Application of Near-Infrared Spectroscopy to Distinguish Brands of Soy Milk Powder and Fake Soy Milk Powder

作者：Zhang Chu; Liu Fei; Kong Wen-wen; He Yong^*

来源：Spectroscopy and Spectral Analysis, 2014, 34(7): 1826-1830.

DOI：10.3964/j.issn.1000-0593(2014)07-1826-05

• Near-infrared spectroscopy
• Soymilk powder
• x-loading weights
• Back-propagation neural network

Near-infrared spectroscopy combined with chemometrics was used to investigate the feasibility of identifying different brands of soymilk powder and the counterfeit soymilk powder products. For this purpose, partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA) and back-propagation neural network (BPNN) were employed as pattern recognition methods to class ify soymilk powder samples. The performances of different pretreatments of raw spectra were also compared by PLS-DA. PLS-DA models based on De-trending and multiplicative scatter correction (MSC) combined with De-trending (MSC+De-trending) spectra obtained best results with 100% prediction accuracy, respectively. Six and seven optimal wavenumbers selected by x-loading weights of the best two PLS-DA models were used to build LDA and BPNN models. Results showed that BPNN performed best and correctly classified 100% of the soymilk powder samples for both the calibration and the prediction set. The overall results indicated that NIR spectroscopy could accurately identify branded and counterfeit soymilk powder products.

• 出版日期2014-7
• 单位浙江大学