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Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition.
J Pharm Biomed Anal. 2008 Dec 15; 48(5):1321-5.JP

Abstract

High performance liquid chromatography (HPLC) was identified green tea's quality level by measurement of catechins and caffeine content. Four grades of roast green teas were attempted in this work. Five main catechins ((-)-epigallocatechin gallate (EGCG), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECG), (-)-epicatechin (EC), and (+)-catechin (C)) and caffeine contents were measured simultaneously by HPLC. As a new chemical pattern recognition, support vector classification (SVC) was applied to develop identification model. Some parameters including regularization parameter (R) and kernel parameter (K) were optimized by the cross-validation. The optimal SVC model was achieved with R=20 and K=2. Identification rates were 95% in the training set and 90% in the prediction set, respectively. Finally, compared with other pattern recognition approaches, SVC algorithm shows its excellent performance in identification results. Overall results show that it is feasible to identify green tea's quality level according to measurement of main catechins and caffeine contents by HPLC and SVC pattern recognition.

Authors+Show Affiliations

School of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China. q.s.chen@hotmail.comNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18952392

Citation

Chen, Quansheng, et al. "Identification of Green Tea's (Camellia Sinensis (L.)) Quality Level According to Measurement of Main Catechins and Caffeine Contents By HPLC and Support Vector Classification Pattern Recognition." Journal of Pharmaceutical and Biomedical Analysis, vol. 48, no. 5, 2008, pp. 1321-5.
Chen Q, Guo Z, Zhao J. Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition. J Pharm Biomed Anal. 2008;48(5):1321-5.
Chen, Q., Guo, Z., & Zhao, J. (2008). Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition. Journal of Pharmaceutical and Biomedical Analysis, 48(5), 1321-5. https://doi.org/10.1016/j.jpba.2008.09.016
Chen Q, Guo Z, Zhao J. Identification of Green Tea's (Camellia Sinensis (L.)) Quality Level According to Measurement of Main Catechins and Caffeine Contents By HPLC and Support Vector Classification Pattern Recognition. J Pharm Biomed Anal. 2008 Dec 15;48(5):1321-5. PubMed PMID: 18952392.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition. AU - Chen,Quansheng, AU - Guo,Zhiming, AU - Zhao,Jiewen, Y1 - 2008/09/17/ PY - 2008/07/25/received PY - 2008/09/05/revised PY - 2008/09/06/accepted PY - 2008/10/28/pubmed PY - 2009/4/25/medline PY - 2008/10/28/entrez SP - 1321 EP - 5 JF - Journal of pharmaceutical and biomedical analysis JO - J Pharm Biomed Anal VL - 48 IS - 5 N2 - High performance liquid chromatography (HPLC) was identified green tea's quality level by measurement of catechins and caffeine content. Four grades of roast green teas were attempted in this work. Five main catechins ((-)-epigallocatechin gallate (EGCG), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECG), (-)-epicatechin (EC), and (+)-catechin (C)) and caffeine contents were measured simultaneously by HPLC. As a new chemical pattern recognition, support vector classification (SVC) was applied to develop identification model. Some parameters including regularization parameter (R) and kernel parameter (K) were optimized by the cross-validation. The optimal SVC model was achieved with R=20 and K=2. Identification rates were 95% in the training set and 90% in the prediction set, respectively. Finally, compared with other pattern recognition approaches, SVC algorithm shows its excellent performance in identification results. Overall results show that it is feasible to identify green tea's quality level according to measurement of main catechins and caffeine contents by HPLC and SVC pattern recognition. SN - 0731-7085 UR - https://www.unboundmedicine.com/medline/citation/18952392/Identification_of_green_tea's__Camellia_sinensis__L____quality_level_according_to_measurement_of_main_catechins_and_caffeine_contents_by_HPLC_and_support_vector_classification_pattern_recognition_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0731-7085(08)00494-9 DB - PRIME DP - Unbound Medicine ER -