The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters

Abstract

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereals and dried animal forages. However relatively few studies have been carried out to evaluate the potential of NIRS to predict chemical constituents and digestibility parameters in undried forages. The predictive accuracy of NIRS relies heavily upon obtaining a calibration set which represents the variation in the main population, accurate laboratory analyses and the application of the best mathematical procedures. This study was undertaken to examine the potential of NIRS to accurately determine the chemical composition and a range of digestibility parameters of undried grass silage with the objective of characterising the feeding value of forage to the ruminant animal. A representative population of 136 grass silages covering a wide distribution in chemical and digestibility parameters formed the database for this investigation. A comprehensive chemical profile of the silages was determined and a range of digestibility parameters was measured through 72 wether sheep. Undried finely chopped 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 spectral data were regressed against a range of chemical and digestibility parameters using modified partial least squares (MPLS) multivariate analysis in conjunction with first and second order derivatization, with and without three scatter correction procedures to reduce the effect of extraneous noise. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The SD/SECV (standard deviation of the constituent data/standard error of cross validation) ratio was also calculated to evaluate the calibrations independent of their units (ratios>2.5 are acceptable for quality screening). In the case of the digestibility data a sub-calibration set of 90 samples and a validation set of 46 samples were selected on the basis of their spectral variation, and the optimum mathematical treatment for the 136 was applied. The results of this study show that NIRS predicted the main chemical parameters with a very high degree of accuracy (i.e., the correlation coefficient of cross validation ( R cv 2) ranged from 0.81–0.96) in all cases, except for buffering capacity which had a R cv 2 0.68 and a SD/SECV ratio of 2.19. The nitrogen fractions all formed calibrations with R cv 2 ranging from 0.87 to 0.94 for amino acid nitrogen and kjeldhal nitrogen, respectively and all SD/SECV ratios were greater than 2.5. The alcohol and volatile fatty acid calibrations produced slightly poorer calibrations with butyric and lactic acid and ethanol, giving the highest correlations. The in vivo digestibility parameters were predicted with considerable accuracy R cv 2 0.79–0.85 ( n=136) and the standard errors of prediction (SEP) for the validation set compared favourably with the SECV statistic for calibrations based on the 136 samples. This study found that NIRS calibrations based on undried grass silage spectra have the capability of predicting a wide range of chemical and digestibility parameters which would afford a rapid assessment of the forage for diet formulation.