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      Geographical origin of green tea identification using LASSO and ANOVA

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          Abstract

          Abstract To standardize the tea export market and guarantee the interest of consumers, the adulteration problem in Taiping Houkui tea should be eliminated. In this study, a screening scheme comprising chemometrics and statistical analysis was proposed to estimate the geographical origin of Taiping Houkui tea. A total of 11 metal ions in Taiping Houkui tea were detected by performing a chemometric experiment. The key variables that can be used to identify the geographical origin of Taiping Houkui tea were screened using the least absolute shrinkage and selection operator method (LASSO). The statistical significance of selected key variables was also tested by analysis of variance (ANOVA), which confirmed the effectiveness of the LASSO. The proposed strategy was verified by the experimental testing and has great potential for determining the geographical origin of green tea.

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          Most cited references36

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          Regression Shrinkage and Selection Via the Lasso

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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation

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                Author and article information

                Journal
                cta
                Food Science and Technology
                Food Sci. Technol
                Sociedade Brasileira de Ciência e Tecnologia de Alimentos (Campinas, SP, Brazil )
                0101-2061
                1678-457X
                2022
                : 42
                : e41922
                Affiliations
                [02] Huangshan Anhui orgnameHuangshan University orgdiv1School of Tourism China
                [01] Hefei Anhui orgnameAnhui University orgdiv1School of Electrical Engineering and Automation China
                Article
                S0101-20612022000101189 S0101-2061(22)04200001189
                10.1590/fst.41922
                fb407af3-1b17-4b71-8315-8505ea873c77

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 10 May 2022
                : 19 March 2022
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 36, Pages: 0
                Product

                SciELO Brazil

                Categories
                Original Article

                tea quality estimation,analysis of variance (ANOVA),least absolute shrinkage and selection operator (LASSO),feature extraction

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