantify aflatoxin levels in maize. The proposed SERS
method would serve as a valuable screening tool with a great accuracy and convenience for a high-throughput
aflatoxin analysis in plant breeding programs, hybrid performance trials, or during harvest in the field requiring
rapid routine analysis.
In preprocessed Raman spectra, qualitative spectral differences in the normalized and derivative preprocessed
spectra were observed in the Raman shift ranges of 400−530 cm-1, 800−980 cm-1, 1100−1250 cm-1, and 1500−1800
cm-1. The major bands in these Raman shift regions may be associated with fugal cellular metabolites and modified
cell membrane and inhibited protein and starch synthesis due to the effects of fungal and aflatoxin infection of
kernels. Shifts of some aflatoxin bands, in particular, in the aflatoxin-related Raman spectral regions, were easily
observed compared to corresponding bands of conventional Raman spectroscopy. The shifts of Raman bands can be
attributed to the effect of charge transfer between adsorbed aflatoxin molecules and silver (Ag) metal in combination
with the resonance excitation of surface plasmon. In representative scanning electron microscope (SEM) images of
the Ag nano-spheres and their mixture with the extract, dispersed Ag nano-crystals with various diameters can be
observed and they form blocks into Ag nano-crystals with the diameters of 70−80 nm at an inter-particle spacing of
1-2 nm. The Ag nano-spheres with the packed Ag nano-crystals contain periodic hot spots or junctions which
provide surface areas to adsorb aflatoxin or bioactive molecules.
The classification models for aflatoxin contaminated and non-contaminated maize sample groups were developed by
applying the k-nearest neighbors (KNN) algorithm for the four preprocessed SERS spectra in the Raman shift region
of 400–1800 cm-1. The correct classification rate of all calibration models was 100% for training data regardless of
the spectra preprocessing method while rather lower classification accuracies (71.491.4%) were achieved when the
calibration models were applied to validation data. The KNN models offering higher classification accuracy in
classifying aflatoxin contaminated samples were obtained using normalized and deconvolution preprocessed spectra
data. In these two models, almost no samples with greater than 20 μg/kg were misclassified as aflatoxin negative.
The chemometric methods including multiple linear regression (MLR), principal components regression (PCR), and
partial least squares regression (PLSR) algorithms, were employed for development of aflatoxin quantification
models. The performance of all models assessed by statistical measures showed a great dependence of the models on
the chemometric and preprocessing method. The MLR models showed better regression quality, higher predictive
accuracy, and lower error rate than other chemometric methods while the PCR models yielded far less satisfactory
results compared to the MLR and PLSR models. All MLR models developed with the preprocessed spectra could
account for more than 90% of variation in both training and validation data set. Coefficients of determination (r2)
and predictive error rate of the former three calibrations models applied to the validation data set were in the range
of 0.9130.934 and 86.292.4 µg/kg, respectively. This observation indicates a good quality of the regressions and
high predictive capability of selected MLR models suitable for screening of aflatoxin contaminated samples. The
Objective 1
PLSR calibration models yielded comparable results to the MLR calibration models for the training data set,
resulting in high r2 values (0.9320.974), lower error rate (47.977.0 g/kg), and a linear regression slope in the
range of close to 1. However, the predictive accuracy of the PLSR calibration models predicting the validation
samples was not as good. Paired sample t-test for the validation data set showed no statistically significant
difference (p < 0.01) between the reference high performance liquid chromatography (HPLC) values and the
predicted values of SERS in all aflatoxin quantification models, except for the MLR model for 2nd derivative
spectra. According to the criterion of RPD (ratio of the standard deviation of the reference data to the standard error
of cross-validation) values, the MLR models may be effective for screening of aflatoxin contaminated samples.
Despite the chemometric models developed by the conventional Raman spectroscopy showed a slightly higher
predictive accuracy than those of the SERS, the effectiveness and efficiency of the SERS would be better if more
considerations are given to the sensitivity, analysis speed, the reproducibility of spectra, and potential improvement
of SERS substrate.
A rapid determination of deoxynivalenol (DON), 3-acetyl-deoxynivalenol (3-DON), 15-acetyl-deoxynivalenol (15DON), ochratoxin A (OTA) and zearalenone (ZON) in animal feed matrices was also developed. A generic
extraction procedure was used to construct the analyte solution for instrument analysis. Ultra-pressure LC (UPLC)
was combined with high sensitivity tandem MS system. Multiple reaction monitoring (MRM) was used for its high
selectivity and sensitivity (Figure 1). Isotope labeled internal standards (U-[13C15]-deoxynivalenol, U-[13C20]ochratoxin A and U-[13C18]-Zearalenone,) were used to minimize the matrix effect.
Objective 1
Figure 1. MRM transitions of DONs, OTA, ZON and their isotope labeled internal standards.
To validate the method, blank samples (corn or cotton seed) were fortified at different levels for recovery study. All
recoveries range from 89% to 106% with RSD less than 12% (Table 1). Based on the validation data, the developed
method could be satisfactorily applied as a routine procedure to identify and quantify the multiple mycotoxins in
corn or cotton seed samples.
Objective 1
Table 1. Recoveries and precision of spiking cornmeal samples by LC/MS/MS method. RSD: Relative Standard
Deviation Accuracy was present as relative error. All correlation coefficients for the standard curves were bigger
than 0.99. RSDs (%) were calculated based on the data collected on three different days.
Analyte
DON
3-DON
15-DON
OTA
ZON
Nominal concentration
(ng g-1)
125
1250
2500
125
1250
2500
125
1250
2500
12.5
125
250
10
20
40
Recovery
(%)
100
96
98
104
93
97
100
95
99
82
106
102
95
89
97
Inter-day RSD
(%) (n=3)
5
6
6
8
6
5
7
6
4
12
9
7
9
8
4
Intra-day RSD
(%) (n=9)
4
5
4
7
6
4
6
6
3
10
7
6
11
9
4
The developed method has been applied for a surveillance program by testing 728 regulatory corn samples through
20112013. Twenty of these 728 samples have OTA higher than 5 ppb. None of them have DONs or ZON higher
than EC regulation (1250 ppb for DONs, 5 ppb for OTA and 200 ppb for ZON in completed feed).
Outcomes/Impacts
The proposed SERS method will offer a rapid and highly sensitive analytical tool with a higher accuracy and lower
constraints in aflatoxin analysis in maize and can provide more comprehensive and detailed information on the
structural and morphological properties of aflatoxin molecules. Successful implementation of the developed SERS
method equipped with automated spectra processing and statistical analysis algorithms would be expected to greatly
facilitate a real-time monitoring and online quality control at critical locations in the maize supply chains, which
should help reduce risks of aflatoxin contamination to animals and humans and increase the safety of maize products
with short- or long-term economic benefits.
With the application of the isotope labeled internal standard in our study, the matrix effect in LC-MS/MS analysis
was effectively eliminated and the performance of our quantification method met the requirement of USFDA and
EU regulation criteria. The LC-MS/MS method developed here represents the fastest and simplest procedure with
the higher throughput analysis than both the conventional HPLC methods and other LC /MS methods involved with
solid phase extraction (SPE) cleanup. The developed method could be satisfactorily applied as a routine procedure to
Objective 1
identify and quantify the above mycotoxins in laboratories of feed quality and safety control when a large sample
load is required.
Publications
K.M. Lee, T.J. Herrman, C. Nansen, and U. Yun. 2013. Application of Raman spectroscopy for qualitative and
quantitative detection of fumonisins in ground maize samples. Int. J. Regul. Sci. 1:114.
K.M. Lee, T.J. Herrman, and U. Yun. 2013. Application of Raman spectroscopy for qualitative and quantitative
analysis of aflatoxins in ground maize samples. J. Cereal Sci. (in press)
K.M. Lee, T.J. Herrman, C. Nansen, and U. Yun. 2013. Application of Raman spectroscopy for qualitative and
quantitative detection of fumonisins in ground maize samples. American Association of Cereal Chemists
(AACC) Annual Meeting. Albuquerque, NM.
Wei Li, Susie Y Dai, Timothy J. Herrman. Applications of LC-MS in Clinical Pathology and Food Safety. College
of Chemistry and Molecular Science, Wuhan University, Wuhan, China.
Awarded Grant(s) and Contacts(s)
Andersons Research Grant Program Regular Competition.
AMCOE (Aflatoxin Mitigation Center of Excellence) Research Program of National Corn Growers Association
(NCGA).
Objective 1
Title
Characterization of Sorghum Biomolecules and their Functionality and Relationships to Sorghum Utilization and
End-Use Qquality.
By
Bean, S.R., CGAHR, USDA-ARS, Manhattan, KS
Outputs
Thirty six samples varying in in-vitro protein digestibility were used for in-depth biochemical characterization. A
new multi-step extraction procedure was developed that was designed to extract as much protein as possible while
leaving covalently bound protein structures intact. The extraction procedure extracted ~90% of total protein.
Significant negative correlations to the amount of the most difficult to extract were found suggesting that the
proteins in these protein complexes are important factors in governing protein digestibility in sorghum. Work is in
progress to characterize the proteins in these fractions and how they are linked together.
Work was also completed on a set of sorghum samples with known variability for kafirin alleles. Raw and cooked
protein digestibility was measured on this set and protein composition characterized using size exclusion
chromatography, reversed-phase high performance liquid chromatography and electrophoresis. The role of proteins
on ethanol fermentation efficiency was also studied with this sample set. Kafirin allelic variants were found to
influence ethanol fermentation efficiency and an RP-HPLC peak was found that was highly negatively correlated to
protein digestibility suggesting it could be used as a marker to predict protein digestibility.
Outcomes/Impacts
Protein digestibility in sorghum remains an important factor regarding the end-use quality of sorghum for food, feed,
and fuel. Screening of a diverse set of sorghum lines for digestibility used in this research has resulted in the
discovery of germplasm with high levels of inherent digestibility while providing sample sets for detailed research
to identify factors that govern protein digestibility in sorghum other than what is known about protein body
structure. Information gained from this sample set can be used by the sorghum breeding community as well as
researchers investigating digestibility in sorghum.
Publications
Kaufman, R. C., Herald, T. J., Bean, S. R., Wilson, J. D., and Tuinstra, M. R. 2013. Variability in tannin content,
chemistry, and activity in a diverse group of tannin containing sorghum cultivars. J. Sci. Food Ag. 93:12331241
Mkandawire, N.L., Kaufman, R. C., Bean, S. R., Weller, C.L., Jackson, D.S., and Rose, D.J. 2013. Effect of
condensed tannins from sorghum (Sorghum bicolor (L.) Moench) on in vitro starch digestibility and amylase activity. J. Agric. Food Chem. 61: 4448-4454.
