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• W2952641808 endingPage "104006" @default.
• W2952641808 startingPage "104006" @default.
• W2952641808 abstract "The feasibility of using elemental profiles and chemometrics for rapid discrimination of organic and conventional green teas was investigated in this work. Organic (n1 = 50) and ordinary (n2 = 50) green teas were collected from the same tea plant. The contents of 16 elements in green teas, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, K and Ni, were analyzed using inductively coupled plasma atomic emission spectrometry (ICP-AES) after microwave-aided digestion with nitric acid. After outlier diagnosis by robust principal components analysis, the DUPLEX algorithm was performed to split the total data into training and test sets. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used for classification of the two types of green teas. As a result, the sensitivity, specificity and accuracy were 0.90, 0.95, and 0.85 for PLSDA, and 0.975, 1.00, and 0.95 for LS-SVM, respectively. The results indicate that it is possible to rapidly distinguish organic and traditional green teas by combination of elemental profiles and chemometrics methods." @default.
• W2952641808 created "2019-06-27" @default.
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• W2952641808 date "2019-09-01" @default.
• W2952641808 modified "2023-10-15" @default.
• W2952641808 title "Fusion of elemental profiles and chemometrics: Discrimination of organic and conventional green teas" @default.
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• W2952641808 doi "https://doi.org/10.1016/j.microc.2019.104006" @default.
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