Rapid prediction of nutrient content of alfalfa hay by using near infrared spectroscopy

The aim of this study was to establish a near-infrared prediction model for six nutrients of alfalfa hay using near-infrared reflectance spectroscopy (NIRS). In total, 200 samples of alfalfa hay were collected from five provinces (Gansu, Ningxia, Hebei, Jiangsu, and Shanxi) to analyze dry matter (DM), crude ash (Ash), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and ether extract (EE). The calibration and validation sets included 160 and 40 samples, respectively. The NIRS system was combined with modified partial least squares (MPLS) to construct and verify the prediction models. The results showed that the coefficient of determination for validation (RSQ) and ratio of performance to deviation for validation (RPD) of the DM prediction model of alfalfa hay were 0.87 and 2.67 and those of the NDF prediction model were 0.90 and 3.16, respectively. The constructed model can be used for prediction in actual production. The RSQ and RPD of the CP prediction model were 0.83 2.41 and those of the ADF prediction model were 0.82 and 2.28, respectively. The constructed prediction model cannot completely replace wet chemical analysis but can be used for the screening analysis of a large number of samples. The RSQ and RPD of the Ash content prediction model were 0.59 and 1.51, respectively. The constructed prediction model can only be used for a rough analysis. The RSQ and RPD of the EE content prediction model were 0.45 and 1.32, respectively. This constructed prediction model was poorly correlated and needed further optimization.