Kaufman, R. C., Wilson, J. D., Bean, S., Presley, D.R., Blanco-Canqui, H., and Maysoon, M. 2013. The effect of
nitrogen fertilization and cover cropping systems on sorghum grain characteristics. J. Agric. Food Chem.
61:5715-5719.
Objective 1
Kapanigowda, M.H., Perumal, R., Aiken, R., Herald, T., Bean, S., and Little, C.R. 2013. Evaluation of sorghum
[Sorghum bicolor (L.) Moench] lines and hybrids for cold tolerance under field and controlled
environments. Canadian J. Plant Sci. 93:773-784.
Smith, B.M., Bean, S. R., Selling, G., Sessa, D., and Aramouni, F. 2013. Role of non-covalent interactions in the
production of visco-elastic resins from zein. Food Chem. 147:230-238.
Andersons Research Grant Program, $24,825.
Objective 2
To develop methods to maintain quality, capture value, and
preserve food safety at key points in the harvest to end
product value chain.
Objective 2
Title
Risk Assessment for the Food Safety Concerns of Mycotoxins in the Pacific Northwest under Climate Variability.
By
Ryu, D, University of Idaho
Outputs
The objectives 1 and 2, optimization and verification of methods for detecting and quantifying fungal population in
soil and development of analytical method to detect mycotoxins are in progress. Different methods using HPLC and
LC-MS are being tested for the detection of mycotoxins while techniques to isolate and identify fungal strains are
being examined.
Test plots for winter wheat varieties are located and sampling plan is being constructed to match the growing season
in the area in collaboration with Washington State University Extension (Variety Testing Program). The test plots
were selected based on the amount of rainfall and agricultural conditions. Grain samples will be analyzed for fungal
population and mycotoxin concentration upon harvest. Soil samples from each testing plots will be collected and
analyzed for the fungal population.
Outcomes/Impacts
As the researcher recently moved and equipped his laboratory, measurable outcomes/impact is not yet available.
Objective 2
Title
To Develop Efficient Operating and Management Systems that Maintain Quality, Capture Value, and Preserve Food
Safety in the Farm-to-User Supply Chain.
By
Mosher, G.A., Iowa State University
Hart, C.E.
Hurburgh, C.R.
Shaw, A.M.
Shepherd, H.E. (Staff)
Snyder, H.
Outputs
An educational module focusing on risk management in grains and oilseeds has been developed for regulatory
personnel who are inspecting grain handling facilities as required by the Food Safety Modernization Act. The
module is part of a larger distance education course offered to regulatory personnel and will be the basis for the
development of a job aid to assist regulatory officials during inspections of grain handling facilities.
Educational modules on corn processing were created to complement wheat and oilseed processing modules created
by Kansas State University. The modules will be included in the distance education course offered to regulatory
personnel.
A review article on soybean composition as related to grain yield was accepted. As part of the American Association
of Cereal Chemistry Food Safety Task Force outputs, a guidance document for the application of ISO22000, Food
Safety Management Systems to bulk processing and handling operations was drafted.
Previous quality systems research was published. Several presentations were created and delivered in 2013 on topics
that included: pre-harvest grain quality outlook, mycotoxin management, and grain storage economics.
Outcomes/Impacts
As federal agencies manage increasing public expectations with lower budgets, it is clear that resources must be
focused on the areas of the supply chain which have the highest likelihood of introducing food safety issues. The
risk management module provides guidance to federal regulators on which microbiological, chemical, and physical
hazards represent the greatest food safety risk. The knowledge allows them to apply focused and targeted
intervention to areas of the farm-to-user supply chain that represent the greatest food safety risk.
The education programs in pre-harvest grain quality look, mycotoxin management, and grain storage economics
allow grain handlers and producers to make more informed monetary decisions on grain storage and testing
procedures.
The consistency of soybean quality patterns and root causes over many years was documented. Markets will be
better advised to adapt to the patterns than to attempt large-scale change at the cost of overall productivity. It is
expected that a comprehensive and science based direction for bulk-materials industry food safety management will
be completed in 2014.
Objective 2
The creation and delivery of distance education modules allows federal agencies to train their inspectors without the
coordination and logistical costs of offering a face-to-face course.
Publications
C.E. Hart. 2013/14 Crop Market Outlook. Voice over Power Point presentation. Iowa State Grain Quality Initiative,
Iowa State University Extension and Outreach. September, 2013.
https://connect.extension.iastate.edu/p8bvrdk0y66/?launcher=false&fcsContent=true&pbMode=normal.
C.R. Hurburgh. Pre-harvest Outlook for Grain Quality: 2013 Crop. Voice over Power Point presentation. Iowa State
University Extension and Outreach. September, 2013.
https://connect.extension.iastate.edu/p3jly245js7/?launcher=false&fcsContent=true&pbMode=normal.
C.E. Hart. Marketing Update for 2013 and Beyond. Proceedings of the Integrated Crop Management Conference,
Iowa State University Extension and Outreach. Ames, IA. December 4-5, 2013.
G.A. Mosher, N. Keren, C.R. Hurburgh. 2013. Employee trust and its influence on quality climate at two
administration levels. Journal of Technology, Management, and Applied Engineering (JTMAE), Volume
29, Number 1 (January through March 2013).
G.A. Mosher, N. Keren, & C. R. Hurburgh. 2013. Development of a quality decision-making scenario to measure
how employees handle out-of-condition grain. Applied Engineering in Agriculture, 29(5), 807-814.
G.A. Mosher. Considerations and limitations of risk analysis in a bulk grain supply chain. Presentation given at the
NC-213 Annual Meeting, February, 2013.
Jelena Medic, Christine Atkinson, and Charles R. Hurburgh, Jr. 2013. Current knowledge in soybean composition.
JAOCS (acc.)
Mosher, G.A., N. Keren, S.A. Freeman, and C.R. Hurburgh. 2013. Two-level measurement of trust and safety
climate in the commercial grain elevator. Journal of Agricultural Safety and Health, 19(2), 125-134.
Awarded Grants(s) and Contract(s)
Iowa Extension 21 Program, NIH/FDA.
Objective 2
Title
Intrinsic Characteristics of Modified DDGS and Development of Effective Handling Strategies.
By
Ambrose, K., Kansas State University, Department of Grain Science and Industry
Casada, M., USDA-ARS-CGAHR, Manhattan, KS
Simsek, S., North Dakota State University, Department of Plant Sciences
Outputs
From the increased profits and also the possibility of product streams with different quality concentrations, corn
ethanol processing industries are increasingly inclined towards the production of Modified DDGS (M-DDGS).
Though the industries are fast converting to this modified DDGS production, there is lack of information on
handling characteristics of modified DDGS. This research work will determine the intrinsic particle characters of MDDGS that influence their bulk handling behavior. This study involves lab scale testing of flow characteristics,
development of mathematical models for optimizing cooling, and also concentrates on development of mechanical
aids that improve the flow properties. The modified and regular DDGS samples were procured from dry-grind
processing facility in the mid-west U.S. The DDGS samples with different oil contents were characterized for the
physical and chemical characteristics. Particle size and particle size distribution were determined using ASABE
Standard S 319.4. The flow properties of bulk granular solids are governed by the factors of product moisture
content, particle size distribution, chemical composition, storage/handling conditions (temperature and relative
humidity), and time consolidation and compaction pressure. Since a combination of these factors influence the flow
behavior of DDGS, in this project work, different combinations of these effects were testing using the FT-4 powder
rheometer (Freeman Technologies, UK).
The particle size distribution plot (fig. 1) clearly indicates that there is most variability and spread for regular DDGS
(R-DDGS) than modified DDGS (M-DDGS). Furthermore, the geometric mean diameter (GMD) of R-DDGS
(1.078mm) is higher than M-DDGS (0.36mm). The fat content of M-DDGS was around 5.36% and R-DDGS is
9.47% (w.b.). As expected, due to smaller particle size and higher packing, the porosity of M-DDGS (62.15%) was
found to be lower than R-DDGS (66.91%). R-DDGS was lighter than M-DDGS as indicated by their bulk density
values (table 1). Air flow resistance as presented in the log-log plot of air velocity vs. pressure drop (fig. 2) across
the bed of M-DDGS revealed that at both 10% and 12% moisture contents, for a given air velocity of 7.29 mm/s the
pressure drop is lower in R-DDGS than M-DDGS. Higher the pressure drop higher the resistance to airflow and
hence more cohesiveness. Least significant difference (LSD) test with alpha = 0.05 (95% confidence levels)
revealed that there were more instances of no significant difference among levels of independent variables for MDDGS (table 3) than R-DDGS (table 2). This indicates that variation in flow properties for M-DDGS is less than RDDGS for given set and range of independent variables. This is possibly due to minimal variation in particle size for
M-DDGS compare to R-DDGS. Thus, M-DDGS could be more stable over varying storage and handling conditions
and show little changes in flow properties than R-DDGS. The wall friction angle range for R-DDGS (29.98° to
31.13°) was higher than M-DDGS (27.77° to 28.46°). Higher the wall friction angle slightly better the flow will be.
Smaller the particle more is the packing with consolidation pressure and time, and thus less free flowing
characteristics observed. In summary, moisture, RH, consolidation pressure and time showed predominant
interaction effects for both R-DDGS and M-DDGS.
Objective 2
Outcomes/Impacts
M-DDGS showed slightly higher tendency towards potential flow problems than R-DDGS due to small particle size.
However, M-DDGS samples had less variation in flow properties when stored in different RH, temperature,
moisture content, and consolidation conditions. Higher resistance to air flow and permeability was observed in MDDGS than R-DDGS, indicating cohesiveness between particles. Overall, this research will help identify the
characteristics that influence the flow property of M-DDGS and help develop an effective handling strategy.
Publications
Bhadra, R., Ambrose, R.P.K. and Casada, M. 2014. Comparison of flow and physical properties of Modified DDGS
with Regular DDGS under varying storage conditions. ASABE Annual International Meeting (Submitted).
Andersons Research Grant Program Team Competition.
Objective 2
Figures and Tables
Figure 1. Particle size distribution of regular DDGS (R-DDGS) and modified DDGS (M-DDGS)
10% moisture content (w.b.)
12% moisture content (w.b.)
Figure 2. Air flow resistance plots for modified and regular DDGS conditioned as 20° C and 40% RH for 10% and
12% (d.b.) moisture contents
Objective 2
Table 1: Physical and chemical property analyses for regular and modified DDGS samples as received from plant.
Regular
DDGS
Modified
DDGS
Moisture
content (%,
w.b.)
