Open AccessMultivariate Analysis for 1H-NMR Spectra of Two Hundred Kinds of Tea in the World
Author(s) -
Masako Fujiwara,
Itiro Ando,
Kazunori Arifuku
Publication year - 2006
Publication title -
analytical sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.392
H-Index - 73
eISSN - 1348-2246
pISSN - 0910-6340
DOI - 10.2116/analsci.22.1307
Subject(s) - principal component analysis , chemistry , theanine , multivariate statistics , black tea , pattern recognition (psychology) , multivariate analysis , proton nmr , chromatography , green tea , chemometrics , categorization , artificial intelligence , analytical chemistry (journal) , statistics , food science , stereochemistry , mathematics , computer science
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.