Near Infrared reflectance spectroscopy...

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Near Infrared reflectance spectroscopy (NIRS) Dr A T Adesogan Department of Animal Sciences University of Florida

Transcript of Near Infrared reflectance spectroscopy...

Near Infrared reflectance

spectroscopy (NIRS)

Dr A T Adesogan

Department of Animal Sciences

University of Florida

Benefits of NIRS

Accurate

Rapid

Automatic

Non-destructive

No reagents required

Suitable for large nos of samples

Characterizes the entire forage composition

Prediction of in vivo OMD of 122

silages from different methods

Method r2 RSD

(M) ADF 0.34 0.051

Pepsin + cellulase 0.55 0.042

ISD – (48hr) 0.68 0.036

Rumen fluid-pepsin 0.74 0.032

NIRS 0.85 0.024

(Barber et al., 1990)

Indices successfully predicted by

NIRS

Lactate & VFA content

N degradability

Soluble N & NH3N

Feed intake, digestibility

and ME content

Minerals

Oil and CP content

ADF content

Lignin content

Lignin composition

Alkaloids

Fungal contaminants

Fermentation characteristics

GE content

Botanical composition

Effect of NH3 treatment

Petersen 2002 (Foss Tecator)

Underlying principle

Based on using wavelengths relating to the

absorbance of light by chemical components

within the feed to predict nutritive value

Forage reflectance spectrum correlated against

standard samples of known composition to derive

a relationship that can be used for future

predictions.

Principle

(Williams, 1977 )

Absorbance

NIR region = wavelength range 700-3000 nm

Conventional NIR machines for forage evaluation use the

1100 – 2500 nm wavelength region

NIR spectra are plots of reciprocal log10 reflectance (log

1/R) versus the wavelength

NIR spectrum

(Deaville and Flynn, 2000)

Wavelength choiceMost important determinant of accuracy of

predicting forage quality

Based on understanding the wavelength regions

associated with various chemical constituents

Choice should:

– reflect constituents which are part /relate to the

predicted term

– minimize the number of wavelengths

Prominent wavelength regions

Water 1940 & 1450 nm

Aliphatic C-H

bands

2310, 1725,

1400nm

Lipids

O-H bands 2100 & 1600 nm Carbohydrates

N-H bands 2180 & 2055 nm Proteins

(Deaville & Flynn, 2000)

Developing the calibrationCalibration = regression b/w spectra or

wavelengths & predicted term (e.g. intake)

Process

1. Examine population structure

• Must include all possible variation in future samples

2. Choose the relevant wavelengths

3. Employ a math treatment to develop the

calibration

4. Validate the calibration

Math treatmentsMultiple linear regression– Adds variables to a monovariate regression

– Possibility of overfitting/ math artefact predictions+

– Uses only limited spectral information

– Gives less accurate predictions

Principal components analysis (/regression)– Groups spectral data into a few, independent

components which are used as the predictors

– Hence uses most of the spectral data

– More accurate

Multiple partial least squares– Similar to PCA but uses both lab data & spectral data

in the prediction

– Often most accurate

Effect of math treatment on

S.E.PMath treatment Voluntary DMI OMD

MPLS 6.57 38

PCA 6.68 41

MLR 7.37 40

S.E.M 0.123 0.9

P <0.05 <0.06

(Deaville & Flynn, 2000)

Validation of the calibration

Entails testing the calibration on a different data set

Conventional method– Uses an independent population for validation

– Requires large # of samples (preferably >100)

Internal cross validation method– Separates the population into different groups and

– Progressively develops calibrations b/w groups & reference data till validation is complete

– Copes with smaller sample sizes

Factors affecting NIRS results

Wavelength choice

Math treatment

NIR instrument type

Sample preparation (density, particle size, % moisture)

Spectral data pretreatment techniques

NIR instrument types

Scanning monochromators

– Scan the entire wavelength regions

– Measure at 700 spectral points= more accurate

Fixed-filter instruments

– Cheaper hence favoured by some labs

– Measure at fewer spectral points

– Only accurate for predicting well-defined chemical

entities hence of limited use for digestibility predictions

– Can overcome this by developing relationships b/w

fixed-filter instruments & monochromators

Misleading predictions due to

sample moisture %

Using Wavelengths b/w 1450 and 1620 nm in

calibration enhances prediction of hay digy(Coleman and Murray, 1993).

However, water is also absorbed in the this region

This highlights the need for proper elimination of moisture

or use of undried samples.

Effect of milling on S.E.P

Method DMI OMD

Coarse milling 7.88 41

Finely milled, 5.97 37

S.E.M 0.349 1.4

P <0.001 <0.001

(Deaville & Flynn, 2000)

Spectral data pre-treatment

Forages/ feeds give overlapped absorption bands

rather than sharp individual peaks at specific

wavelengths

Spectral data pre-treatment can resolve such

problems which are due to:

– Sample particle size variations

– Temperature/humidity

– Light scatter

– Path length variation

Light pathways

(Reeves III, 2000)

Spectral shifts

(Reeves III, 2000)

1 2 3

A B = Peak shifts

Can be due to temp.

variations

A C Baseline shifts

Can be due to particle

size variations

A D Multiplicative

scatter

Can be due to particle

size variations

A E Multiplicative

scatter

2nd component (F) present

Shift correction methods

Correction Methods include:

– Derivatization

– Std. Normal variate detrending

– Multiplicative scatter correction

Derivatisation

When NIR spectra contains several overlapped

bands

Derivatisation resolves overlapped bands into

component absorptions

Hence derivatisation increases peak definition

– Reduces the effect of variable path length

Derivatisation

Other spectral pre-treatments

Standard normal variate (SNV) detrending

– Scales each spectrum to have a s.d. of 1.0

– Reduces spectral & particle size variability

Repeatability file/ multiplicative scatter correction

– Re-shapes each spectrum & till it resembles the target

spectrum obtained from the mean of a file of spectra

– Reduces variability due to moisture content

SNV – detrending

Wavelength (nm)

1000 1200 1400 1600 1800 2000 2200 2400 2600

Log 1

/R

0.1

0.2

0.3

0.4

0.5

0.6

0.7Accentuates moisture content

effect

Wavelength (nm)

1000 1200 1400 1600 1800 2000 2200 2400 2600

SN

V-D

-2

-1

0

1

2

‘Raw’ Spectra

‘SNV-detrended’ Spectra

NIRS - problems

Expensive initial outlay

Black box – biological meaning

Requires large data sets & frequent updating

Transfer of wet chemistry errors

Calibration population must be similar & contain

same variation as samples to be tested.

NIRS - problems

Species-specific equations

Can’t be directly used for predicting mineral %

– Minerals not absorbed in the NIR region

– Can only use NIRS for minerals based on

correlation b/w the mineral and an organic

component

NIRS – problems continued

Requires validation

– Most analytical methods also do, but this is

ignored

Complex algorithms/ chemometrics required

Misuse of equations

– Species-specific equations used for ‘other’ spp

– Calibrated with ‘unvalidated’ reference

methods

References

Deaville and Flynn, 2000. Near infrared reflectance

spectroscopy: An alternative approach to forage quality

evaluation. In Givens et al. 2000. Forage evaluation in

animal nutrition. Page 201. CABI, Wallingford

Reeves III J. B. 2000. Use of near infrared reflectance

spectroscopy. In D’Mello JPF. Farm animal metabolism

and nutrition. Page185. CABI Publishing.