8.63
Bulk
Density
(kg/m3)
442.41
Porosity
(%)
Crude Protein
(%, db)
Fat (%,
db)
66.91
29.29
9.47
11.56
488.30
62.15
34.47
5.36
Table 2. Main effects due to moisture (%, db), RH (%), temperature (°C), consolidation pressure (kPa), and
consolidation time (days) on flow properties of regular DDGS.*
Parameter
Condition SI
BFE
FRI
SE (mJ) CBD
Cohesion ffc
Wall
Evaluated
(mJ)
(g/ml)
friction
angle (°)
Moisture
8
1.29c
571.58c
1.62c
1.53c
0.48a
0.24c
7.54a
30.66b
b
b
b
b
a
b
b
content (%
10
1.34
576.34
1.71
1.60
0.48
0.52
6.93
30.58b
a
a
a
a
b
c
c
w.b.)
12
1.36
580.16
2.09
1.74
0.46
0.64
6.18
31.08a
0.03
1.49
0.10
0.00
0.00
0.00
0.73
1.44
1.622ab
Relative
40
1.33a
0.47ab
1.81a
6.91a
30.77a
575.96a
0.46a
Humidity
1.52b
60
1.36a
0.45a
1.81a
6.88a
30.61a
575.89a
0.46a
b
a
b
a
a
a
a
(%)
1.63
80
1.32
0.47
1.81
6.91
30.92a
576.22
0.46
0.00
1.49
0.11
0.00
0.00
0.00
0.73
1.44
1.62ab
Temperature 20
1.34a
0.47ab
1.80a
6.91a
30.77a
575.96a
0.46a
(°C)
1.62b
40
1.33a
0.48a
1.80a
6.88a
30.61a
575.89a
0.46a
b
a
a
a
a
a
a
1.63
60
1.32
0.47
1.81
6.91
30.92a
576.22
0.46
0.00
1.49
0.11
0.09
0.09
0.00
0.73
1.44
575.75b
Conditioning 0
1.79b
1.61b
0.47b
0.46b
7.09a
30.98a
1.33a
Pressure
575.87b
10
1.77b
1.61b
0.48a
0.46b
6.86b
30.94a
1.33a
a
a
a
a
a
b
a
(kPa)
576.45
20
1.85
1.64
0.47
0.47
6.75
30.38b
1.33
0.00
1.49
0.10
0.00
0.00
0.00
0.73
1.44
Conditioning 2
1.33b
575.45c
1.77b
1.61b
0.47b
0.46b
7.11a
31.05a
Time (days) 4
1.33b
576.03b
1.80b
1.61b
0.47c
0.46b
6.99a
30.92a
a
b
b
b
a
b
a
6
1.34
575.97
1.77
1.62
0.47
0.46
6.92
31.13a
a
a
a
a
b
a
b
8
1.33
576.65
1.88
1.65
0.47
0.49
6.56
29.98b
0.00
1.49
0.11
0.00
0.00
0.00
0.73
1.44
*values in colored cells are +/- 1 standard deviation. Bold font indicates that there is no significant difference among
levels of the given independent variables for LSD test at α = 0.05. SI – stability index; BFE – basic flowability
energy; FRI – flow rate index; SE – specific energy; CBD – Conditioned bulk density; ffc – flow function.
Objective 2
Table 3. Main effects due to moisture (%, db), RH (%), temperature (°C), consolidation pressure (kPa), and
consolidation time (days) on flow properties of modified DDGS.*
Parameter
Evaluated
Condition
SI
BFE
(mJ)
FRI
SE (mJ)
CBD
(g/ml)
Cohesion
ffc
Wall
friction
angle (°)
c
a
a
0.37
Moisture
8
1.28c
709.54c
2.01c
5.27b
5.80
27.77c
0.48
b
b
b
b
b
b
a
content (%
0.62
10
1.30
715.47
3.01
5.38
5.16
28.48a
0.48
a
a
a
a
a
c
a
w.b.)
0.86
12
1.35
721.09
4.02
5.76
3.64
28.12b
0.48
0.01
1.18
0.09
0.22
0.22
0.03
0.44
1.13
c
a
b
a
a
a
a
2.25
0.48
Relative
40
0.61
715.25
28.45a
1.31
5.43
4.87
b
a
b
a
a
a
a
Humidity
3.21
0.48
60
0.61
715.39
28.03a
1.31
5.48
4.87
a
b
a
a
a
a
a
(%)
4.00
0.47
80
0.62
715.47
28.09a
1.31
5.44
4.86
0.01
1.18
0.09
0.22
0.00
0.03
0.44
1.13
c
a
a
a
a
a
0.51
Temperature 20
1.31a
2.51
5.43
0.48
28.45a
715.25
4.87
a
b
a
a
a
a
a
(°C)
0.65
40
1.31
3.28
5.43
0.48
28.03a
715.39
4.87
b
a
a
a
a
a
a
0.70
60
1.31
4.16
5.47
0.47
28.09a
715.47
4.86
0.00
1.18
0.00
0.22
0.00
0.03
0.44
1.13
b
b
a
b
a
b
a
ab
2.01
28.06
Conditioning 0
5.38
0.48
714.95
4.78
1.30
0.60
a
b
ab
a
b
a
a
Pressure
2.01
28.46a
10
5.38
0.48
715.20
4.92
1.30
0.61
b
a
b
a
ab
a
a
(kPa)
3.36
27.84b
20
5.59
0.47
715.96
4.90
1.31
0.63
0.00
1.18
0.09
0.22
0.00
0.03
0.44
0.22
c
ab
b
a
a
b
5.32
Conditioning 2
1.31a
714.94b
0.48
0.60
4.95
28.37
2.01
b
b
cb
a
b
a
ab
b
Time (days) 4
5.37
1.31
714.91
0.48
0.60
4.91
28.16
2.01
b
c
b
a
b
5.4
6
1.31b
715.01b
0.47
0.60
4.89
27.99b
3.57
a
a
a
a
bc
a
b
8
1.32
716.61
4.21
5.7
0.47
0.64
4.72
27.96b
0.00
1.18
0.09
0.22
0.00
0.03
0.44
1.13
*values in colored cells are +/- 1 standard deviation. Bold font indicates that there is no significant difference among
levels of the given independent variables for LSD test at α = 0.05. SI – stability index; BFE – basic flowability
energy; FRI – flow rate index; SE – specific energy; CBD – Conditioned bulk density; ffc – flow function.
Objective 2
Title
Laboratory and Field Data for Pack Factor Determination.
By
Montross, M.D., The University of Kentucky
McNeill, S.G.
Thompson, S.A., The University of Georgia
Casada, M.E., USDA-ARS, Manhattan, Kansas
Boac, J.M., Kansas State University
Bhadra, R.
Maghirang, R.G.
Outputs
Stored-grain packing is defined as the increase in grain bulk density caused by the cumulative weight of overbearing
material on the compressible grain products. As material is added, the vertical pressure increases in an exponential
manner with grain height. Bin geometry, material properties, and numerous other variables influence packing,
therefore, these factors were considered when developing the new packing model and conducting subsequent model
validation exercises. One goal of this scientific approach was to reduce the total amount of data required to achieve
accurate packing estimates over the range of bin sizes and various storage conditions encountered in the grain
industry.
In this study Hard Red Winter (HRW) wheat was intensively studied to evaluate the effects of blending, growing
season, and dockage using 27 untreated HRW wheat samples from Idaho, Montana, South Dakota, Colorado,
Oklahoma, North Dakota, Texas, Kansas, Nebraska, Washington, and Oregon. These were primarily from the 2007
through the 2010 crop years with a test weight (TW) that ranged between 52.9 and 64.3 lb/bu. The samples were
tested at two moisture content (MC) levels, nominally 10% and 13%, that would represent the range in expected
moisture contents for HRW wheat. Moisture content and test weight accounted for the majority of the variation in
the compacted bulk density (r2 > 0.96) over the 27 samples and two moisture content levels.
Although the packing behavior of the wheat samples was accurately described by test weight and moisture content
there were significant differences in the compressibility of individual samples. For example, one sample with a test
weight 54.4 lb/bu and a moisture content of 9.3% increased to 59.9 lb/bu when subjected to an overburden pressure
of 10 psi. This is an increase of 10.1% in bulk density. Compared to a sample with a high test weight (63.3 lb/bu at a
moisture content of 10.2%), the bulk density increased to 67.8 lb/bu at an overburden pressure of 10 psi, an increase
of 7.1% in bulk density.
The composites were combined to represent possible blending conditions that might be applicable to the grain
industry according to location, growing season, and variety. There were a total of ten composite samples made from
the 27 single variety samples. The packing of the composite samples could be predicted based on the regressions
derived for the 27 individual varieties (r2 of 0.95) using moisture content and test weight as the only variables
(Figure 1).
Objective 2
72
70
Predicted Density (lb/bu)
68
66
64
62
60
58
56
54
52
52
54
56
58
60
62
64
66
68
70
72
Measured Density (lb/bu)
Figure 1. Bulk density of composite samples predicted from only TW and MC (R2 = 0.95)
The effect of dockage on the compressibility of the HRWW composites was determined at levels of 0%, 1%, and
5% and two moisture content levels. Dockage reduced the test weight. For one representative sample, the test weight
was 61.7, 58.7, and 52.9 lb/bu when the dockage was 0%, 1%, and 5%, respectively. Packing increased when the
dockage level increased. At an overburden pressure of 10 psi, the packing was 6.5%, 8.6%, and 13.2% for dockage
levels of 0%, 1%, and 5%, respectively.
Knowledge of the test weight, moisture content, and dockage are required to accurately determine packing.
Obtaining representative samples from grain in storage can be problematic due to bin entry and worker safety.
Representative samples can more readily be obtained while loading a grain bin by following recommended sampling
procedures for trucks or, if possible, by using a diverter-type sampler on the stream of grain being loaded.
The computer model was validated by comparing the predicted pack factor and mass of the stored grain to actual
values as determined through field measurements of grain inventory in bins and piles. Comparing the model output
to field measurements allowed other physical parameters used in the model to be further assessed, such as wall
friction, grain-on-grain friction, and the lateral to vertical pressure ratio. Over 200 bins were measured in the field.
