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Characterization and identification of different Chinese fermented vinegars based on their volatile components
Author(s) -
Liu Lichun,
Hu Huiyu,
Yu Yanpeng,
Zhao Jiahui,
Yuan Lanlan,
Liu Shanshan,
Zhao Shanshan,
Huang Rong,
Xie Jianhua,
Shen Mingyue
Publication year - 2021
Publication title -
journal of food biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 47
eISSN - 1745-4514
pISSN - 0145-8884
DOI - 10.1111/jfbc.13670
Subject(s) - partial least squares regression , principal component analysis , linear discriminant analysis , chemometrics , raw material , food science , aroma , mathematics , gas chromatography–mass spectrometry , fermentation , steaming , chemistry , chromatography , mass spectrometry , statistics , organic chemistry
In this study, volatile components of 40 Chinese fermented vinegar samples, made from different raw materials, starters, and processing technologies, were collected from different geographic origins in China (Shanxi, Jiangsu, Sichuan, and Fujian Province) and their volatile components were analyzed by headspace‐solid‐phase microextraction‐gas chromatography‐mass spectrometry. Sixty‐two aroma compounds have been identified by NIST library combined with retention index, mainly including esters, heterocyclics, acids, aldehydes, and ketones. In addition, multivariate analysis including principal component analysis and partial least squares‐discriminant analysis (PLS‐DA) were carried out to discriminate vinegars based on their composition of volatile components. For PLS‐DA models, analysis of variance (ANOVA) or variable importance in the projection (VIP) value were used to select variables with the highest discriminatory power, and the Kennard–Stone algorithm was used to select the training and testing samples. The PLS‐DA models (ANOVA or VIP) all provided a classification accuracy of 100% for the training set, and subsequent application of these models allowed the grouping of unknown samples (testing set) according to their characteristics (raw materials and processing technology). Practical applications Traditional Chinese vinegars have a long history but nowadays adulterations of them are becoming a problem in the market. In this study, Chinese fermented vinegars from different varieties were identified based on volatile composition. We found that starter cultures and fermentation process have the greatest influence on the volatile components of vinegars, while the influence of raw material and steaming of raw material are weaker volatile components. Then, partial least squares‐discriminant analysis models, we carried out could successfully be applied to predict unknown vinegar samples based on a database of volatile components. This study provided a strategy to detect the identity of different vinegars, which can also be used to monitor the quality and safety of traditional Chinese vinegars.