The use of Near Infrared Reflectance Spectroscopy on dried samples to predict biological parameters of grass silage

ANIMAL FEED SCIENCE AND TECHNOLOGY

ELSEVIER

Animal Feed Science Technology 68 (19971235-246

The use of Near Infrared Reflectance Spectroscopy on dried samples to predict biological parameters of grass silage R.S. Park a,b3* , F.J. Gordon a3bTc, R.E. Agnew a7b,c,R. J. Barnes d, R.W.J. Steen a,b,c a Agricultural Research Institute of Northern Ireland, Hillsborough, Co. Down BT26 6DR, UK b Agricultural and Enuironmental Science Division, Newforge Lane, Belfast BT!9 SPX, UK ’ The Queen’s University of Belfast, Newforge Lane, Belfast BT9 5PX, UK ’ NIRSystems Inc., Highfield House, Foundation Park, Roxborough Way, Maidenhead, Berkshire SL6 3UD, UK Accepted 3 March I997

Abstract This study was undertaken to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) for the prediction of in vivo OMD (%) and voluntary intake (g/kg W”.“) measured through sheep and cattle respectively. A population of 136 grass silages representing a wide range in chemical and biological parameters was used in this investigation. The dried milled silage samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). The paper examines three multivariate regression techniques: modified partial least squares (MPLS), principal component regression (PCR) and stepwise multiple linear regression (SMLR) and investigates the effect of spectral pretreatment using 1st and 2nd order derivatization with and without three scatter correction procedures: standard normal variate and detrending (SNV-D), normal multiplicative scatter correction (NMSC) and weighted multiplicative scatter correction (WMSC), to optimize accuracy of prediction. The optimum mathematical treatment was selected by minimizing the standard error of prediction (SEP) of a blind validation set using a calibration and validation set of 90 and 46 respectively. The optimum methods were for in vivo organic matter digestibility (OMD), the stepwise regression procedure using 1st derivatization with a scatter correction (SEP 2.4%, R* 0.87) and for intake the MPLS regression technique again using 1st derivatization and a scatter correction (SEP 4.77 g/kg W’.“, R* 0.79). Comparison of three wavelength ranges (1100-2500 nm, 700-2500 nm and

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0377.8401/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved. PII SO377-8401(97)00055-2

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400-2500 nm) on the effect of calibration performance improvement from extending the range beyond 1100-2500

for OMD and intake showed little nm. 0 1997 Elsevier Science B.V.

Keywords: Grass silage; Intake prediction; Organic matter digestibility prediction; Near infrared reflectance spectroscopy; NIFCS

1. Introduction Optimizing ruminant feeding systems requires accurate information on the feeding value of the basal forage(s). In Western Europe ensiled crops are the main winter forage for cattle (Wilkinson and Stark, 1992) and in most northwestern areas grass is the predominant ensiled forage; e.g., in the United Kingdom grass silage represents 93% of all ensiled forages (MAFFJ, 1994). The feeding value of grass silage depends upon a combination of its intake potential and nutrient digestibility, and the accurate prediction of these two parameters is the key to providing controlled feeding regimes for ruminants. Near infrared reflectance spectroscopy (NIRS) was first shown to be a rapid method for predicting the chemical composition of forages by Norris et al. (1976). Since then numerous workers have explored the use of NIRS for the prediction of both chemical composition and digestibility (dry matter (DM) and organic matter (OM)) of grass silages. Barber et al. (1990) demonstrated that using NIRS the OMD of grass silage could be predicted much more accurately than any other laboratory method. Attempts have also been made to use NIRS to predict the voluntary intake potential of forages (Norris et al., 1976; Shenk et al., 1977; Ward et al., 1982; Coelho et al., 1988; Abreu et al., 1991; Flinn et al., 1992). However no previous work, other than that undertaken at this Institute by Steen et al. (19951, is available on the use of NIRS to predict the intake potential of grass silage. The development of robust and accurate NIRS prediction systems depends upon having a large calibration database which represents a wide range in the characteristics of the forage to be predicted. A major study carried out at the Agricultural Research Institute of Northern Ireland (Steen et al., 1995) has provided a large set of silage samples (n = 136) and associated OMD and voluntary intake data through animals. The objective of the present study was to examine a range of mathematical treatments of NIRS spectral data obtained using dried samples from the study of Steen et al. (1995) with a view to determining optimum regression techniques, data transformations and wavelength ranges for the prediction of intake potential and OMD of grass silages.