The actual mass of grain in each elevator and farm bin was determined from scale tickets provided by cooperators,
additionally, the cooperators provided data on grain material properties, such as TW and MC. The details of the bin
geometry and grain surface profile were measured by the investigators using a LEICO DISTO D8 laser distance and
angle meter (Leica Geosystems AG, St. Gallen, Switzerland), occasionally assisted by standard tape measures. On
regularly-shaped conic surfaces seven or more profile points were taken to determine the grain surface profile. A
more irregularly-shaped grain surfaces was measured using more points to determine the surface profile. The
irregular-shaped grain surfaces occur in larger diameter bins that are emptied using a side draw. In the Eastern US,
the LEICO DISTO D8 laser was used to map the grain surface to account for any irregularities. The surface maps
consisted of hundreds of points taken along the grain to get an average angle of repose, as well as, determine the
exact height the grain. To find the total amount of grain stored in a bin, the integral of the mapped surface was taken,
to find the volume, and that volume was multiplied by the TW of the grain. The total amount of grain stored within
the bin was determined by multiplying the average TW by the total volume of grain. A “calculated packing factor”
Objective 2
was determined as the ratio of total mass from scale measurements to mass calculated from the grain volume and
TW.
Outcomes/Impacts
Alternative methods to determine the volume of grain in a bin have been established. This allows for accurate
quantification of the surface profile in a bin. Currently the limitation is the surface has to be seen from the manhole.
For bins measured in the Eastern U.S. containing SRW wheat, the scale ticket data was often only available on a
whole facility basis, or based on a differential between multiple measurements, because many cooperators did not
track scale tickets by individual bins or they blended multiple bins when selling the grain. Figure 2 shows the
WPACKING model-predicted mass plotted against the actual mass based on scale ticket data. The model tended to
under-predict packing for SRW wheat with an average error between the observed and predicted masses of –3.25%
(standard deviation = 0.06).
SRW Wheat
Figure 2. Predicted versus actual inventory for SRW wheat for combined bins.
For corn, packing factors were measured in steel bins with diameters ranging from 12 to 105 ft (3.6 to 32 m) and
eave heights ranging from 14.5 to 91 ft (4.4 to 28 m) for those bins. For corn bins, the maximum difference between
the WPACKING program predicted mass and the actual reported mass was –2.75% with a median of –0.35%. For
about 70% of the corn bins the WPACKING program under predicted the mass of grain in comparison with actual
mass reported. However, the maximum percent differences between RMA predicted and reported mass of grain for
corn were -8.60% (median of 2.02%) and 25.92% (median of 8.69%), respectively. Thus, indicating that the current
WPACKING program provided better predicted mass values than the existing RMA procedure. Commercial bins
(both from farm sites and elevators) were measured in Central, Midwest, and Southern regions of the U.S. to
improve the overall robustness of the compaction factor prediction results. Figure 3 shows the WPACKING modelpredicted mass plotted against the actual mass based on scale ticket data. The model tended to under-predict packing
for corn in the Eastern U.S. with an average error between the observed and predicted masses of –1.03% (standard
deviation = 0.07).
Objective 2
Figure 3. Predicted versus actual inventory for corn for combined bins.
Objective 2
Title
Hyperspectral Imaging Methodology to Measure Fungal Growth and Aflatoxin in Corn.
By
Yao, H., Geosystems Research Institute/ Mississippi Agricultural & Forestry Experiment Station
Mississippi State University
Outcomes/Impacts
Summary: The focus is on the development of rapid, non-destructive technologies for fungal infection and aflatoxin
detection in grains.
Situation: Aflatoxin is a naturally occurring toxin, found in grain crops and products. It is regarded as one of the
most important food safety problems in the world. Since aflatoxin is an important food safety concern when present
in food and feed products, many countries have developed regulations to manage aflatoxin in food production. For
example, in the U.S., aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA).
The general guidelines are 20 ppb (parts per billion) for human consumption and 100 ppb for feed. Aflatoxin
contamination in corn is of particular concern for the food industry as corn is one of the main components in food
and feed worldwide. In the U.S., the aflatoxin level in corn is monitored throughout the production chain including
in-field sampling, grain storage, elevators, grain inspection stations, as well as export ports. Badly contaminated
corn is rejected resulting in immediate loss to the producers. The current approved methods for aflatoxin screening
are costly chemical-based analytical methods such as high performance liquid chromatography (HPLC) or thin-layer
chromatography (TLC).
Response: Corn kernels infected with aflatoxin producing and non-producing Aspergillus flavus were collected from
field and lab experiments. The infected kernels were imaged with a tabletop hyperspectral scanning imaging system.
In the meantime, the actual aflatoxin content was determined using a destructive approach for reference purpose.
Image processing algorithms are developed to do rapid fungal infection and aflatoxin contamination detection.
Impact: Corn contaminated with toxigenic strains of A. flavus can result in great losses to the agricultural industry
and pose threats to public health. The research effort will provide a rapid, non-destructive method for screening corn
at elevators or grain collection points, identifying and diverting contaminated grain into alternative uses, thereby
protecting the food supply and increasing producer profitability. Results from the current study enhanced the
potential of fluorescence hyperspectral imaging for the detection of fungal infected and aflatoxin contaminated corn.
Publications
Peer Reviewed Journal Articles
Yao, H., Z. Hruska, R. Kincaid, R. L. Brown, D. Bhatnagar, and T. E. Cleveland. 2013. Hyperspectral image
classification and development of fluorescence index for single corn kernels infected with Aspergillus
flavus. Transactions of ASABE, 2013, 56(5): 1977-1988.
Yao, H., Z. Hruska, R. Kincaid, R. L. Brown, D. Bhatnagar, and T. E. Cleveland. 2013. Detecting corn inoculated
with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosystems
Engineering, 115:125-135
Objective 2
Robert L. Brown, Abebe Menkir, Zhi-Yuan Chen, Deepak Bhatnagar, Jiujiang Yu, Haibo Yao, Thomas E.
Cleveland. 2013. Breeding Aflatoxin Resistant Maize Lines Using Recent Advances in Technologies.
Journal of Food Additives and Contaminants. 30(8): 1382-1391.
Hruska, Z., H. Yao, R. Kincaid, Dawn D., R. L. Brown, T. E. Cleveland, and D. Bhatnagar. 2013. Fluorescence
Imaging Spectroscopy (FIS) for Comparing Spectra from Corn Ears Naturally and Artificially Infected with
Aflatoxin Producing Fungus. Journal of Food Science. 78: T1313–T1320.
Book Chapters
Yao H. and Y. Huang, Remote Sensing Applications for Precision Farming, Chapter 18 in “Remote Sensing of
Natural Resources”, edited by Dr. Wang and Dr. Wen, published by CRC Press, 2013. ISBN-10:
1466556927
Patents
Yao, H., Z. Hruska, R. Kincaid, R. L. Brown, T. E. Cleveland. Method and Detection System for Detection of
Aflatoxin in Corn with Fluorescence Spectra. 10/22/2013. U.S. Patent No. 8,563,934
Peer Reviewed Proceedings
Samiappan, S., Bruce, L.M., Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D., and Cleveland, T.E.
Support Vector Machines Classification of Fluorescence Hyperspectral Image for Detection of Aflatoxin in
Corn Kernels. Proceedings of IEEE 5th Workshop on Hyperspectral Image and Signal Processing:
Evolution in Remote Sensing Conference. June 25-28, 2013, Gainesville FL
Lee, M. A., Huang, Y., Yao, H., Bruce, L. M. Determining Optimal Storage of Field Sampled Cotton Leaves for
Hyperspectral Analysis, Proceedings of IEEE 5th Workshop on Hyperspectral Image and Signal
Processing: Evolution in Remote Sensing Conference. June 25-28, 2013, Gainesville FL
Conference Articles
Yao, H., Kincaid, R., Hruska, Z., Brown, R.L., Bhatnagar, D., and Cleveland, T.E. 3-D Surface Scan of Biological
Samples with a Push-broom Imaging Spectrometer. International Symposium on Photoelectronic Detection
and Imaging: Imaging Spectrometer Technologies and Applications, Proceedings of SPIE Vol. 8910,
891027. June 25-27, 2013, Beijing, China
Yao, H., Kincaid, R., Hruska, Z., Brown, R.L., Bhatnagar, D., and Cleveland, T.E. 2013. Hyperspectral Imaging
System for Whole Corn Ear Surface Inspection. Proceedings of 2013 SPIE Conference, “Sensing for
Agriculture and Food Quality and Safety V”, 8721-17, April 30- May 1, 2013, Baltimore, MD
Conference Abstracts
Yao, H., Kincaid, R., Hruska, Z., Brown, R.L., Bhatnagar, D., and Cleveland, T.E. Huang, Y. 2013. Using
Hyperspectral Imaging for Assessment of Fungal Infection and Aflatoxin Contamination on Whole Corn
Ears. ASABE Annual Conference, 07/22-25/2013, Kansas City, MO.
Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D., and Cleveland, T.E. Fluorescence Hyperspectral
Imaging for Detection of Aflatoxin in Maize, International Aflatoxin-in-Maize Working Group “Global
Solutions for a Worldwide Problem”, May 14-16, 2013, New Orleans, LA
Objective 2
Yao, H., Hruska, Z., Kincaid, R., Brown, R. Toward Rapid Aflatoxin Detection in Single Maize Ear with Imaging
Spectroscopy. Grand Challenge Exploration: Agricultural Development and Nutrition, GCE Phase I
Grantee Meeting, Bill & Melinda Gates Foundation, Seattle, WA, March 13-15, 2013.
PI: Loren Wes Burger. Source: USDA Specific Cooperative Agreement
Title: Development of rapid, non-destructive hyperspectral imaging methodology to measure fungal growth and
aflatoxin in corn. Amount: $325,298
PI: Haibo Yao. Source: Bill & Melinda Gates Foundation, Grand Challenges Explorations Round 8:
Title: Development of Portable Technology for Rapid Aflatoxin Detection. Amount: $100,000
PI: Haibo Yao. Source: Peanut and Mycotoxin Innovation Lab/USAID
Title: AflaGoggles for Screening Aflatoxin Contamination in Maize. Amount: $204,393
Objective 2
Title
Nanostructured Products for Management of Insects in Stored Grain.
By
Weaver, D.K., Montana State University
Buteler, M.
Outputs
Nanostructured alumina is a novel type of insect control using very fine particles to have greater efficacy than a
conventional, commercially available inert dust for use against stored products insect pests. A time series of
nanostructured particle adherence an exposed adult of the sawtoothed grain beetle is shown in Figure 1. Four
different methods of synthesis that yield nanostructured alumina were compared for efficiency. Particle size and
other physical attributes were measured. The different products were also used in bioassays in stored grain with
Rhyzopertha dominica and Sitophilus oryzae. Mortality data was collected at three day intervals and the results were
analyzed.
Figure 1.SEM of nanostructured
alumina particles adhering to the head
of a sawtoothed grain beetle adult
over a 30 minute interval. Photo
courtesy of Teodoro Stadler,
collaborator and MSU Affiliate
Professor.
Also:
A training session was held for certified crop advisors and on-farm storage managers at the Crop Pest Management
School in Bozeman, Montana on January 2, 2013.
