4.7 Article

Development of a Colorimetric Sensor Array for the Discrimination of Chinese Liquors Based on Selected Volatile Markers Determined by GC-MS

期刊

JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
卷 62, 期 43, 页码 10422-10430

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jf503345z

关键词

Chinese liquor; colorimetric sensor array; GC-MS; chemometrics

资金

  1. National Natural Science Foundation [81171414, 81271930, 31171684]
  2. Key Technologies R&D Program of Sichuan Province of China [2013FZ0043, 2010NZ0093]
  3. Key Technologies R&D Program of China [2012BAI19B03]
  4. Sharing fund of Chongqing University

向作者/读者索取更多资源

A new colorimetric sensor array was developed for the discrimination of 12 high-alcoholic Chinese base liquors from Luzhou Co., Ltd., and 15 commercial Chinese liquor of different brands as well as flavor types. Seventeen volatile compounds within four chemical groups were determined as markers in the base liquor by GC-MS analysis and factor analysis method (FAM). A specialized colorimetric sensor array composed of 20 sensitive dots was fabricated accordingly to obtain sensitive interaction with different types of volatile markers. Discrimination of the liquor samples was subsequently performed using chemometric and statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). The results suggested that facile identification of either base liquors with high-alcoholic volume or commercial liquors of the same flavor types could be achieved by analysis of the color change profiles. The response of the sensor improved significantly in comparison with those that rely on nonspecific interactions, and no misclassification was observed for both liquor samples using two chemometric methods. Besides, it was also found that the discrimination is closely related to the characteristic flavor compounds (esters, aldehydes, and acids) and alcoholic strength in liquors, and its performance was even comparable with that of GC-MS.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据