Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis.
The aim of the present paper was to provide new insight into the short-wave near-infrared (NIR) spectroscopic analysis of milk powder. Near-infrared spectra in the 800- to 1,025-nm region of 350 samples were analyzed to determine the brands and quality of milk powders. Brand identification was done by a least squares support vector machine (LS-SVM) model coupled with fast fixed-point independent component analysis (ICA). The correct answer rate of the ICA-LS-SVM model reached as high as 98%, which was better than that of the LS-SVM (95%). Contents of fat, protein, and carbohydrate were determined by the LS-SVM and ICA-LS-SVM models. Both processes offered good determination performance for analyzing the main components in milk powder based on short-wave NIR spectra. The coefficients of determination for prediction and root mean square error of prediction of ICA-LS-SVM were 0.983, 0.231, and 0.982, and 0.161, 0.980, and 0.410, respectively, for the 3 components. However, there were less than 10 input variables in the ICA-LS-SVM model compared with 225 in the LS-SVM model. Thus, the processing time was much shorter and the model was simpler. The results presented in this paper demonstrate that the short-wave NIR region is promising for fast and reliable determination of the brand and main components in milk powder.
Wu
D
D
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Feng
S
S
He
Y
Y
D000818:Animals; D005223:Fats / Q000032:analysis; D005519:Food Preservation / Q000145:classification*; D006801:Humans; D041943:Infant Formula / Q000145:classification*; D007785:Lactose / Q000032:analysis; D008892:Milk / Q000737:chemistry* / Q000145:classification*; D008894:Milk Proteins / Q000032:analysis; D019265:Spectroscopy, Near-Infrared; D013395:Sucrose / Q000032:analysis
D005223:Fats; D008894:Milk Proteins; D013395:Sucrose; D007785:Lactose