Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy

Knowledge of the amounts and digestibility of amino acids in pig feedstuffs is essential for calculating the appropriate inclusion level in a complete diet. Wet chemical analysis and in vivo digestibility trials are time-consuming and costly and cannot be used for routine assessment. Near-infrared spectroscopy (NIRS) offers a rapid, cost effective and environmentally friendly method for evaluating feedstuffs. Calibrations models were developed using NIRS to predict the content of crude protein and 18 amino acids from a wide range of feedstuffs used in pig production (n = 607). The samples ranged from single feed ingredients (containing amino acids from 0.3 to 129.8 g/kg of dry matter) to feed mixtures (containing amino acids from 1.2 to 53.2 g/kg of dry matter). The predictive ability of the calibrations was tested with an independent dataset (n = 150) and with cross-validation. Furthermore, we compare these calibrations with calibrations developed on more narrowly defined groups of samples and with predictions from regression analysis of crude protein. The models were able to predict the concentrations of crude protein and 18 amino acids with good levels of precision and high coefficients of determination for calibration (RSQ ^CAL) from 0.91 to 0.99 and validation (RSQ^VAL) from 0.87 to 0.97. Calibration models were able to predict all amino acids except tryptophan and valine with greater accuracy than those from protein regression. We also developed calibration models to predict the apparent ileal and total tract digestibility of protein and amino acids. With the exception of tryptophan, RSQ values (>0.7) and standard error of cross validation (SECV) values (<5%) were obtained for the digestibility of most of the amino acids. In conclusion, NIRS can be used to predict crude protein and amino acid concentrations from a wide range of single ingredients and feed mixtures used for pig diets without separate models for each feedstuff. The digestibility of protein and amino acids can be predicted with an acceptable accuracy to be useful in formulating pig diets.