2. Materials and methods 2. I.

Silages

One hundred and thirty six grass silages, produced commercially on farms across Northern Ireland over the two year period 1992-94, were used in this investigation. The silages were selected to provide a wide range in: (a) oven dry matter, (b) pH, cc>

R.S. Park et al. /Animal Feed Science Technology 68 (19971235-246 Table I Range, mean and standard deviation used in the study

in the chemical

composition

and biological

parameters

211

of the 136 silages

Parameter

Range

Mean

Standard deviation

Dry matter (g/kg) Crude protein (g/kg DM) Ammonia-N (g/kg total N) PH Metabolisable energy (MJ/kg DM) (predictedby NIRS) Dry matter intake (g/kg W” “) Organic matter digestibility (in viva%)

155 to413 II to 212 45 to 385 3.5 to 5.5 8.8 to 12.3 45 to 113 53.1 to 80.7

219 133 123 4.2 10.3 71.3 67.8

32.2 24.5 63.5 0.4 0.8 12.0 7.13

ammonia nitrogen as proportion of total N and (d) predicted metabolizable energy (ME) content. The silages also embraced a range of sward types, harvesting dates, silage additives and ensiling techniques. Table 1 indicates the means, range and standard deviations of the chemical and biological parameters of the silages. Approximately seven tonnes OF each silage were transported to the Agricultural Research Institute of Northern Ireland and ad libitum intake (g/kg W”.75) and organic matter digestibility (OMD%) measured through cattle and sheep respectively. The methods used in the animal studies are described by Steen et al. (1995). One hundred and ninety two individually fed beef cattle with a mean liveweight of 415 kg were offered the 136 silages ad libitum in two linked changeover design experiments. Each silage was offered as the sole feed to 10 animals for a period of two weeks and eight silages were offered in each of 17 periods. An additional 16 animals were offered a standard diet in each period to enable variation in intake due to periods to be removed. Digestibility balances were performed using seventy two wether sheep, with a mean liveweight of 50 kg, fed at maintenance level. In a complete changeover design experiment four sheep were offered each silage, with eight silages plus the standard diet as the sole feed for each of the 17 periods. The intake data estimated from the beef cattle study and the organic matter digestibility data calculated from the sheep balance study were adjusted statistically to remove animal and period effects. During the preparation of the silages for feeding three representative samples of each silage were taken, dried at 85°C for 21 h in a forced draught oven and subsequently milled, to pass through a 1 mm screen, using a Christy Norris crossbeater mill. This resulted in 408 dried silage samples (three per silage) which were used for NIRS scanning. 2.2. NIRS measurements The 408 dried samples were air equilibrated before being scanned at 2 nm intervals over the visible and near infrared spectral range (400-2500 nm) using a NIRSystems Model 6500 Scanning Spectrophotometer (Perstorp Analytical, Silver Spring, Maryland, USA). Samples were scanned using a closed cell, with two packings per sample, and with spectral data recorded as log l/Reflectance values (log l/R). The six spectra were averaged to produce a mean spectrum for each silage.

