title: [Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder].
abstract: 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 chi-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.
authors.forename: Chu
authors.initials: C
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Liu
authors.forename: Wen-Wen
authors.initials: WW
authors.lastname: Kong
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D002138:Calibration; D016002:Discriminant Analysis; D005504:Food Analysis / Q000379:methods*; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D011208:Powders; D041942:Soy Milk / Q000737:chemistry* / Q000145:classification; D019265:Spectroscopy, Near-Infrared
chemical_list: D011208:Powders