Parametro Do RPD
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Transcript of Parametro Do RPD
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Copyright 2010 Iowa State University
NIR Light
Electromagnetic spectrum
2
What is so special about the waves in NIR range?
NIR light is absorbed by molecules containing C-H, N-H, and O-H
groups (fats, proteins, carbohydrates, organic acids, alcohol, water)
NIR range:750 - 2500 nm
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Copyright 2010 Iowa State University
Transmittance
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Source Wavelength
selector SAMPLE Detector Readout
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Reflectance
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Source
Wavelengthselector
SAMPLE
Detector
Readout
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Copyright 2010 Iowa State University
NIRS Instruments
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NIRS and AOCS
Near Infrared Spectroscopy (NIRS)is arapid, indirect, analytical method with
expanding use in oilseed marketing. The Soybean Quality Traits (SQT) has
four programs to promote uniformity
across NIRS users and platforms.
Core sample set
Proof of application testingStandard Calibration Method
Proficiency (check sample) testing
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Copyright 2010 Iowa State University7
AOCS Analytical Guidelines Am 1a-09
Near Infrared Spectroscopy InstrumentManagement and Prediction Model Development
Main Topics
Performance
Sample Sets
Lab ValuesPreprocessing
Validation/Calibration Statistics
Calibration Transfer/StandardizationMoisture Basis
Correlated Y Variables
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NIRS Calibration
Steps required for estimation of a mathematicalrelationship between optical properties of thesample and its known chemical composition.
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Spectral DataX
Spectral DataX
Constituent concentrationY
(Obtained by standard wet
chemistry methods)
Constituent concentrationY
(Obtained by standard wetchemistry methods)
Regressionalgorithm
Regression
algorithmMathematicalrelationship
(calibration model)
Y= f ( X )
Mathematicalrelationship
(calibration model)
Y= f ( X )
Multiple Linear Regression (MLR)
Principal Component Regression (PCR)
Partial Least Squares (PLS)
Artificial Neural Networks (ANN)
Locally Weighted Regression (LWR)
Support Vector Machines (SVM)
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Copyright 2010 Iowa State University9
Spectral Preprocessing- Remove noise
- Enhance useful information
Example using multiplicative
scatter correction Function of derivatives
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Copyright 2010 Iowa State University
NIRS PredictionCalculation of composition by using spectral data(transmittance or reflectance) in calibration model
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Spectral DataX
Spectral Data
X
Constituent concentrationY
Constituent concentrationY
Mathematicalrelationship
(calibration model)
Y= f ( X )
Mathematicalrelationship
(calibration model)
Y= f ( X )
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Copyright 2010 Iowa State University
NIRS Validation
Proof that a calibration is maintaining accuracywith standard, accepted chemical methods overthe useful life of the calibration.
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NEW SAMPLES Not in Calibration Population
Spectral DataX
Spectral DataX
Constituent
concentrationY
(Obtained by standard wetchemistry methods)
Constituent
concentrationY
(Obtained by standard wetchemistry methods)
Comparison of
calculated values toreference data
SEP, RPD, etc
Comparison of
calculated values toreference data
SEP, RPD, etc
Mathematicalrelationship
(calibration model)Y= f ( X )
Mathematicalrelationship
(calibration model)
Y= f ( X )
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Copyright 2010 Iowa State University
Evaluation Statistic - RPD
RPD Class Application
0.02.3 Very poor Not recommended
2.43.0 Poor Rough screening
3.14.9 Fair Screening
5.06.4 Good Quality control
6.58.0 Very good Process control
8.1+ Excellent Any application
RPD = (Std. Dev. of Reference Data)/(Std. Error of Prediction)
S C
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Copyright 2010 Iowa State University
SBM Protein Calibration
Figure 2. Protein in soybean meal
Calibration Cross Validation
2006-2008 Samples
SECV=0.65; RPD = 6.2
Validation Set
2009 Samples
SEP = 0.36; RPD = 2.6
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Copyright 2010 Iowa State University
Sample Set Size is Important
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Reproducibility of Reference Data
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Reproducibility of Reference Data
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Inter-Lab Reproducibility
See Also: Hartwig, R.A. and Charles R. Hurburgh, Jr. 1991. Interlaboratory comparison of soybean
protein and oil determinations. JAOCS 68(12):949-957.
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Calibration Transfer Basic
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Calibration Transfer Difficult
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Moisture Basis
Terminology Native moisture basis = moisture basis as
developed. Choices:
As is (at the moisture as scanned)
Direct (Reference values adjusted to an MB)
Reported moisture basis = moisture basis asconverted and reported (if different)
These are all the same sample:
35% Pro@10% M=33.8% Pro@13%M
=38.9% Pro@ 0% M
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Copyright 2010 Iowa State University
Correlated Y - Amino Acids
C l t d Y
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Copyright 2010 Iowa State University22
Correlated Y:
Amino AcidsComparison of NIR
calibration models
(average r2) with
linear regressions of
reference amino
acids to reference
protein
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
LEU LYS HIS P HE AS P TYR AR G GLY ALA IS O P R O VAL THR GLU M ET S ER C YS TR Y
CoefficientofDeterminatio
The bes t of NIR
calibrations
Linear regression
to protein
Regression vectors
of 18 AA PLS
models for FOSS
Infratec 1241 Grain
Analyzer; most of
the curves follow
the same pattern,which indicates that
calibrations predict
one constituent.
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Copyright 2010 Iowa State University
Summary
AOCS Analytical Guidelines Am 1a-09Near Infrared Spectroscopy Instrument Management and
Prediction Model Development
A guideline to assist in NIRS calibration efforts.
Covers the main issues of NIRS Operation.Users need to be able to do their own calibration.
Guidelines are not static; expect updates!
Provide input!
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Copyright 2010 Iowa State University
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