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2.3. Analysis of spectral data Mathematical treatment of the spectral data was performed using the ISI-NIRS2 Version 3.1 software (Infrasoft International, Port Matilda, PA, USA) (Shenk, 1992). Using this software calibrations were developed for OMD and voluntary intake using three multivariate regression techniques: Modified Partial Least Squares (MPLS) (Martens and Naes, 19891, Principal Component Regression (PCR) and Stepwise Multiple Linear Regression (SMLR). The SMLR technique selects the wavelengths most highly correlated to the reference data. MPLS and PCR both reduce the spectral data to a few independent factors or principal components. PCR is essentially a two stage method in which the data compression stage is carried out independently of the regression stage, and each principal component is orthogonal to the previous one. In MPLS these two stages are combined so that not only is much of the spectral variation accounted for but these independent factors are correlated to the reference data, and the independent factors do not have to be orthogonal to one another. Transformations of the spectral data, through derivatization and a range of scatter correction procedures, were also examined as there is evidence that these techniques can reduce spectral interference from particle size and other extraneous effects (Baker and Barnes, 1990). Therefore within each of the three regression techniques, equations were produced using the raw log l/R, first order and second order derivatized data. Derivatization was performed using the method of Norris and Williams (1984). A mathematical derivatization of 1, 4, 4 and 2, 12, 6 were used in the first order and second order respectively, where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated and the third is the number of data points in a running average. Also within each of the above regression and derivatization techniques the following scatter correction procedures were applied: No Scatter Correction (NC); Standard Normal Variate and Detrend @NV-D); Normal Multiplicative Scatter Correction (NMSC) and Weighted Multiplicative Scatter Correction (WMSC) (IS1 Software, Port Matilda, PA, USA). The SNV transformation is applied to each individual spectrum in isolation and without any reference to the sample set. This transformation first centres the spectral values, i.e., subtracting the mean of the individual spectrum from each value. These centred values are then scaled by the standard deviation calculated from the individual spectrum values. Thus SNV transformed spectra have a standard deviation of 1.0 and a mean of zero. Detrending removes the linear and quadratic curvature of each spectrum, usually caused by different packing densities, with the use of a second-degree polynomial. The NMSC and WMSC transformations however are set dependent with each individual spectrum regressed on the set-mean-spectrum. These spectra have non-zero mean. In WMSC to compute the simple linear regression the absorbancies are weighted according to their standard deviation. In the development of all calibrations 11 terms were set as the maximum. Overfitting was avoided in SMLR equations by accepting only equations containing wavelength terms with an F-statistic greater than eight (Windham et al., 1985). In MPLS and PCR equations cross validation was used to select the optimum number of principal components, or eigenvectors, and avoid overfitting. Cross validation was performed by

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removing one sample from the population of 136 in turn and forming a calibration based on the remaining 135 samples and using this to predict the excluded sample. The validation errors were combined into a standard error of cross validation (SECV) and the optimum number of terms was taken as the number resulting in the lowest prediction error. Using these procedures 36 calibration equations were produced for both OMD and intake. The best three mathematical treatments of the data within each regression technique were selected on the basis of the lowest standard error of calibration (SEC). Each of these data treatments were then tested by dividing the population into calibration and validation sets of 90 and 46 samples respectively. The calibration set of 90 samples was selected to represent the range in spectral variation of the original 136 samples using Mahalanobis distances through the IS1 software. These calibrations were validated on the remaining 46 samples and the standard error of prediction (SEP) compared. 2.4. Wavelength

range

The spectral data collected covered the range 400-2500 nm. As all NIRS equipment do not have the facility to cover this range, the data were used to explore the effect of wavelength range on accuracy of prediction. The ranges chosen were 1100-2500 nm, 700-2500 nm and 400-2500 nm. In the comparison of wavelength ranges calibration and validation statistics were determined for each of the three best mathematical treatments identified previously for the 400-2500 nm range.

