FOOD SCIENCE ›› 2014, Vol. 35 ›› Issue (6): 83-89.doi: 10.7506/spkx1002-6630-201406017

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Comparative Analysis of Volatile Components of Chinese Rice Wines from Different Areas

WANG Pei-xuan1,2, MAO Jian1,2,3,*, LI Xiao-zhong1, LIU Yun-ya1,2,3, MENG Xiang-yong1,2,3   

  1. 1. School of Food Science and Technology, Jiangnan University, Wuxi 214122, China;
    2. National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi 214122, China;
    3. National Engineering Research Center of Chinese Rice Wine, Shaoxing 312000, China
  • Received:2014-02-26 Revised:2014-03-14 Online:2014-03-25 Published:2014-04-04
  • Contact: MAO Jian E-mail:li_xzh@sina.com

Abstract:

The volatile compounds of 12 brands of Chinese rice wines (CRW) from different areas were
determined and compared by headspace solid phase micro-extraction in combination with gas chromatographymass
spectrometry (HS-SPME-GC-MS). A total of 142 volatile compounds were detected in samples of these
12 CRW brands, including esters, alcohols, aldehydes, ketones, acids, phenols and hydrocarbons accounting
for 33.04%, 39.26%, 24.64%, 0.37%, 0.63%, 0.28% and 1.36% of the total amount of identified compounds,
respectively. Thirteen volatile compounds were common to these CRW brands. Principal component analysis
(PCA) was adopted to investigate their main volatile components and cluster analysis was used to cluster the
different CRW brands. The PCA results showed that isobutyl alcohol, benzyl acetate, ethyl lactate, ethyl acetate,
phenethyl alcohol and furfural were the main volatile compounds, which are mainly responsible for the overall
flavor and taste of CRW. We established three clusters consisting of different volatile components according to
cluster analysis, which could objectively reflect the real information.
Key words: Chinese rice wine; gas chromatography-mass spectrometry (GC-

Key words: Chinese rice wine, gas chromatography-mass spectrometry (GC-MS), headspace solid phase micro-extraction (HS-SPME), volatile compounds, principal component analysis, cluster analysis

CLC Number: