FOOD SCIENCE ›› 2022, Vol. 43 ›› Issue (12): 219-219.doi: 10.7506/spkx1002-6630-20210806-078

• Component Analysis • Previous Articles    

Identification of Characteristic Volatiles in Vinegar Prepared with Monascus-fermented Rice during Acetic Acid Fermentation Using Multivariate Statistical Analysis

GAO Hang, XU Dandan, WANG Wenping, ZHAO Yan, ZHANG Jian, DING Jie, TAN Lei, ZHANG Xin   

  1. (1. Beijing Academy of Food Science, Beijing 100068, China;2. Beijing Food Brewing Research Institute Co. Ltd., Beijing 100050, China;3. Beijing Second Station of Food Quality Examination, Beijing 100050, China)
  • Published:2022-07-01

Abstract: An electronic nose, gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry (GC-O) were used to explore the changes of aroma compounds in vinegar prepared with Monascus-fermented rice during its acetic acid fermentation process. Different multivariate statistical analysis, such as cluster analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to investigate the differences in volatile compounds between different stages of fermentation and to identify the characteristic volatile components. The results showed that the electronic nose could clearly discriminate samples from different acetic acid fermentation stages. A total of 54 volatile compounds were identified by GC-MS and GC-O during the acetic acid fermentation process. The characteristic aroma components were n-octanol, isobutanol and ethyl valerate for the early stage; benzoic acid, ethyl palmitate, n-hexanol, 2,4-di-tert-butylphenol and ethyl lactate for the middle stage; propyl acetate, ethyl lactate and isobutyl acetate for the mid-to-late stage; L(+)-2,3-butanediol and ethyl heptanate for the late stage. The results of this study provide a theoretical basis for aroma regulation and flavor improvement of vinegar prepared with Monascus-fermented rice.

Key words: vinegar prepared with Monascus-fermented rice; acetic acid fermentation; volatile compounds; cluster analysis; principal component analysis; partial least squares-discriminant analysis

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