3. Results and discussion In addition to developing performance statistics for the prediction of OMD and intake of grass silages via NIRS a major objective of the present study was to examine a range of data transformations and regression techniques on the spectral data in order to optimise the accuracy of prediction. In studies involving selection of optimum methods of handling spectral data, a range of calibration/validation statistics can be used to compare the performance of individual methods. In the present study within each regression technique, the best three data treatments were selected on the basis of minimising the SEC and maximising the coefficient of determination (R2) using the spectral data from all 136 samples. The details of the equations selected by this method, including calibration and validation statistics are presented in Table 2. However when comparing across regression techniques it is accepted that validation statistics are more appropriate. With MPLS and PCR comparisons cross validation statistics (SECV and I-VR where I-VR is equivalent to R2 of cross validation) are considered the most suitable, as this validation uses all the samples available and therefore presents a truer estimation of the performance of the calibration (Shenk and Westerhaus, 1993). However cross validation is not suitable with the SMLR technique and hence the performance statistics on a blind validation set (n = 46), which remained constant across all mathematical treatments, were used in comparisons involving this technique.

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3.1. Organic matter digestibility

The data presented in Table 2 show that the SMLR technique gave the lowest SEP and the highest correlation coefficient CR*> for the blind validation set (n = 46). The SEP values for all the other quoted regression equations, across the three regression techniques, were very similar. However comparison of the SEC statistics (n = 136) indicates that the MPLS regression technique produced the best two calibration equations. The SEC values for the PCR equations were much larger than for the MPLS and SMLR techniques. The superiority of the MPLS over the PCR technique is also clearly highlighted in the cross validation statistics, with the mean SECV being 2.21 and 2.70 for the three best MPLS and PCR equations respectively. Baker et al. (1994) in a similar comparison of MPLS, multiple stepwise regression (MSR) and principal component analysis (PCA) techniques concluded that the MPLS technique achieved the greatest accuracy of prediction of OMD. In the present study the three best MPLS regression equations also had fewer terms than the PCR equations (means of 7 and 9 respectively). A similar effect was noted by Goedhart (1990) when comparing PLS and PCR techniques for the prediction of the OMD of maize forage. This would support the view that the MPLS technique more successfully compresses the spectral data and correlates

Table 2 The calibration and validation regression techniques Regression

Derivative

statistics

Scatter Correction

for the prediction

Calibration (n = 136)

of OMD and voluntary

and validation

intake using a range of

Calibration (n = 90)

Validation (n = 46)

SEC

R2

SECV

l-VR

SEC

R2

SEP

R2

(%) NMSC WMSC SNV-D SNV-D NONE WMSC NMSC SNV-D WMSC

1.64 1.82 1.86 2.55 2.64 2.65 1.84 1.89 1.96

0.94 0.93 0.93 0.87 0.86 0.86 0.93 0.93 0.93

2.15 2.22 2.26 2.65 2.74 2.70 NA NA NA

0.91 0.90 0.89 0.86 0.85 0.86 NA NA NA

1.65 1.77 1.69 2.48 2.63 2.64 1.88 1.91 1.98

0.95 0.94 0.94 0.88 0.87 0.87 0.93 0.93 0.92

2.7 2.7 2.8 2.8 2.7 2.8 2.4 2.8 2.8

0.85 0.86 0.85 0.84 0.84 0.83 0.87 0.83 0.84

Voluntary Intake fg / kg W o.75) SNV-D MPLS 1 NMSC 1 WMSC 2 SNV-D PCR 0 SNV-D 2 SNV-D 1 WMSC SMLR 2 NMSC 1 WMSC 0

3.43 4.3 1 4.24 5.01 5.34 5.35 3.94 4.11 4.20

0.90 0.84 0.85 0.79 0.77 0.77 0.87 0.86 0.85

5.05 5.22 5.19 5.30 5.56 5.50 NA NA NA

0.78 0.77 0.77 0.76 0.75 0.76 NA NA NA

4.59 4.61 4.83 4.94 5.45 5.59 4.48 4.99 5.65

0.82 0.86 0.80 0.79 0.75 0.74 0.83 0.78 0.75

5.42 4.77 5.56 5.26 6.09 6.32 5.29 5.27 5.19

0.74 0.79 0.75 0.79 0.74 0.71 0.78 0.76 0.79

Organic Matter Digestibility MPLS 2 2 2 PCR 0 0 0 SMLR 1 2 1

NA = Not applicable.