A popular press article entitled “Preserving Quality of Stored Grain” was delivered via the.Montana Farm Bureau
Ag NewsWire on August 29.
A stored product pest management module was presented to AGSC 401 – IPM students on the Montana State
University Campus on October 31, 2013.
Objective 2
An invited presentation, “Nanostructured materials and their potential as pest control tools” was given in the
Member Symposium: Stored Product Entomology: Impacts on a Connected World at the 61st Annual Meeting of the
Entomological Society of America, November 10-13, Austin, TX.
Outcomes/Impacts
Preliminary results indicate that several methods of synthesis yielded particles that increased mortality in adults of
both species at high relative humidity, relative to the commercially available product. This result was probably due
to the size, shape and consistency of the particles produced.
Having an inert dust that had inherently higher capability to kill storage insects under conditions of high humidity
would be an advantage to those responsible for the safe storage of grain. Inert dusts are an alternative to
conventional grain protectants and may be more active than other inert dusts. And remain so for an extended period
of time.
Publications
Throne, J. E. and D. K Weaver. 2013. Life history parameters of adult Angoumois grain moths (Lepidoptera:
Gelechiidae) on stored corn. Journal of Stored Products Research. 55: 128-133
Objective 2
Title
Identify Grain Attributes that Relate to Whole-wheat Pasta Quality.
By
Manthey, F.A., North Dakota State University
Deng, L.
Elias, E.M.
Outputs
Grain from 40 durum wheat cultivars grown at four locations in North Dakota was used to identify grain attributes
that relate to whole-wheat pasta quality. Grain attributes include test weight, kernel vitreousness, kernel weight, and
kernel size. Kernel protein content and quality and ash content were also determined. Whole-wheat quality was
assessed by determining physical quality (pasta color and mechanical strength) and cooking quality (cook time,
cooked weight, cooking loss, and cooked firmness).
A poster was presented at the annual AACC International meeting, Albuquerque, NM, September 29-October 2,
2013.
Outcomes/Impacts
Preliminary results indicate that the physical and cooking qualities of whole-wheat pasta varied with genotype and
that the grain quality requirements for whole-wheat pasta may not be the same as those for pasta made from
semolina. These results need to be confirmed by additional testing.
Different grain quality requirements between pasta made with semolina and pasta made with whole-wheat would
create a demand for wider range of durum wheat quality. This would help maintain the price that durum producers
receive at the elevator. Discounts for imperfections in the crop would be lessened as there would be a demand for a
greater range in crop quality.
Publications
Deng, L., and Manthey, F.A. 2013. Effect of durum cultivar and mill configuration on the textural and cooking
quality of whole-wheat pasta.
http://www.aaccnet.org/meetings/Documents/2013Abstracts/2013Pab173.htm
North Dakota Wheat Commission, $20,000.
North Dakota Wheat Commission.
Objective 2
Title
Effectiveness and Profitability of Alternative Insect Control Strategies, and Evaluation of Alternative Supply Chain
Management and Traceability Technologies.
By
Adam, B., Oklahoma State University
Outputs
We continued to develop a whole-chain traceability system with the unique feature that those who add information
into the system can choose which parties in the supply chain are able to see that information, and what specific
pieces of the information those parties can see. This provides confidentiality for those providing information,
removing a major obstacle to voluntary participation in such systems. While the system is being developed initially
for use in the beef supply chain, it can easily be adapted for use in other product supply chains. (Thus, although
some of the publications and presentations identified below refer to beef – the focus of a major part of the initial
funding – the technology is applicable to many food products, including those that are especially targeted in NC213.)
One of our collaborators is a company (Top 10 Produce) that has developed technology to facilitate communication
in a short supply chain between producer and consumer. Its currently-operating commercial system provides
producer/production information directly to consumers with a computer or smartphone, and permits consumers to
ask questions or provide feedback directly to producers. By collaborating, we can build a whole-chain traceability
for long supply chains that allows participants to selectively share information all the way from producer to
consumer.
Publications
Adam, Brian D., Mike Buser, Blayne Mayfield, Johnson Thomas, Philip Crandall, Steve Ricke, Ashwin Kumar, and
Krishna Palepu. "Demonstration of a Whole-Chain Traceability System that Protects Confidential
Information." Invited Keynote Presentation at the Arkansas Association for Food Protection (AAFP)
Conference, Fayetteville, AR, September 10-11, 2013.
Adam, Brian D., and Mike Buser. 2013. “Whole-Chain Traceability – Information Sharing from Farm to Fork and
Back Again.” Invited Presentation at the Mid Continental Association of Food and Drug Officials, Rogers,
Arkansas, February 26-27.
M.D. Buser, B.D. Adam, T.J. Bowser, B.E. Mayfield, J.P. Thomas, P.G. Crandall, and S.C. Ricke. 2013. “Concept
of a Stakeholder-Driven Whole-Chain Traceability System for Beef Cattle.” Presented at the Oklahoma
section of American Society for Agricultural and Biological Engineers, Stillwater, OK, February 22.
Outcomes/Impacts
Change in knowledge
The prototype traceability system that has been developed allows supply chain participants to select information that
they wish to share with other participants. This is a key feature for development of a complete traceability system
that provides benefits to participants that are greater than the costs.
Objective 2
Publications
Crandall, Philip G., Corliss A. O’Bryan, Dinesh Babu, Nathan Jarvis, Mike L. Davis, Michael Buser, Brian Adam,
John Marcy, and Steven C. Ricke. 2013. “Whole-chain traceability, is it possible to trace your hamburger to
a particular steer, a U. S. perspective?” Meat Science. 95(2):137-44.
Funded Grants
Ongoing during 2013:
1. “Establishing a national institute for whole chain traceability and food safety,” Oklahoma State University
Planning Grant for Establishing an Interdisciplinary Program, 8/31/2012-8/31/2013, $38,650, Co-PDs M. Buser and
Adam, with PIs: Cartmell, Naile, Sitton (Agricultural Education Communications and Leadership), Chung,
Holcomb, Peel (Agricultural Economics), Jaroni, Muriana (Animal Science), Fletcher, Ma (Entomology and Plant
Pathology), Bowser, Frazier, Jones, Mao, Weckler, Wang (Biosystems and Agricultural Engineering),
Brandenberger (Horticulture and Landscape Architecture), Mayfield, Thomas (Computer Science), Hoff (Molecular
Biology & Molecular Genetics), Caniglia, Long (Sociology), Ingalls, Liu, Kamath, Bukkapatnam (Industrial
Engineering), Snider (Center for Veterinary Health Sciences).
2. “Encouraging Small Farms to Adopt Produce Traceability Technology Through Creation of Brand Value, with
Top 10 Produce,” USDA-NIFA-SBIR, 6/1/2012-12/31/2012, with no-cost extension until 12/31/2013, $100,000. PIs
Bailey, Adam, and Chung.
3. “Advancement of a whole-chain, stakeholder driven traceability system for agricultural commodities: beef cattle
pilot demonstration.” USDA/NIFSI, 9/1/2011-8/31/2014, $543,000 with Noble Foundation, U. of Arkansas, and
Pardalis, Inc. PIs Buser, Adam, Mayfield, Thomas, Bowser, Ricke, and Crandall.
4. “Implementing and Evaluating Traceability Technology in Wheat Storage and Handling.” Anderson’s Research
Grant Proposal Competition, Administered by NC-213 Regional Research Project. 2/1/2010-1/31/2012 (1-yr no-cost
extension to 1/31/2013). $50,000. PIs Brian D. Adam (Agricultural Economics), Carol Jones (Biosystems and
Agricultural Engineering), and David Biros (Management Science and Information Systems). Grant was matched by
Dept. of Management Science and Information Systems of Oklahoma State University, totaling $100,000.
Leverage: An Andersons grant (#4 above) was used as partial leverage for funded grants #1, 2, and 3 above.
Evaluation of Integrated Pest Management (IPM) in Controlling Stored Product Insects
Outputs:
Research by B.D. Adam and Suling Duan (graduate student), along with Frank Arthur and James Campbell
(USDA/ARS) on the topic “Optimal insect control for grain storage in warm climates: Can chemicals ever be
avoided?”
Abstract: Elevator managers in the Southern and Central Plains have been reluctant to switch from routine
fumigation to sampling-based IPM to control insects in stored wheat. Research has suggested that their reluctance is
justified: under typical conditions the insects grow enough in the warm climate that fumigation is almost always
Objective 2
necessary: sampling adds unnecessary expense. This study, though, better models actual weather variability faced by
managers by simulating over a much longer data series. This makes sampling-based IPM relatively more
economical since greater variability increases the likelihood fumigation will not be necessary in any particular year.
This result holds, though, only if insect immigration rates into the bins are low, a condition that may or may not be
under the control of managers.
Research by B.D. Adam and Li Niu (graduate student), along with Frank Arthur, James Campbell, and Paul Flinn
(USDA/ARS) on the topic “A GIS Approach to Measuring Economic Costs of Integrated Pest Management Tools in
Rice Processing Facilities.”
Abstract: Methyl bromide is a commonly used fumigant for controlling insects in food processing facilities.
However, it has been designated as an ozone depleter and is becoming less available and more costly. Integrated
pest management (IPM) is an alternative, and may additionally reduce insecticide resistance, improve worker safety,
and reduce environmental concerns and consumer concerns about pesticide residuals. However, little is known about
the costs and efficacy of IPM in food processing facilities. Here, we consider several IPM approaches and measure
both the treatment costs as well as the costs of failing to control insects for each approach.
Research by John Mann, B.D. Adam, and Frank Arthur (USDA/ARS) on the topic: “The Economics of Resistance
to Phosphine by Stored Product Insects.”
Abstract: The primary motivation for this study is that recently resistance by Rhyzopertha dominica – Lesser Grain
Borer (LGB) – to phosphine in stored grain has been detected in parts of the US. Significant economic damage from
LGB resistance to phosphine has already occurred in countries such as Australia and Brazil. Currently there are no
economical alternatives to phosphine as a fumigant against stored grain pests.
The overall objective of this study was to determine how the costs of alternative strategies to control LGB in stored
grain are affected by LGB resistance. Three possibilities for LGB population dynamics based on genetic research,
and three grain management strategies (calendar-based fumigation, sampling-based IPM, and aeration-based IPM)
were considered. When costs associated with LGB resistance were incorporated, simulation results suggested that in
Oklahoma, where the weather is considered favorable to LGB growth, sampling-based IPM would only be costeffective if the development of LGB can be slowed to considerably less than what occurs under calendar-based
fumigation, perhaps through effective aeration.
