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 (R2p) 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.
Key words:Near/mid-infrared spectroscopy;Protein;Milk powder;Least-squares support vector machines
吴迪,曹芳,冯水娟,何勇*. 基于支持向量机算法的红外光谱技术在奶粉蛋白质含量快速检测中的应用[J]. 光谱学与光谱分析, 2008, 28(05): 1071-1075.
WU Di,CAO Fang,FENG Shui-juan,HE Yong*. Application of Infrared Spectroscopy Technique to Protein Content Fast Measurement in Milk Powder Based on Support Vector Machines. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(05): 1071-1075.
[1] Helrich K. Official Methods of Analysis of the Association of Official Analytical Chemists (15th edn.). Arlington: Association of Official Analytical Chemists Inc., 1990. 807. [2] Kamizake N K K, Goncalves M M, Zaia C T B V, et al. Journal of Food Composition and Analysis, 2003, 16: 507. [3] Reid M L, Woodcock T, O’Donnell P C, et al. Food Research International, 2005, 38: 1109. [4] HE Yong, FENG Shui-juan, LI Xiao-li, et al(何 勇, 冯水娟, 李晓丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(11): 2021. [5] TANG Yan-feng, ZHANG Zhuo-yong, FAN Guo-qiang(汤彦丰, 张卓勇, 范国强). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(4): 521. [6] LIU Shu-hua, ZHANG Xue-gong, ZHOU Qun, et al(刘沭华, 张学工, 周 群, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(6): 878. [7] He Y, Wu D, Feng S J. International Journal of Food Properties, 2007, 10(1): 1. [8] Sorensen L K, Jepsen R. International Dairy Journal, 1998, 8: 863. [9] Hermida M, Gonzalez J M, Sanchez M, et al. International Dairy Journal, 2001, 11: 93. [10] Wang Li, Lee S C F, Wang Xiao-ru, et al. Food Chemistry, 2006, 95: 529. [11] Roychoudhury P, Harvey M L, McNeil B. Analytica Chimica Acta, 2006, 561: 218. [12] Bra’s P. L, Bernardino A S, Lopes A J, et al. Chemometrics and Intelligent Laboratory Systems, 2005, 75: 91. [13] Reeves Ⅲ J B, Delwiche S R. Appl. Spectrosc., 1997, 51: 1200. [14] Reeves Ⅲ J B. J. Near Infrared Spectrosc., 1994, 2: 49. [15] Vapnik V. Statistical Learning Theory. New York: John Wiley and Sons Inc., 1998. [16] Suykens J A K, Gestel T V, De Brabanter J, et al. Least Squares Support Vector Machines. Singapore: World Scientific Publishing, 2002. [17] Suykens J A K, Vanderwalle J. Neural Processing Letters, 1999, 9(3): 293. [18] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Basic and Application of Near Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. 369. [19] Zhou Qun, Sun Su-qin, Yu Lu, et al. Journal of Molecular Structure, 2006, 799: 77.