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it to the reference parameter, in this case OMD, than PCR. For this reason we would also expect the MPLS technique to be more robust than the SMLR technique which selects only 8/9 wavelengths from a total of 1050. The performance statistics for the prediction of OMD using NIRS in the present study are a major improvement over chemical methods, such as modified acid detergent fibre (MADF), acetyl bromide lignin, in vitro digestibility and pepsin cellulase. For example, Barber et al. (1990) reported the in vitro digestibility method, with a R* of 0.64, to be the best of the chemical approaches for the prediction of OMD. Other methods such as MADF, were considerably poorer. These data from chemical analysis are clearly inferior to those of the present study in which the best MPLS calibration statistic (n = 136) is a SEC of 1.64 and R* of 0.94. These performance statistics using NIRS are also better than those obtained by Baker and Barnes (1990) from NIRS when using a nine term, second order derivative, multiple linear regression equation for in vivo OMD based on 101 grass silages. In this latter instance the SEC was 2.77 and R* 0.81. 3.1.1. Effect of spectral pre-treatment It is recognised that near infrared spectral data is influenced by particle size, scatter coefficient and pathlength (Barnes et al., 1989). In order to remove these extraneous effects the spectral data can be pretreated either by derivative transformation or scatter correction procedures. In the present study derivatization (either first or second order) of the log l/R optical data has optimized the regressions for each of the three best calibrations within the MPLS and SMLR regression techniques. However no derivatization was selected with the PCR technique, although as indicated earlier this was the least accurate regression technique. A recent study by Baker et al. (1994) also found that derivatization was employed in all of their best calibrations for OMD prediction when comparing MPLS, MSR and PCA techniques. The present study also shows that some method of scatter correction enhances prediction of OMD, with eight out of the nine regressions selected as being best applying a scatter correction technique. These findings also support the work of Baker et al. (1994) who reported a scatter correction being used in seven out of nine best calibration equations. The results of the present study would support the view that the treatment of spectral data by MPLS regression, coupled with derivatization and scatter correction, is the most appropriate for the prediction of OMD. Furthermore using this approach on the data available in this study resulted in a SECV of 2.15 (OMD%) and I-VR of 0.91. These performance statistics are in line with those reported by Baker et al. (1994) when using a sample set with a similar OMD range (52.8-82.3%) to that used in the present study. 3.1.2. Wavelength range Previous approaches to the prediction of silage quality through NIRS have mainly used the wavelength range from 1100-2500 nm. However recent advances in instrumentation enable the range to be extended to 400-2500 nm and hence include spectral information in the visible and lower end of the near infrared region. In the present study the effect of altering the spectral range on accuracy of prediction of OMD was examined by producing the validation statistics for the three best equations within each regression

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technique and for each of the three spectral ranges: 400-2500 nm, 700-2500 nm and 1100-2500 nm. These data are presented in Table 3 as means for the three equations within each wavelength range and regression technique. In relation to OMD the validation statistics indicate that extending the wavelength range beyond 1100-2500 nm has little effect on the predictive accuracy of the calibrations, irrespective of the regression technique employed. This effect is supported by the fact that the wavelengths selected in the calibration equations were generally within the 1100-2500 nm region, and a major proportion of which have previously been shown to be associated with digestibility e.g., 1652 nm, 1666 nm, 1668 nm and 2252-2280 nm. Many of the wavelengths selected are also associated with N-H and C-H bonds in proteins and protein fractions i.e., amide I, II and III. Also playing a large role in the OMD calibrations are C-H, C-O and O-H cellulose bonds plus C-H, C-C and O-H bonds in oil. These wavelength regions are in accord with the work of Murray et al. (1987), Lindgren (19881, Kridis (1989) and Baker and Barnes (19901, who have suggested that the spectral regions 1650-1670 nm and 2260-2280 nm consistently appear as the most important regions associated with digestibility. Givens et al. (1992) demonstrated that in cereal straws the 1650 and 2254 regions related to the indigestible fractions of the forage and Russell et al. (1989) has indicated that these regions relate to lignin bonding.