In Kansas, where the weather is cooler, sampling-based IPM would be more likely to be cost-effective. Additionally,
aeration-based IPM would be the most cost-effective strategy since LGB growth could be suppressed sufficiently
that fumigation would seldom be necessary. Although these results only reflect the case of “low” immigration, they
justify further research into the application of different IPM technologies and the impact of such technologies on
LGB resistance and the corresponding costs from changes in resistance.
For grain managers, one symptom of increased insect resistance is the need for increased frequency of fumigation.
Ironically, one of the implications of the model is that if fumigations are done well so that effectiveness is high,
symptoms of increasing insect resistance may be initially over looked. In warmer climates where sampling-based
IPM is relatively more expensive, grain managers may be unaware of current levels of resistance. If the development
of LGB resistance is on the threshold, continuing to use current strategies may lead to significant economic loss.
Once past this threshold, the options for grain managers to make alternative strategy decisions are further reduced.
The main challenge is to extend the useful life of phosphine by developing and adopting strategies that can reduce
insect exposure to the fumigant.
Objective 2
Publications
Niu, Li, Brian D. Adam, James F. Campbell, and Frank H. Arthur. “A GIS Approach to Measuring Economic Costs
of Integrated Pest Management Tools in Rice Processing Facilities.” Selected Paper at the AAEA Annual
Meeting in Washington, DC, August 4-6, 2013. Available at http://purl.umn.edu/150456.
John T. Mann, II, Brian D. Adam, and Frank H. Arthur. “Stored Grain Insect Control Costs in Varying Climates
and Levels of Insect’s Phosphine Resistance.” Selected Paper at the AAEA Annual Meeting in
Washington, DC, August 4-6, 2013. Available at http://purl.umn.edu/150631.
Outcomes/Impacts
Change in knowledge
Optimal Insect Control Preliminary results of the research on optimal insect control in warm climates indicates that
under normal rates of insect immigration into storage bins, one treatment is always necessary in OKC, representing
the Southern Plains. The cost of the optimal strategy is the cost of one fumigation. One fumigation controls insect
growth sufficiently that no insect-damaged kernels (idk) discounts or live insect discounts result. This result holds
over the 29-year period as long as the one fumigation occurs within a 130-day window ranging from August to
January. The robustness of the results across application dates for calendar-based fumigation may partly explain
why many elevator managers have followed this approach.
A second preliminary result is that in Wichita, representing the Central Plains, if insect immigration rate can be
reduced to a low level, fumigation is almost never necessary. These two results are obtained using only one year of
weather data.
A third result, different from previous results because of the more complete consideration of weather variability, is
that in OKC, representing the Southern Plains, under low immigration rates, there are many years in which
fumigation is not necessary, even when grain is stored for the full 10 months and even though temperatures and
humidity are higher.
Insect Resistance The research on insect resistance to insecticides builds on previous research by incorporating the
most recent genetic research into an economic model. Costs of resistance have not been previously measured
explicitly. Considering these costs leads to a set of recommendations for reduced frequency of fumigation and
increased use of tools and methods that do not use phosphine fumigation.
Change in actions
Optimal Insect Control Results suggest that a sampling-based IPM approach, such as that recommended by
entomologists at USDA-ARS in which current weather information is combined with insect sampling and an expert
system for predicting insect growth, can be an economically attractive alternative to calendar-based fumigation if an
elevator manager can reduce insect immigration rates, perhaps by careful sanitation and sealing of storage structures.
Insect Resistance If subsequent research results are consistent with the findings reported here, grain storage
managers have increased incentive to conduct more intensive sampling programs to identify insect problems earlier.
Concurrently, they should intensify efforts to implement non-phosphine methods of insect control that they can use
to reduce the use of phosphine in order to extend its useful life in their storage facilities.
Objective 2
IPM in Rice Processing Facilities
The results will provide managers economic information to choose a better insect control method in their goal of
producing wholesome, pest-free and profitable products.
Publications
Gautam, S. G., G. P. Opit, K. L. Giles, and B. Adam. 2013. “Weight losses and germination failure caused by
psocids in different wheat varieties.” J. Economic Entomology 106(1):491-498.
Funded Grants
Ongoing:
1.“Evaluation of Methyl Bromide Alternatives for Their Efficacy at Controlling Pests of Dry Cured Ham and Aged
Cheese Products.” USDA-NIFA, 10/1/2011-9/30/2013, $500,000 ($60,228 to OSU) with Mississippi State
University and Kansas State University. PIs Schilling, Phillips, and Adam.
2. “Integrated Pest Management Programs to Reduce Reliance on Methyl Bromide Fumigation in Rice Mills.”
USDA/NIFA Integrated Programs, 9/1/2011-8/31/2014, $490,000 ($73,140 to OSU) with Arkansas State
University, USDA-ARS Manhattan, KS, Texas Agricultural Experiment Station, and Louisiana State University
Agricultural Center. PIs McKay, Arthur, Campbell, Adam, Wilson, Yang, and Reagan.
3. “Evaluation, Integration, and Implementation of Non-fumigation Based Pest Management Approaches for Food
Processing Facilities.” USDA/NIFA Integrated Programs, 9/15/2010-9/14/2013, $782,019, $100,000 to OSU. PIs
Zhu, Arthur, Subramanyam, Campbell, Flinn, Adam, and Jenson.
4. “Evaluation, Integration, and Implementation of Non-fumigation Based Pest Management Approaches for Food
Processing Facilities.” USDA/NIFA Integrated Programs, 9/15/2010-9/14/2013, $782,019, $100,000 to OSU. PIs
Zhu, Arthur, Subramanyam, Campbell, Flinn, Adam, and Jenson.
5. “Sitlington Enriched Graduate Scholarships,” to be used for recruiting graduate students. A competitive grant
funded by the Sitlington Endowment (Division of Agricultural Sciences and Natural Resources, OSU) in the amount
of $4,000/yr., plus $1,000 in research support, for three years 20012-2015; total $15,000. Project Leader Brian
Adam.
Funded Grants
New:
“Evaluation of New Strategies and Tactics to Manage Insect Pests in Mills,” USDA-NIFA-ICGP-004257,
10/1/2013-9/30/2016, $500,000 ($50,000 to OSU). PIs Kun Yan Zhu, Bhadriraju Subramanyam (Kansas State U),
Frank Arthur, James Campbell (ARS-USDA), Brian Adam (Oklahoma State U), and Ducatte (Kansas State U).
Objective 2
Title
Fungal Susceptibility Measurement using the Solvita® Grain CO2 Respiration Test.
By
Stroshine, R.L., Purdue University, ABE Department, West Lafayette, IN
Ileleji, K. L.
Pertiwi, C. (Grad Student)
Outputs
The objective of this study is to investigate the use of the Woods End Laboratories Solvita® Grain CO2 Respiration Test
Kit for determining the susceptibility of shelled corn to growth of fungi as a tool for reducing the risk of a loss in corn
quality. Three series of tests were conducted. In the first series, samples of shelled corn collected for grading during the
loading of rail cars by elevators were tested. These samples were collected from 29 shipments loaded between October
of 2011 and May of 2012. For each shipment, one sample was taken from each hopper car. The total number of samples
available from a single shipment varied between 4 and 60 cars. Between 2 and 4 samples (each ~300 g) were selected
from each shipment and tested by the ABE Department. First, the moisture content of the shelled corn was determined
using the whole kernel oven drying procedure (ASABE S352). De-ionized water was added to the samples to increase
their moisture content to 21%. After 24 hours of equilibration, 100 g subsamples were placed in air-tight jars. After an
additional 47 hours, which was 71 hours after re-wetting, the lids were removed for one hour to allow the air in the jars
to equilibrate to the composition of the ambient air. Beginning at hour 72 and continuing through hour 78, the Solvita®
test kit was used to determine the CO2 content of the air in the jars. A small plastic “paddle” containing a strip of
indicator gel that changes color in response to the concentration of CO2 in the surrounding air was inserted into each jar.
The indicator gel color was quantified by a number between 0 and 5.5 with higher color numbers indicating a higher
percentage of CO2 in the air inside the jar. Color numbers were determined by removing the paddle from the jar with
minimal disturbance of the air in the jar and inserting it into a Digital Color Reader. The paddle was then returned to the
jar.
Figure 1 shows the average color numbers at hours 72 to 78 for the samples from several shipments. The samples and
shipments were selected to demonstrate the range in fungal susceptibility encountered from low, where the increase in
color number versus time was relatively slow, to high, where the increase was relatively rapid. Figure 2 is a plot of the
average color number of the samples from each shipment for each hour after re-wetting. Shipments are plotted in
shipment number order. In general, corn lots with higher shipment numbers would have been in storage for longer
periods of time. The peaks and dips formed by connecting the individual color number averages indicate variability in
susceptibility to mold growth. Trend lines were fit to each hour’s readings and the slopes of the trend lines are shown
along with the line. The slopes for hours 74 through 78 are positive indicating that, for hours 74, 75, 76, 77, and 78, as
the sample number increased (an indication that the corn had probably been stored longer) the color number at that hour
tended to increase.
Objective 2
Sample 15
Sample 9
Sample 21
Sample 11
Sample 13
Sample 6
Figure 1. Plots of Color Number versus time for rewetted samples of shelled
corn taken from rail cars and incubated for 72 hours prior to testing.
0.0177 (Hr 78)
0.0218 (Hr 77)
0.0299 (Hr 76)
0.0324 (Hr 75)
0.0178 (Hr 74)
-0.0048 (Hr 73)
-0.0053 (Hr 72)
Figure 2 Color Number readings for samples from each of the 29 shipments
for hours 72 through 78. Linear trend lines were fit to the data and are also
shown along with the slopes of the trend lines.
The authors believe that the positive slopes are an indication that fungal susceptibility increases with time in storage.
They also believe that, if the corn in the shipments were exposed to conducive conditions, there would be more mold
growth in corn that is more susceptible.
Objective 2
In a second series of CO2 kit tests conducted in 2013, samples from 8 of the 29 shipments were re-tested. This was
approximately one year after the original tests were conducted. The entire sample was needed for the first test. That
meant that a sample from another rail car that was part of the same shipment had to be used for the re-test. Although
there could be differences in fungal susceptibility among samples from different cars in a unit train, in most tests
conducted, results for samples from different cars within the same shipment were
a)
b)
c)
Figure 3. Color number versus time for different samples from the same
corn shipment tested at the time of collection (2012) and six months to
one year after collection (2013).
relatively consistent. Readings at a given hour typically agreed within ±0.3. Furthermore, the grading factors for the
samples from the different cars in a given shipment were also reasonably consistent. The blending and mixing that
occurred during the loading of the rail cars apparently produced relatively consistent quality among most cars. In the one
year that elapsed between the two sets of tests, the samples were in plastic sandwich bags that had been placed in boxes
and kept in a laboratory at approximately 24°C (75°F). Moistures of the samples at the time they were first tested at
Purdue were between 14.4 and 15.4%. Even though the samples were in plastic bags, their moisture contents dropped to
between 11.3 and 13.32% during the year-long storage period. It is possible there was some mold growth before the
samples dried to a moisture at which mold could not grow. There may also have been some decline in the integrity of
cell and tissue membranes that serve as barriers to fungal invasion. Therefore, it was anticipated that the fungal
susceptibility of the 2013 samples would be greater than that of the 2012 samples. This would be manifest as a more
rapid increase in the color number versus time curves obtained using the Test Kits.
