Determination of protein, total carbohydrates and crude fat contents of foxtail millet using effective wavelengths in NIR spectroscopy

The near infrared spectroscopy methods for determining the protein, total carbohydrates and crude fat contents in foxtail millet were investigated. The samples were divided into calibration and validation subsets for multivariate modeling using the joint x-y distance (SPXY) algorithm. The interval partial least squares (iPLS) and the successive projections algorithm (SPA) coupled with multiple linear regression (MLR) methods were applied for variable selection during the modeling process. The coefficient of determination of validation (Rval2), the root mean square error of prediction (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD) of the obtained optimum models were 0.94, 0.28 and 4.07 for protein, 0.92, 0.40 and 3.28 for total carbohydrates, and 0.70, 0.17 and 1.76 for crude fat, respectively. Prediction models using effective wavelengths were proposed for the three constituents. The results indicated that near infrared spectroscopy is a promising approach for predicting the protein, total carbohydrates and crude fat contents of foxtail millet rapidly and accurately.