Document 6528735

2302548
Sample Preparation
for
Chemical Analysis
1
Analytical Perspective
Homogenization
Sample Reduction
Analytes Semivolatile
Volatile
Sample
Metals
ions
DNA/RNA
Solid
Liquid
Biological
Sa
m
Gas
pl
in
g
Aqueous
Semi-solid
Extraction
Information
Concentration
Clean-up
Preservation
Sample Preparation
Methods
• Accuracy
•
•
•
•
•
Precision
Cost
Qualitative
Labor
Quantitative
Time
Automation
Chromatography
Spectroscopy
Analysis
2
Common instrument methods and the necessary
sample preparation prior to analysis
Analytes
Sample preparation
Instrument
Organics
Extraction, concentration,
cleanup, derivatization
GC, HPLC, GC/MS, LC/MS
Volatile organics
Transfer to vapor phase,
concentration
GC, GC/MS
Metals
Extraction, (derivatization)
concentration, speciation
AA, GFAA, ICP, ICP/MS,
UV-VIS, IC
Ions
Extraction, concentration,
derivatization
UV-VIS, IC
DNA/RNA
Cell lysis, extraction, PCR
CE, UV-VIS, Fluorescence
Amino acids, fats,
carbohydrates
Extraction, cleanup
GC, HPLC, CE
Microstructures
Etching, polishing, reactive
ion techniques, ion
bombardments, etc.
SEM, microscopy, surface
spectroscopy
Mitra, S. editor, “Sample Preparation Techniques in Analytical Chemistry,” John Wiley & Sons, Inc. 2001. 3
Method of Quantitation
z Calibration
against chemical standards
z If there is extraction step involved:
measure recovery of spiked known
amount of analyte to the matrix
z Sample preparation is usually matrix
dependent
4
Calibration curves
Signal
• Matrix of the standard should be as close to the samples
as possible
• Standards of known concentrations should cover the
concentration range expected in the sample
LOD (3 x S/N)
Limit of linearity
LOQ (10 x S/N)
Analyte concentration
5
How to prepare calibration curve?
z
Pb in soil: Acid digestion and AAS
1.
Standards are prepared by spiking clean soil
with known amounts of Pb and taken
through entire process of digestion and
analysis
(more accurate)
2.
Standards are used to calibrate only the AA
(simpler)
6
Errors in Quantitative Analysis:
Accuracy and precision
Mean − True value
Accuracy =
True value
Measure of systematic error
Measure of reproducibility affected by random error
Σx i
x=
n
Σ(x i − x ) 2
σ=
n
Σ(x i − x ) 2
s=
n −1
Coefficient of variation (CV) or relative standard deviation (RSD)
RSD =
s
x
s
%RSD = × 100
x
7
Horwitz Curve
70
60
Sample preparation
accounts for
majority of the
variability
Alflatoxins
Relative Standard Deviation
50
40
Pesticides
Drug
residues
in feeds
30
20
Random and
systematic errors are
higher during sample
preparation than
during analysis
Pharmaceuticals
10
0
-10
Major
components
-20
-30
Minor
components
-40
Minimize the number
of steps
Trace
analysis
-50
-60
-70
1
1.E-02
1.E-04
1.E-06
1.E-08
1.E-10
Concentration
V. Meyer, LC-GC North Am., 20, 106-112, 2 (2002)
1.E-12
8
Sources of Error in Sample Preparation
and Analysis
Sample Processing
Operator
Contamination
Calibration
Chromatography
Instrumentation
Columns
Integration
Sample Introduction
Other
0
10
20
30
40
50
Percentage of Respondents
Ronald E. Majors, Trends in Sample Preparation, LC-GC, Vol. 14, No. 9, 1996
9
Number of Sample Preparation
Techniques Required per Sample
Average Number Respondents (%) Respondents (%)
(1996)
(2001)
1
11.1
19.3
2
28.4
28.6
29.1
3
20.7
4
12.0
15.0
5
6.5
8.6
6
12.9
2.1
7
0.7
>7
5.0
10
Time spent in analytical process
Sample
preparation
60%
Sample
collection
and data
handling
33%
Actual
measurement
7%
11
Merit for instruments or analytical methods
No. Parameter
Definition
1.
Accuracy
Deviation from true value
2.
Precision
Reproducibility of replicate measurements
3.
Sensitivity
Ability to discriminate between small differences in
concentration
4.
Detection limit
Lowest measurable concentration
5.
Linear dynamic range Linear range of the calibration curve
6.
Selectivity
Ability to distinguish the analyte from interferences
7.
Speed of analysis
Time needs for sample preparation and analysis
8.
Throughput
Number of samples that can be run in a given time period
9.
Ease of automation
How well the system can be automated
10. Ruggedness
Durability of measurement, ability to adverse conditions
11. Portability
Ability to move instrument around
12. Greeness
Ecoefficiency in terms of waste generation and energy
consumption
13. Cost
Equipment cost + cost of supplies + labor cost
12