Parametro Do RPD

8/11/2019 Parametro Do RPD

1/24


2/24

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


3/24


Transmittance

3

Source Wavelength

selector SAMPLE Detector Readout


4/24


Reflectance

4

Source

Wavelengthselector

SAMPLE

Detector

Readout


5/24


NIRS Instruments


6/24


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


7/24

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


8/24


NIRS Calibration

Steps required for estimation of a mathematicalrelationship between optical properties of thesample and its known chemical composition.

8

Spectral DataX

Spectral DataX

Constituent concentrationY

(Obtained by standard wet

chemistry methods)


(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)


9/24


Spectral Preprocessing- Remove noise

- Enhance useful information

Example using multiplicative

scatter correction Function of derivatives


10/24


NIRS PredictionCalculation of composition by using spectral data(transmittance or reflectance) in calibration model

10

Spectral DataX

Spectral Data

X




(calibration model)

Y= f ( X )


(calibration model)

Y= f ( X )


11/24


NIRS Validation

Proof that a calibration is maintaining accuracywith standard, accepted chemical methods overthe useful life of the calibration.

11

NEW SAMPLES Not in Calibration Population

Spectral DataX

Spectral DataX

Constituent

concentrationY


Constituent

concentrationY


Comparison of

calculated values toreference data

SEP, RPD, etc

Comparison of

calculated values toreference data

SEP, RPD, etc


(calibration model)Y= f ( X )


(calibration model)

Y= f ( X )


12/24


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


13/24


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


14/24


Sample Set Size is Important


15/24


Reproducibility of Reference Data


16/24


Reproducibility of Reference Data


17/24


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.


18/24


Calibration Transfer Basic


19/24


Calibration Transfer Difficult


20/24


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


21/24


Correlated Y - Amino Acids

C l t d Y


22/24


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.


23/24


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!


24/24


Where To Find Us

Documents

Parametro Do RPD