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[Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder].
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Jul; 34(7):1826-30.GP

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

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Pub Type(s)

Journal Article

Language

chi

PubMed ID

25269289

Citation

Zhang, Chu, et al. "[Application of Near-infrared Spectroscopy to Distinguish Brands of Soy Milk Powder and Fake Soy Milk Powder]." Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, vol. 34, no. 7, 2014, pp. 1826-30.
Zhang C, Liu F, Kong WW, et al. [Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder]. Guang Pu Xue Yu Guang Pu Fen Xi. 2014;34(7):1826-30.
Zhang, C., Liu, F., Kong, W. W., & He, Y. (2014). [Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder]. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, 34(7), 1826-30.
Zhang C, et al. [Application of Near-infrared Spectroscopy to Distinguish Brands of Soy Milk Powder and Fake Soy Milk Powder]. Guang Pu Xue Yu Guang Pu Fen Xi. 2014;34(7):1826-30. PubMed PMID: 25269289.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - [Application of near-infrared spectroscopy to distinguish brands of soy milk powder and fake soy milk powder]. AU - Zhang,Chu, AU - Liu,Fei, AU - Kong,Wen-Wen, AU - He,Yong, PY - 2014/10/2/entrez PY - 2014/10/2/pubmed PY - 2015/8/19/medline SP - 1826 EP - 30 JF - Guang pu xue yu guang pu fen xi = Guang pu JO - Guang Pu Xue Yu Guang Pu Fen Xi VL - 34 IS - 7 N2 - 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. SN - 1000-0593 UR - https://www.unboundmedicine.com/medline/citation/25269289/[Application_of_near_infrared_spectroscopy_to_distinguish_brands_of_soy_milk_powder_and_fake_soy_milk_powder]_ L2 - https://www.ingentaconnect.com/openurl?genre=article&issn=1000-0593&volume=34&issue=7&spage=1826&aulast=Zhang DB - PRIME DP - Unbound Medicine ER -