Near-Infrared Spectroscopy for Assessing the Chemical Composition and Fatty Acid Profile of the Total Mixed Rations of Dairy Buffaloes

Near-infrared spectroscopy (NIRS) is an efficient, non-destructive method for evaluating the chemical composition of various compounds. This study aimed to assess both the proximate composition, fibres, and fatty acid (FA) content of Total Mixed Rations (TMRs) in dairy buffalo nutrition. A total of 240 TMR samples were collected from ten dairy buffalo farms across four seasons to develop predictive models using Partial Least Squares Regression (PLSR). Calibration models for dry matter (DM), crude protein (CP), ether extract (EE), and starch demonstrated good predictive accuracy, with coefficients of determination in cross-validation (R2cv) around 0.90 and Residual Predictive Deviation (RPDcv) values exceeding 3.0. Fatty acid models showed slightly lower R2cv values, ranging from 0.80 to 0.90. A good predictive performance was observed for linoleic acid (18:2 n-6) and α-linolenic acid (18:3 n-3), with RPDp values above 3.0, indicating reliable predictions. The inclusion of omega-3-rich compounds in the diet provides significant benefits for both animal health and product quality, highlighting the importance of ration monitoring. The findings confirm that while NIRS is effective for assessing chemical composition, further refinement is needed to improve FA prediction accuracy. These results support the use of NIRS as a practical tool for nutritional monitoring in lactating buffaloes.