Geographical origin identification and chemical markers screening of Chinese green tea using two-dimensional fingerprints technique coupled with multivariate chemometric methods. (May 2022)

• Record Type: Journal Article
• Authors: Gu, Hui-Wen; Yin, Xiao-Li; Peng, Tian-Qin; Pan, Yuan; Cui, Hui-Na; Li, Zhi-Quan; Sun, Weiqing; Ding, Baomiao; Hu, Xian-Chun; Zhang, Zi-Hong; Liu, Zhi
• Is Part Of: Food control. Volume 135(2022)
• Publication Date: 2022-05
• Subjects: Authentication and traceability -- Characteristic components -- High-performance liquid chromatography-diode array detector -- Two-dimensional fingerprints -- Multivariate statistical analysis; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2021.108795
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Identifying the geographical origins of green teas produced in specific regions is of significance since the geographical origin of tea influences its quality and price greatly. In this work, a novel two-dimensional (2D) fingerprints acquired by high-performance liquid chromatography-diode array detector (HPLC-DAD) was firstly proposed to identify geographical origins of Chinese green teas. A total number of 62 chemical components were extracted from 2D HPLC-DAD fingerprints of 78 tea samples by multivariate curve resolution-alternating least squares (MCR-ALS) algorithm. Afterward, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to classify tea samples based on the extracted components. Inspection of PCA score plots of two scaling types (UV and Par) showed that tea samples from two different geographical origins have an obvious clustering tendency. As for the OPLS-DA analysis, the Q 2 cum of two types-scaling OPLS-DA models are greater than 0.75, and the total recognition rates for test set are 92.86%. What's more, according to p value of t -test, VIP values, V-plot and S-plot, 17 characteristic components were screened and identified as chemical markers to distinguish between Zhejiang teas and Shandong teas. This work indicated that the proposed strategy is suitable for identifying geographical origins of Chinese green teas. Highlights: 2D HPLC-DAD fingerprints was proposed to identify geographical origins of tea. 62 chemical components were extracted from 2D HPLC-DAD fingerprints by MCR-ALS. PCA and OPLS-DA were used to classify tea samples using the extracted components. 17 characteristic components were screened and identified as chemical markers. There are correlations between chemical components and geographical origins of tea.