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Stepwise Identification of Six Tea (Camellia sinensis (L.)) Categories Based on Catechins, Caffeine, and Theanine Contents Combined with Fisher Discriminant Analysis

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Abstract

Six tea types were stepwise discriminated based on their catechins, caffeine, and theanine contents of total 436 tea samples collected worldwide, combined with Fisher classification pattern recognition. Those tea samples of six types (green, white, yellow, oolong, black, and dark teas) of commonly consumed teas with different processing methods were analyzed in this work. Five main catechins ((−)-epigallocatechin gallate (EGCG), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-epicatechin (EC), and (+)-catechin (C)), caffeine, and theanine contents were accurately measured by high-performance liquid chromatography (HPLC). As a novel approach, stepwise identification combined with Fisher discriminant analysis was applied to develop an identification model. Several parameters, including model component factors, were optimized by cross-validation. The optimal Fisher model was achieved with caffeine, total catechins, theanine, theanine × theanine, EGCG/total catechins, and theanine × caffeine as component factors. The discrimination rates of black, dark, white, oolong, yellow, and green teas were 95.90, 100.00, 97.40, 95.70, 91.80, and 88.30 %, respectively. Compared with other pattern recognition approaches, the Fisher algorithm exhibited excellent performance in the final identification. The overall results show that this method is suitable to stepwise identify six tea categories, according to the measurements of main chemicals with catechins, caffeine, and theanine by HPLC and followed by the Fisher pattern recognition.

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Acknowledgments

This study was funded by the special public welfare research of the General Administration of Quality Supervision, Inspection, and Quarantine of the People’s Republic of China (grant no. 201410225), Natural Science Foundation in Anhui Province (grant no. 1408085MC61), and Featured Industrial Development Fund of Anhui Province.

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    Authors and Affiliations

    1. State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 230036, Hefei, People’s Republic of China

      Jingming Ning, Daxiang Li, Xianjingli Luo, Ding Ding, Yasai Song, Zhengzhu Zhang & Xiaochun Wan

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    1. Jingming Ning
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    2. Daxiang Li
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    Correspondence to Xiaochun Wan.

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    All financial supports were declared. We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in or the review of the entitled, “Stepwise identification of six tea (Camellia sinensis (L.)) categories based on measurements of catechins, caffeine, and theanine contents combined with Fisher discriminant analysis.”

    Conflict of Interest

    Jingming Ning declares that he has no conflict of interest. Daxiang Li declares that he has no conflict of interest. Xianjingli Luo declares that she has no conflict of interest. Ding Ding declares that she has no conflict of interest. Yasai Song declares that he has no conflict of interest. Zhengzhu Zhang declares that he has no conflict of interest. Xiaochun Wan declares that he has no conflict of interest.

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    The content has not been published or submitted for publication elsewhere. All authors have contributed significantly and are in agreement with the content of the manuscript. We have declared our financial support and relationships in our manuscript. All the tables and figures are prepared originally by us.

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    Jingming Ning and Daxiang Li contributed equally to this work.

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    Ning, J., Li, D., Luo, X. et al. Stepwise Identification of Six Tea (Camellia sinensis (L.)) Categories Based on Catechins, Caffeine, and Theanine Contents Combined with Fisher Discriminant Analysis. Food Anal. Methods 9, 3242–3250 (2016). https://doi.org/10.1007/s12161-016-0518-2

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    • DOI: https://doi.org/10.1007/s12161-016-0518-2

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