near infrared reflectance spectroscopy...
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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
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.
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
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
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
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.
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