title: Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world.
abstract: NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks.
authors.affiliation: JEOL DATUM LTD., Tokyo, Japan. masako@jeol.co.jp
authors.forename: Masako
authors.initials: M
authors.lastname: Fujiwara
authors.forename: Itiro
authors.initials: I
authors.lastname: Ando
authors.forename: Kazunori
authors.initials: K
authors.lastname: Arifuku
mesh_terms: D000465:Algorithms; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D011522:Protons; D012815:Signal Processing, Computer-Assisted; D013662:Tea
chemical_list: D011522:Protons; D013662:Tea