Table 3 Effect of wavelength range on the validation statistics for the prediction of OMD and voluntary intake (for each regression technique the data presented is a mean of the three best maths treatments presented in Table 2) Regression

technique

Wavelength

Organic matter digestibility (%) MPLS 400-2500 700-2500 1100-2500 PCR 400-2500 700-2500 1100-2500 Stepwise 400-2500 700-2500 1100-2500 Voluntary intake (g/kg MPLS

PCR

Stepwise

NA = Not applicable.

W *.‘j) 400-2500 700-2500 1100-2500 400-2500 700-2500 1100-2500 400-2500 700-2500 1100-2500

(nm)

Validation

(n = 136)

Validation

( n = 46)

SECV

l-VR

SEP

R2

2.21 2.16 2.28 2.70 2.86 2.89 NA NA NA

0.90 0.91 0.90 0.86 0.84 0.83 NA NA NA

2.7 2.4 2.5 2.8 3.1 3.1 2.7 2.9 2.8

0.85 0.89 0.88 0.84 0.80 0.80 0.85 0.82 0.84

5.15 5.06 5.20 5.36 5.96 5.86 NA NA NA

0.77 0.78 0.77 0.76 0.71 0.71 NA NA NA

5.25 5.39 4.69 5.89 5.99 6.05 5.25 5.92 5.38

0.76 0.79 0.82 0.75 0.71 0.72 0.78 0.74 0.77

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3.2. Voluntary intake The calibration and validation statistics for the three best intake prediction relationships within each regression technique are presented in Table 2. On the basis of the calibration statistics (SEC), using the 136 samples, the MPLS technique achieves the best performance, having the lowest individual SEC (3.43 g/kg W”.75) and highest R* (0.90) as well as the best overall mean across the three equations within each regression technique, although it was only marginally better than the SMLR technique. However as indicated earlier when comparing the MPLS and PCR techniques it is considered that cross-validation statistics provide the most appropriate comparison. In the present study these data demonstrate that the MPLS regression technique in each of the three instances, provides better predictive potential than PCR. This effect is in line with that reported earlier for OMD. As indicated previously the blind validation method is the only appropriate method of making comparisons involving the SMLR technique. Using this approach would suggest that the MPLS technique is again only marginally better than the SMLR technique, having the lowest individual SEP but a similar mean over the three best equations to that achieved with the SMLR (mean SEP in both instances = 5.25). While both the SMLR and MPLS techniques result in similar performance statistics the SMLR technique is considered to be subject to a number of unsatisfactory features, such as collinearity and overfitting. The MPLS algorithm reduces the spectral data to a few uncorrelated variables which utilize the reference data in their construction. This incorporation of most of the spectral data in the MPLS technique, as opposed to the selection of 8/9 wavelengths as in the SMLR technique, would support the view that MPLS prediction equations are likely to be a more robust approach to predicting biological parameters. Therefore it is suggested that in spite of similar performance statistics between SMLR and MPLS that the latter is likely to be the more suitable for widespread use. 3.2.1. Effect of spectral pre-treatment It is clear from Table 2 that the use of a scatter correction procedure and derivative transformation of the spectral data, have both made major contributions to optimising the prediction potential of the calibrations. First and second order derivatization were used in seven out of the nine instances and a scatter correction used in all nine instances. This is a similar trend to that reported in this study, and others (Baker et al., 1994) for OMD. 3.2.2. Comparison of intake prediction for grass silage and other forages by NIRS No previous work has been found on the use of NIRS to predict the intake of ensiled grass. However this technique has been employed by a number of workers to predict the intake of fresh and dried forages. For example Norris et al. (1976) using data on the intake of hay by sheep reported a SEP of 7.8 g/kg W’.” and an R* of 0.62 with n = 87. Ward et al. (1982) using 21 fresh forages, covering a range in OM intake from 52.6 to 112.3 g/kg W’.“, related NIR spectral data to OM intake by animals at pasture

