Short-Wave Near-Infrared Spectroscopy of Milk Powder: Quantitative Analysis of Fat Content

The present study has aimed at providing new insight into short-wave near-infrared (short-wave NIR) spectroscopy of fat of milk powder. To do that, we analyzed NIR spectra in the 800-1025nm region of 350 milk powder samples. Based on the whole short-wave NIR spectra, performances of least-square support vector machine (LS-SVM) and partial least squares (PLS) are good. Determination coefficients for prediction were up than 0.95, and the root mean square error of prediction (RMSEP) are less than 0.5. The loading weights of PLS and regression coefficients of PLS and LS-SVM were used to determine the sensitive wavelengths for fat content of milk powder. Optimal four sensitive wavelengths, namely 900, 928, 990, and 1018nm, were obtained, and the spectra at these wavelengths were used for the content determination. Rp2 of LS-SVM models are up than 0.97, and RMSEP are less than 0.26. Thus these wavelengths would be useful for the development of portable instrument or online applications to discriminate the fat content of milk powder.