Objective 2
Plots from the 2012 and 2013 tests on shipments 3, 11, and 23 are shown in Figure 3. For samples 11 and 23 the
expected increase is evident but there was an apparent decrease for shipment 3. Closer examination of the results
revealed that one of the three replicates was much lower than the other two and that this caused a significant decrease in
the average. If the readings for this replicate were removed, the 2012 and 2013 curves were nearly identical, as shown in
Figure 3. There was one other shipment in which the 2013 curve was very similar to the 2012 curve. For the remaining 6
shipments (two of which are shown in Figure 3) the 2013 curves were well above the 2012 curves.
In a third experiment, samples were collected from Purdue farms during the fall of 2012. Two of the samples15% and
18%) were harvested by combine while the third was hand harvested on the ear. The ears were placed on a laboratory
bench for several days to dry and were then shelled in a laboratory sheller having rubber rollers (Agriculex SCS-2 Corn
Sheller manufactured in Canada). The moisture content of the shelled corn was 26%. All of the samples were placed in
a walk in freezer at -20°C for approximately 8 months. After they were removed from the freezer, they were placed in 5
gallon buckets at the moistures at which they came out of the freezer (15%, 18%, and 26%). Results of this third set of
tests are not yet available. Those results, along with more details of the studies summarized in this report, will be
included in a Master’s thesis currently being written by Ms. Cininta Pertiwi.
Outcomes/Impacts
We anticipate that grain stored on farms and in elevators could be tested using the 3-day CO2 kit tests as a means of
determining the risk of spoilage if the corn remains in storage. Corn that has a greater fungal susceptibility could be
utilized sooner before its quality deteriorates to an unacceptable level. Test results could also be used to identify lots
of shelled corn with relatively low fungal susceptibility whose quality would deteriorate more slowly if they were
shipped to tropical climates or subjected to conditions conducive to mold growth. We believe that use of the Test Kit
could reduce losses and improve the overall quality of shelled corn marketed in the United States or sold overseas.
Contacts
Richard Stroshine
Department of Agricultural and Biological Engineering,
Purdue University, 225 South University Street
West Lafayette, IN 47907
Phone: (765) 494-1192
Fax: (765) 496-1115
Emails: [email protected]
Objective 2
Title
Developing New Stored Grain Pack Factors.
By
Casada M.E., USDA-ARS-CGAHR, Manhattan, KS
Thompson, S.A., University of Georgia, Department of Biological and Agricultural Engineering
Maghirang, R.G., Kansas State University, Department of Biological and Agricultural Engineering
Bhadra, R.
Boac, J.M.
Montross, M.D., University of Kentucky, Department of Biosystems and Agricultural Engineering
McNeill, S.G.
Outputs
Storing grain in bulk storage units results in grain packing from overbearing pressure, which increases grain bulk
density and storage-unit capacity. Packing factors for grains at varying depths in all bin sizes and types are required
to determine the mass of grain in storage from bin volumetric measurements and test weights. Due to the increase in
storage capacity of a bin from packing, accurate packing factors are crucial for everyone in the industry who is
concerned with grain storage capacity and inventory control. This study has compared compaction factors of hard
red winter (HRW) wheat and shelled corn in vertical storage bins using three existing methods: a packing model
(WPACKING), the USDA Risk Management Agency (RMA) method, and the USDA Farm Service Agency (FSA)
warehouse group method. Concrete and steel bins containing HRW wheat were measured in Kansas, Oklahoma, and
Texas. Steel bins containing corn were measured in Kansas, Colorado, Iowa, Texas, and North Dakota.
For HRW wheat, packing has been measured in 35 bins of corrugated steel and reinforced concrete with diameters
ranging from 4.6 to 31.9 m and grain heights ranging from 2.1 to 42 m. The predicted mass values of compacted
stored wheat from the three methods were compared to the reported mass from scale tickets. The maximum and
median differences between the WPACKING model-predicted mass and reported mass were -4.7% and -1.3%,
respectively, for corrugated steel bins; and +9.7% and +2.2%, respectively, for reinforced concrete bins. In most
cases, the model under-predicted the mass in the corrugated steel bins and over-predicted mass in concrete bins. For
the existing RMA method, the range of difference was from -3.7% to +11% for steel bins and from -7.2% to +7.8%
for concrete bins. The RMA method median difference was +1.9% for steel bins and +1.0% for concrete bins. Most
of the data for steel bins (10 out of 19) and for concrete bins (26 out of 37) were over-predicted with the RMA
method. For the FSA method the range of difference was from -1.4% to +7.6% with a median of +3.9% for steel
bins and from -4.7% to +10.4% with a median of +3.5% for concrete bins. The average magnitude of the difference
was 1.6% for WPACKING, 4.4% for the RMA method, and 3.4% for the FSA method for steal bins and 3.7% for
WPACKING, 3.2% for the RMA method, and 4.3% for the FSA method for concrete bins containing HRW wheat.
Packing factors have been measured in approximately 50 steel bins for corn with diameters ranging from 3.6 to 32 m
and eave heights ranging from 4.4 to 27.7 m. For corn bins, the difference between WPACKING model-predicted
mass and reported mass ranged from -4.5% to +4.5% with a median of -0.32%. With about 70% of the corn bins, the
model under predicted the mass of grain in comparison with reported mass. For the existing RMA method, the
difference between predicted and reported mass of corn ranged from -2.7% to +5.0%, with a median of +0.90%. For
the FSA method the difference between predicted and reported mass ranged from -3.3% to +7.1% and the median
Objective 2
difference was +1.4%. The average magnitude of the difference was 1.8% for the FSA method, 1.6% for the RMA
method, and 0.8% for WPACKING for the corn bins.
Overall, the current WPACKING program provided better predicted mass values than the existing RMA and FSA
procedures with these bins. The current data will be used to calibrate the WPACKING model and further improve
the predictions. The third beta version of the new software was completed this year. Laboratory tests of grain
compressibility are being conducted at the University of Kentucky and are reported separately.
Outcomes/Impacts
These results will be incorporated into the final software package that can be used by farmers, elevator managers,
and government officials. The software will allow the user to enter: bin wall type, type of grain, height when full
(top of cone) or complete height data for randomly loaded flat storages, and hopper bottom angle if not flat, and
other variables for grain properties. The tool will calculate the average packing factor for the bin and will provide
accurate estimates of the confidence intervals for those pack factors. This accuracy and confidence-interval
information will be a significant improvement over old methods for which the errors are not known. In addition, the
new model should have better accuracy than the old method because it accounts for many important variables in
grain and bin properties that affect the final packing but were not taken into account by the old method. With these
expected improvements over the old methods, the new stored grain packing factor predictions from this work will
likely be adopted by government agencies and grain industry customers to enhance their operations.
Publications
Bhadra, R., J. M. Boac , M. Casada, S. Thompson, R. Maghirang, M. Montross, and S. McNeill. 2013. Field
measurements for food grain packing factors in the U.S. ASABE Paper No. 13-1621335. St. Joseph, Mich.:
ASABE.
McNeill, S. G., M. Casada, M. Montross, S. Thompson, R. G. Maghirang, and R. Bhadra. 2013. New models for
describing grain packing. ASABE Paper No.13-1620757. St. Joseph, Mich.: ASABE.
Montross, M., J.M. Boac, R. Bhadra, R.G. Maghirang, S. McNeill, M. Casada, and S.A. Thompson. 2013.
Equilibrium moisture content of hard red wheat varieties and composite samples. ASABE Paper No. 131619035. St. Joseph, Mich.: ASABE
Turner, A., C. Rodrigues, D. Schiavone, J. Jackson, M. Montross, S. McNeill, J.M. Boac, R. Bhadra, S. Thompson,
and M.E. Casada. 2013. Field Measurement of Packing in Stored Grain. ASABE Paper No. 13-1620198.
St. Joseph, Mich.: ASABE.
USDA-RMA
Objective 2
Title
Mechanistic Modeling of Grain Handling.
By
Casada M.E., USDA-ARS-CGAHR, Manhattan, Kansas
Maghirang, R.G., Kansas State University, Department of Biological and Agricultural Engineering
Boac, J.M.
Outputs
Computer simulations using the discrete element method (DEM) are capable of reducing the large effort and cost of
evaluating grain handling issues experimentally. However, adequate particle models have not been developed and
validated for most types of grain. Previously, we simulated bulk grain property tests for soybeans and corn kernels,
as well as simulated soybean commingling in a pilot-scale boot using discrete element method (DEM) models.
In the current study wheat was modeled using particles comprised of one to four overlapping spheres. Using these
models, bulk grain properties (angle of repose, bulk density, and hopper discharge flow) were simulated using DEM
with published data on material and interaction properties as inputs. The material properties were particle shape, size
distribution, Poisson’s ratio, shear modulus, and density. The interaction properties were particle coefficients of
restitution, static friction, and rolling friction. Predicted results were compared with published experimental data to
determine the most appropriate particle models for simulating bulk behavior of wheat kernels using DEM.
The tests showed that a single-sphere wheat particle model with shear modulus, G, that was within the range of
published values (G = 20 MPa), gave the most accurate values of angle of repose (AOR) and bulk density, while
maintaining correct hopper flow and relatively high computational speed in the DEM models. This wheat particle
model has coefficients of restitution of 0.80, static friction of 0.70 (for particle-particle contact) and 0.35 (for
particle-surface contact), and rolling friction of 0.50. The particle size distribution was normally distributed.
Modeling problems where grain is not free flowing and is subjected to severe compression may require the use of
particle models with fully realistic values of G. An alternative 1-sphere, high-G particle wheat model with G equal
to the mean of published values (G = 210.8 MPa) was developed for these special cases, but the computational times
were approximately 77% longer than for the first, low-G, model. Other modeling scenarios, where shape strongly
affects the modeling results, may require particles that characterize the wheat kernel shape more exactly. A third
model composed of three overlapping spheres was also developed for special cases requiring more a precise kernel
shape. This particle model also produced computational times much longer than for the first model. All three particle
models were effective in the tests considered (AOR, bulk density, and hopper flow) but the 1-sphere, low-G model
was preferable except for the special cases mentioned because of its faster computational times.
