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• W4285403857 endingPage "111689" @default.
• W4285403857 startingPage "111689" @default.
• W4285403857 abstract "A new tea classification method based on X-ray photoelectron spectroscopy. • X-ray photoelectron spectroscopy-based method was proposed for tea classification. • Relationship between binding energy and tea polyphenols was investigated. • More than 90% of tested tea can be successfully classified within minutes. • Results from theory and experiment provide an innovative methodology reference. The classification of tea products is nowadays mainly determined by sensory assessment and chemical analysis methods. These methods are subjective, time-consuming, and laborious. In this work, a rapid analytical method for tea classification was proposed on the basis of X-ray photoelectron spectroscopy (XPS) and quantum chemical calculation. A total of 56 kinds of tea products were studied. By utilizing the data fusion strategy, the correlation between XPS peak parameters and tea characteristics was established. The quantum chemical calculations of the core-level ionization potentials deepen the understanding of the XPS features. The binding energy of the main fitted peak for O 1s, BE1 O1s , was found to have a good correlation with tea polyphenols contents, which can be used to classify tea products into six tea types (black, dark, green, oolong, white, and yellow tea) with an accuracy greater than 90 %. The results suggest that the proposed XPS method is suitable for the rapid discrimination of tea classification, which contributes to the efficient application of tea." @default.
• W4285403857 created "2022-07-14" @default.
• W4285403857 creator A5002121205 @default.
• W4285403857 creator A5014097109 @default.
• W4285403857 date "2022-10-01" @default.
• W4285403857 modified "2023-09-29" @default.
• W4285403857 title "A rapid classification method of tea products utilizing X-ray photoelectron spectroscopy: Relationship derived from correlation analysis, modeling, and quantum chemical calculation" @default.
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• W4285403857 doi "https://doi.org/10.1016/j.foodres.2022.111689" @default.
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