Authentication of the production season of Xinyang Maojian green tea using two-dimensional fingerprints coupled with chemometric multivariate calibration and pattern recognition analysis. (15th February 2023)

• Record Type: Journal Article
• Authors: Zhang, Xiao-Hua; Liu, Ren-Jun; Zheng, Jing-Jing; Qing, Xiang-Dong; Yang, Kai-Long; Zhang, Ya-Qian; Pan, Le-Yuan; Nie, Jin-Fang
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 176(2023)
• Publication Date: 2023-02-15
• Subjects: HPLC-DAD fingerprints -- ATLD-MCR -- Chemometric pattern recognition analysis -- Production season -- Xinyang Maojian green tea; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2023.114556
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: This study aimed to authenticate the production season of Xinyang Maojian green tea, and screen and identify its characteristic components through non-targeted metabonomics methods based on two-dimensional fingerprints combined with chemometric analysis. Firstly, two-dimensional fingerprints of spring and autumn teas were obtained through HPLC-DAD analysis to form a three-dimensional array (retention time × absorption wavelength × sample). Subsequently, the multiple co-elution peaks and spectral profiles in two-dimensional HPLC-DAD fingerprints were resolved by using alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) algorithm. We obtained the relative concentration matrix C (24 × 122), which was further used to distinguish the production season of Xinyang Maojian green tea through chemometric pattern recognition analysis. The evaluation results of both orthogonal partial least squares-discriminant analysis (OPLS-DA) and partial least squares-discriminant analysis (PLS-DA) models were better than those of PCA models, and could effectively distinguish the production season of Xinyang Maojian green teas. Moreover, 5 variables were selected through VIP method to build new UV-scaling and Par-scaling OPLS-DA models. In conclusion, the following characteristic components were identified in accordance with the analytical standards and published data: gallocatechin (GC), theobromine (THB), epigallocatechin gallate (EGCG), gallocatechin gallate (GCG), and epicatechin gallate (ECG). Highlights: The 2D HPLC-DAD fingerprints of Xinyang Maojian green tea was conducted. ATLD-MCR was used to resolve the multiple co-elution peaks and spectral profiles. PCA, PLS-DA and OPLS-DA were performed based on relative concentration matrix C. The picking seasons of Xinyang Maojian green tea can be well distinguished. 5 variables selected by VIP has identical predictive ability as the original model.