Application of NIRS for hay evaluation at different degrees of sample preparation.

Abstract

A study compared the performance of the NIRS (Near-Infrared Spectroscopy) calibration models developed with different degrees of hay sample preparations. Spectral data of 1-mm ground or whole hay samples were regressed against wet chemistry results of moisture, NDF (neutral detergent fiber), ADF (acid detergent fiber), CP (crude protein), and IVDMD (in vitro dry matter digestibility). A total of 227 imported alfalfa (Medicago sativa L.) and another 360 timothy (Phleum pratense L.) hay samples were used to develop the calibration models. The models developed with ground hay samples were more robust and accurate than whole hay based on cross-validation's R2 (coefficient of determination), standard error, and RPD (ratio percentage deviation). The R2 of cross-validation ranged from 0.61 (moisture of alfalfa) to 0.95 (CP prediction of timothy). Although R2 of calibration models was mainly greater than 0.90, the R2 of cross-validations remained marginal. Estimation of nutrient concentrations in imported hay can be achieved by calibrated NIRS. The NIRS calibration models must be improved by including more imported hay samples from different years and origins. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparation inputs compromising analysis accuracy by NIRS.