Differentiating grades of Xihu Longjing teas according to the contents of ten major components based on HPLC-DAD in combination with chemometrics. (August 2020)

• Record Type: Journal Article
• Authors: Gu, Hui-Wen; Yin, Xiao-Li; Ma, Ying-Xiang; Wang, Jing; Yang, Fan; Sun, Weiqing; Ding, Baomiao; Chen, Ying; Liu, Zhi
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 130(2020)
• Publication Date: 2020-08
• Subjects: HPLC-DAD -- Grade differentiation -- Xihu Longjing teas -- Principal component analysis -- Hierarchical cluster analysis; ATLD alternating trilinear decomposition -- CAF caffine -- DC catechin -- EC epicatechin -- ECG (−)-epicatechin gallate -- EGC (−)-epigallocatechin -- EGCG (−)-epigallocatechin gallate -- GA gallic acid -- GC (−)-gallocatechin -- GCG (−)-gallocatechin gallate -- HCA hierarchical cluster analysis -- HPLC-DAD high performance liquid chromatography-diode array detection -- PCA principal component analysis -- TBM theobromine; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2020.109688
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: A new chemometrics-assisted high performance liquid chromatography-diode array detection (HPLC-DAD) method was developed to rapidly quantify ten major components in nine Xihu Longjing tea samples. This new strategy proved to be a useful tool for handling the co-eluted problems during chromatographic analysis and enabled the quantification of ten major components in tea samples within only 10 min. The average recoveries of these analytes in tea samples ranged from 100.0% to 112.7% except (−)-epigallocatechin gallate (EGCG). The relative standard deviations of intra- and inter-day assays were in the range of 0.14%–5.09% for all the ten analytes. Furthermore, the quantitative results were used as input variables to perform principal component analysis (PCA) and hierarchical cluster analysis (HCA) for differentiating tea grades. Results demonstrated that the nine different grades of Xihu Longjing tea samples were successfully divided into three categories. This study showed that it is possible to perform tea grades differentiation according to the contents of major components using chemometric pattern recognition methods. Highlights: A chemometrics-assisted HPLC-DAD method was developed for quantitative analysis of teas. This new strategy proved to be a very useful tool for handling the co-eluted problems. Ten major components in tea samples can be accurately quantified with only 10 min. PCA and HCA were performed for tea grades differentiation according to the quantitative results. There are some correlations between the contents of major components and the tea grades.