SCIENCE ALERT: Chemometric analysis for differentiation Laoshan green tea harvest timing (Food Chemistry)

Editor's Note

This study presents a novel model and validated analytical method to authenticate Laoshan green teas harvested in summer versus autumn 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. 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 a proposed strategy competent for the authentication of plucking seasons of Laoshan green tea.

June 2021

Food Chemistry

DOI: https://doi.org/10.1016/j.foodchem.2020.128959

HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea

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.