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and reported an SEC of 9.6 g/kg W”.75 and R* 0.72. Similarly Abreu et al. (1991) examined pulse straws (n = 113) and observed that the NIR spectra could account for 86% of the variation in intake. Redshaw et al. (1986) using n = 70 (for cattle) and 59 (for sheep) with legumes, grass and their mixtures reported a SEC of 7.3 and 6.7 g/kg W”.75 and R* of 0.71 and 0.71 for cattle and sheep respectively. The results of the present study, which covers a large range in DM intake (45-113 g/kg W”.75) indicates a considerable improvement in the accuracy of intake prediction of grass silages. The cross validation statistics (n = 136) for the best MPLS regression equation show that intake in cattle can be predicted with an accuracy of f5.05 g/kg wo.75

3.2.3. Wavelength range The data on the effect of wavelength range on the accuracy of intake prediction are presented in Table 3. The mean validation statistics, across the three best equations within each regression technique and wavelength range, show only a marginal improvement when the spectral range is extended to 400 nm. This is reflected in the wavelengths selected in the prediction equations with the majority being within the 1200-2500 nm region. However in a few instances a wavelength was selected from the green region of the visible spectrum (e.g., 494 nm). This could suggest a correlation between intake and chlorophyll. Wavelengths relating to N-H bonds in protein, O-H bonds in water and -CH, in oil were also selected at the lower region of the near infrared (908-1002 nm>. Nevertheless the wavelengths of most importance for intake prediction are relatively similar to those reported for OMD, being primarily associated with protein, protein fractions, cellulose and oil. However in addition, wavelengths associated with phenols, alcohols and olefins also occur frequently in the intake equations. Few results have been reported from elsewhere on the wavelengths selected in the prediction of forage intake. Ward et al. (1982) suggested the most important wavelength to be 1410 nm. Taking spectral pre-treatment into account this could correspond to the wavelength 1420 nm which appeared in several of the equations developed in the present study and is associated with glycol, alcohol, phenol and C-H combination aromatic bonds. The regions 1636-1668 nm and 2244-2288 nm were also selected in this study and are recognised as being strongly correlated to digestibility. The other regions appearing most consistently were 2300 nm, relating to the components cellulose and oil, and 1988 and 2050-2070 nm relating to N-H bonds. Rook and Gill (1990) and Steen et al. (1995) have reported that nitrogen and nitrogen components have strong positive relationships with intake even when collinearity between nitrogen content and other parameters are removed. Norris et al. (1976) reported the major wavelengths to be 1976, 1690, 1898, 2080, 2208, 1718 and 2158 run, most of which are similar to those selected by the same authors in the prediction of OMD. It is interesting however that Redshaw et al. (1986) reported that animal species (sheep vs. cattle) had a major effect on the wavelengths selected in the prediction of intake. This factor could be important in the previously noted inability of a sheep model to rank the intakes of silages for cattle (Cushnahan et al., 1994).

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4. Conclusions The present study has demonstrated that the two main components of grass silage contributing to its feeding value, OMD and voluntary intake, can both be accurately predicted by NIRS. Optimum data treatment has involved the MPLS regression technique coupled with data transformation to remove spectral interference. Little improvement in accuracy was obtained by extending the wavelength range beyond 1100-2500 nm.

Acknowledgements The authors would like to thank The Department of Agriculture for Northern Ireland, The Northern Ireland Grain Trade Association, The Milk Marketing Board for Northern Ireland and Strathroy Milk Marketing for financial support for this study. The authors also wish to thank Mr N. Grant for technical assistance.

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