title: [Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines].
abstract: In the present study, the JASCO Model FTIR-4 000 fourier transform infrared spectrometer (Japan) was used, with a valid range of 7 800-350 cm(-1). Seven brands of milk powder were bought in a local supermarket. Milk powder was compressed into a uniform tablet with a diameter of 5 mm and a thickness of 2 mm, and then scanned by the spectrometer. Each sample was scanned 40 times and the data were averaged. About 60 samples were measured for each brand, and data for 409 samples were obtained. NIRS analysis was based on the range of 4 000 to 6 666 cm(-1), while MIRS analysis was between 400 and 4 000 cm(-1). The protein content was determined by kjeldahl method and the factor 6.38 was used to convert the nitrogen values to protein. The protein content value is the weight of protein per 100 g of milk powder. The NIR data of the milk powder exhibited slight differences. Univariate analysis was not really appropriate for analyzing the data sets. From NIRS region, it could be observed that the trend of different curves is similar. The one around 4 312 cm(-1) embodies the vibration of protein. From MIRS region, it could be determined that there are many differences between transmission value curves. Two troughs around 1 545 and 1 656 cm(-1) stand for the vibration of amide I and II bands of protein. The smoothing way of Savitzky-Golay with 3 segments and zero polynomials and multiplicative scatter correction (MSC) were applied for denoising. First 8 important principle components (PCs), which were obtained from principle component analysis (PCA), were the optimal input feature subset. Least-squares support vector machines was applied to build the protein prediction model based on infrared spectral transmission value. The prediction result was better than that of traditional PLS regression model as the determination coefficient for prediction (R(p)2) is 0.951 7 and root mean square error for prediction (RMSEP) is 0.520 201. These indicate that LS-SVM is a powerful tool for spectral analysis. Moreover, the study compared the prediction results based on near infrared spectral data and mid-infrared spectral data. The results showed that the performance of the model with mid-infrared spectral data was better than the one with near infrared spectra data. It was concluded that infrared spectroscopy technique can do the quantification of protein content in milk powder fast and non-destructively and the process was simple and easy to operate. The results of this study can be used for the design of a simple and non-destructive spectra sensor for the quantitative of protein content in milk powder.
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors: Di
authors: D
authors: Wu
authors: Fang
authors: F
authors: Cao
authors: Shui-Juan
authors: SJ
authors: Feng
authors: Yong
authors: Y
authors: He
mesh_terms: D000818:Animals; D016018:Least-Squares Analysis; D008892:Milk / Q000737:chemistry*; D008894:Milk Proteins / Q000032:analysis*; D011208:Powders; D025341:Principal Component Analysis; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*
chemical_list: D008894:Milk Proteins; D011208:Powders