AI Article Synopsis
- Researchers analyzed Laoshan green teas harvested in summer and autumn using HPLC-DAD to identify 57 chemical components.
- They utilized multivariate techniques, specifically PCA, SVM, and PLS-DA, achieving high accuracy rates, with SVM and PLS-DA reaching 100% recognition for certain datasets.
- The study confirmed that the PLS-DA model based on key components selected using VIP can effectively authenticate the plucking seasons of Laoshan green tea.
Article 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.
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| http://dx.doi.org/10.1016/j.foodchem.2020.128959 | DOI Listing |
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