HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea. (15th June 2021)

• Record Type: Journal Article
• Authors: Peng, Tian-Qin; Yin, Xiao-Li; Gu, Hui-Wen; Sun, Weiqing; Ding, Baomiao; Hu, Xian-Chun; Ma, Li-An; Wei, Shu-Dong; Liu, Zhi; Ye, Shi-Yi
• Is Part Of: Food chemistry. Volume 347(2021)
• Publication Date: 2021-06-15
• Subjects: HPLC-DAD fingerprints -- MCR-ALS -- Tea authentication -- Green tea -- Plucking seasons; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.128959
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: HPLC-DAD fingerprints strategy was developed for authentication of plucking seasons of tea. Co-eluted information were resolved by MCR-ALS from the HPLC-DAD fingerprints data. A total of 57 components were resolved and used for the discrimination of plucking seasons. SVM and PLS-DA were used to establish models to distinguish plucking seasons of tea. Characteristic components selected by VIP has identical predictive ability as the original model. Abstract: Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.