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

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 (R-p(2)) 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. (C) 2007 Elsevier Ltd. All rights reserved.