Objective 2
Outcomes/Impacts
These particle models are being used in DEM models of bucket elevator legs to determine best management
practices for reducing unwanted grain commingling. Results of this study will be used to accurately predict
commingling levels and improve grain handling, which can help farmers and grain handlers reduce costs and
maintain grain purity during transport and export of grain.
Publications
Boac, J. M., M. E. Casada, R. G. Maghirang, and J. P. Harner III. 2013. Particle models for discrete element
modeling of bulk grain properties of wheat kernels. ASABE Paper No. 13-1619002. St. Joseph, Mich.: ASABE.
USDA-ARS
Objective 3
To quantify and disseminate the impact of market-chain
technologies on providing high value, food-safe, and biosecure
grains for global markets and bioprocess industries.
Objective 3
Title
To be a Multi-Institutional Framework for the Creation of Measurable Impacts Generated by Improvements in the
Supply Chain that Maintain Quality, Increase Value, and Protect Food Safety/Security.
By
Shepherd, H.E., Iowa State University
Hardy, C.L.
Hurburgh, C.R.
Shaw, A.M.
Rippke, G.
Development on the International Center for Grain Operations and Processing, a non-profit entity, continues, with
additional partners and supporters added in 2013. The Center aims to serve as the primary education and applied
research partner to the global grain handling and commodity utilization industry. Globalization and rapid change in
the grain handling industry has provided the need for the Center.
University and private sector agribusinesses continued to utilize our NIRS-based grain component testing service,
with 10,529 samples of corn and soybeans submitted from the 2013 crop year. Especially the 2012 crop season had
extremes of early moisture, heat, drought and low humidity, which continued to create quality patterns outside of
previous experience. Soybean protein and oil were both somewhat above average so that soybean meal protein
remained high, and oil yields per bushel were high as well. Two examples are given in the test plot data from four
Iowa counties over the past five years. Each year had a different pattern of quality.
Table 1. Corn Quality 2009-2013 (Adair, Bremer, Black Hawk, and Palo Alto counties in Iowa)
Four plots per year, same locations each year, 20-50 hybrids per plot.
Objective 3
Table 2. Soybean Quality 2009-2013 (Adair, Bremer, Black Hawk, and Palo Alto counties in Iowa)
Four plots per year, same locations each year, 10-40 varieties per plot.
Seminars and training programs were done, covering the impact of 2013 weather on crop quality.
The second update of the grain and processing industry food safety preventive control template is posted on the
Iowa Grain Quality Initiative website.
The second training course for FDA inspectors was held in Raleigh, NC, on April 23-25, 2013 focusing on feed mill
inspections. There were 75 participants from the US and territories. Precourse distance education modules were
also added as shown in Table 3.
Table 3. Pre-course Distance Education Units for Feed Mill Inspectors
The training for inspections other than feed mills will be all on-line as shown in Table 4.
Objective 3
Table 4. Online Training for Non-Feed Mill Inspections
The Iowa Grain Quality Initiative Storage Team created its first web modules (3) and spreadsheets (1) for producer
training.
Dryeration – Shawn Shouse – ISU Extension – 2013
Grain Aeration- Greg Brenneman - ISU Extension - 2013
Fan Performance- Greg Brenneman - ISU Extension - 2013
Comparison of Drying Systems Calculator – Edwards – ISU Extension – 2013
Outcomes/Impacts
The International Center for Grain Operations and Processing serves as an industry- supported non-profit,
coordinating body. The Center provides a centralized point for training to meet the demographic changes of the U.S.
workforce in the face agricultural sector growth.
Objective 3
Producers and the grain industry received advance forecasts of crop quality and storability conditions for 2013.
Processors received advance estimates of product yields from both corn and soybeans.
As was the case in 2012, FDA inspectors were relatively unfamiliar with agriculture. The additional distance
education modules were very useful in providing background in advance of the on-site course.
Grain related industries were updated to the rulemaking process for the Food Safety Modernization Act. Practical
methods and actions for achieving compliance were created.
Publications
Many were given as part of workshop, conference, or other public events. These are the most recent or have the
most relevance.
Hurburgh, C. R. 2013. FSMA, and Food Safety in Bulk Agricultural Product Markets. Cornbelt Chapter, Grain
Elevator and Processing Society , Bloomington, IL. 1/7/2013. (100)
Hurburgh, C. R. 2013. New Forces Shaping the Future: Climate, Food Safety, Technology, Supply Chains…and
more. Annual Meeting of American Association of Grain Inspection & Weighing Agencies. Las Vegas,
NV. 5/5/2013. (75)
Iowa Grain Quality Initiative. 2013. Storage Team web modules (3) and spreadsheets (1) for producers.
http://www.extension.iastate.edu/Grain/Topics/GrainStorage.htm
Hurburgh, C. R.. 2013. Flexibility in Food Safety Inspection. Comments to FDA Open Meeting on Feed
Preventive Control Rules. November 25, 2013. Chicago, IL. (88)
http://www.fda.gov/Food/GuidanceRegulation/FSMA/default.htm
Shaw, Angela, C.R. Hurburgh, Heather Snyder, Howard Shepherd and Connie Hardy. 2013. Food Safety
Preventive Control Plan Checklist v2.1. Iowa State University Extension and Outreach, Iowa Grain
Quality Initiative, Department of Food Science and Human Nutrition.
http://www.extension.iastate.edu/Grain/Topics/fdarecordcompliance.htm
C.R. Hurburgh, Jr. Aflatoxin and quality issues in 2012. Presentation given at the NC-213 Annual Meeting,
February, 2013.
A.M. Shaw. Food Safety Modernization Act and Production Impact on Bulk Industry Suppliers. Rousselot Inc. &
Sonac USA, Denver, Colorado, April 2013.
Iowa Extension 21 Program.
Various Industry contracts and service fees.
National Institutes of Health / Food and Drug Administration.
Objective 3
Title
Creating Awareness on Grain Dust Explosion in Grain Handling and Processing Facilities through Worker Training.
By
Ambrose, K., Kansas State University
Maier, D.E.
Miller, B.
Campabadal, C.
Outputs
In the U.S., number of grain dust explosions greatly reduced by the implementation of OSHA’s Grain Handling
Facilities Standard in the year 1987.Extensive awareness programs on potential hazards during grain handling and
processing increased the knowledge of workers and supervisors. That standard focused on controlling grain fires,
grain dust explosions, and hazards associated with entry into bins, silos, and tanks. Among these, grain dust
explosions are considered the most severe hazard potentially causing loss of life and extensive property damage.
Larger corporate companies usually have an in-house safety training program. But, small industries do not have a
structured safety program to educate their workers on grain dust hazards. A lack of knowledge or awareness about
safety threats, viewing regulations as against their independence, outdated handling/processing methods are some of
the perceived obstacles to establishing a safety program in the work environment. Kansas State University has a
history and tradition of documenting dust explosions in the U.S. and in the past has led the education of the grain
processing industry on the perils of dust generation and explosion prevention. K-State has also published a DVD on
grain dust explosion which continues to be in great demand in the industry.
Through this awareness creation program, we are focusing educating the workers on practical risk information on
dust hazards and develop relevant educational materials to mitigate fatalities and loss in grain handling and
processing facilities. Furthermore, we cover the sources of dust generation, handling/conveying equipment
maintenance and their relation to dust generation, preventive maintenance in grain and feed handling and processing
facilities, OSHA regulations, and NFPA standards. We will also educate supervisors/managers on training their
workers and using best training practices to curtail the risk of dust explosion.
Outcomes/Impacts
Through a Susan Harwood Targeted Topic Training Grant (2012-2013), K-State has been creating awareness on
grain dust explosion across Kansas, Missouri, Texas, Nebraska, and Minnesota. K-State, through a Susan Harwood
Targeted Topic Grant, has been creating awareness on grain dust explosion to workers and supervisors. This
awareness programs are being received very well with good comments from the industry. In 2013, multiple training
programs were offered in conjunction with Grain Elevator And Processing Society (GEAPS) Exchange, local
GEAPS Chapter Meetings, safety meetings conducted by Kansas Grain and Feed Association (KGFA) and National
Grain and Feed Association (NGFA). So far we have trained 350 workers and supervisors from the U.S. grain and
feed handling and processing facilities.
Objective 3
Training on advanced methods of grain dust control within the grain handling and processing industry. Ambrose, K.,
Maier, D.E., Cook, H., Miller, B.M. & Campabadal, C. Susan Harwood Training Grant, Occupational Safety and
Health Administration (OSHA), Department of Labor. ($104,901).
Contacts
Kingsly Ambrose, Department of Grain Science and Industry, Kansas State University; Phone: 785-532-4091;
Fax:785-532-7010; e-mail: [email protected]
Objective 3
Title
GEAPS-KSU Grain Operations Distance Education and Professional Credentialing Program.
By
Maier, D.E., Professor & Head, Grain Science & Industry, Kansas State University
Miller, B., Distance Education Program Coordinator
Ambrose, K., Professor
Campabadal, C., IGP Program Specialist and Instructor in Feed Manufacturing and Grain Storage, International
Grains Program
Hurburgh, C.R., Professor, Agricultural & Biosystems Engineering, Iowa State University
Outputs
Kansas State University has continued to develop and support the GEAPS-K-State Grain Operations Distance
Education Program. The program has also launched a credentialing component, allowing industry professionals to
work through a series of six specific courses, providing them with an overview of the grain industry.
Outcomes/Impacts
The partnership continues to be a success, since inception, the program has had over 1900 participants from 27
countries (including Latin American countries) enroll in twenty courses offered seventy-one times with a completion
rate of 85%. In 2013, eighteen courses were offered between January and November including two new courses
covering grain dust explosion prevention and an introduction to grain operations. A course on facility planning and
design was also updated. In addition, three new courses are currently under development for offering in 2014 as well
as two updates to previously developed courses. Courses are also being translated in Spanish, with two offerings
scheduled in 2014 as well. Courses are continuing to be developed in collaboration with NC-213 experts from
Purdue University, Kansas State University, North Dakota State University, Oklahoma State University, Iowa State
University, USDA GIPSA, and USDA ARS.
Grain Elevator and Processing Society (GEAPS), Kansas State University.
Contacts
Dirk Maier, Department of Grain Science and Industry, Kansas State University; Phone: 785-532-6161; Fax: 785532-7010; e-mail: [email protected] - URL: http://www.grains.k-state.edu
Brandi Miller, Department of Grain Science and Industry, Kansas State University; Phone 785-532-4053; email:
[email protected]

2013 annual progress reports - OARDC

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