Extraction and determination of phenolic compounds in Chinese teas using a novel compound salt aqueous two-phase system coupled with multivariate chemometric methods. (1st June 2022)

• Record Type: Journal Article
• Authors: Zhang, Xiao-Hua; Zheng, Jing-Jing; Qing, Xiang-Dong; Lin, Fang; Yuan, Yan-Ting; Yang, Kai-Long; Zhang, Jing-Zhe; Gu, Hui-Wen
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 162(2022)
• Publication Date: 2022-06-01
• Subjects: Chinese tea -- Phenolic compounds -- HPLC-DAD -- Aqueous two-phase systems -- Chemometrics; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2022.113477
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The phenolic compounds are the primary ingredients contributing to the color, fragrance, and health care functions of tea. The present study sought to extract the phenolic compounds from Chinese tea samples using a novel compound salt aqueous two phase system (ATPS) with iso -propanol and two inorganic salts (sodium citrate and ammonium sulfate). The results showed that the analytes were mainly distributed in the alcohol-rich top phase within the recovery range of 82.54–110.73%. Additionally, the alternating trilinear decomposition (ATLD) algorithm combined with HPLC-DAD was employed for the simultaneous rapid quantitative analysis of six phenolic compounds in four Chinese tea samples within 7.0 min. Based on the quantitative data obtained by the proposed ATPS-ATLD method, the principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods were adopted to classify and analyze the four types of Chinese teas. Overall, the proposed ATPS-ATLD method could be a potential approach for distinguishing the types of Chinese teas. Highlights: A novel compound salt ATPS was proposed for the first time. Tea polyphenols in Chinese teas can be accurately quantified within only 7.0 min. The ATPS-ATLD-based method was developed. The pattern recognition combined with high-way quantitative analysis was developed. The types of Chinese tea were successfully classified and analyzed.