Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM

Abstract

Protein is an important component of milk powder. The fast and non-destructive detection of protein content in milk powder is important. Infrared spectroscopy technique was applied to achieve this purpose. Least-squares support vector machine (LS-SVM) was applied to building the protein prediction model based on spectral transmission rate. The determination coefficient for prediction (Rp2) was 0.981 and root mean square error for prediction (RMSEP) was 0.4115. It is concluded that infrared spectroscopy technique can quantify protein content in milk powder fast and non-destructively. The process is simple and easy to operate, and the prediction ability of LS-SVM is better than that of partial least square. Moreover, the comparison of prediction results showed that the performance of model with mid-infrared spectra data was better than that with near infrared spectra data.