Prediction of silage digestibility by near infrared reflectance spectroscopy

Abstract

A total of 142 silage samples were used to evaluate the ability of near infrared reflectance spectroscopy (NIRS) to predict dry matter (DM) and neutral detergent fibre (NDF) digestibility. Three techniques, Partial Least Square (PLS), Principal Component Regression (PCR) and Multivariate Linear Regression (MLR), and a series of mathematical treatments were applied to establish the NIRS calibrations. The F-test and T-test of correlativity between reference and NIRS values of silage samples in validation set were carried out to verify the models. Results showed the PLS technique gave the best prediction and calibrations, and that the calibrations developed from the combination of smooth and first-order derivative treatment were largely acceptable. The prediction of DM digestibility of dried samples was good with the determination coefficient of validation (r2) being greater than 0.80. The prediction of dried samples was better than that of fresh samples. The relative standard deviation (RSD) values of DM digestibility for dried and fresh samples were 8.25 and 11.26%, respectively. As for NDF digestibility, the RSD values were higher, being of 22.87 and 23.50% for dried and fresh samples, respectively.