HPLC-DAD fingerprints combined with chemometric techniques for the authentication of plucking seasons of Laoshan green tea.
Food Chem
; 347: 128959, 2021 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-33465688
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Té
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Cromatografía Líquida de Alta Presión
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Informática
/
Análisis de los Alimentos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Food Chem
Año:
2021
Tipo del documento:
Article
País de afiliación:
China