食品与发酵工业 >

2021 , Vol. 47 >Issue 8: 212 - 217

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.025511

分析与检测

基于GC-MS结合化学计量学的浓香型白酒分类方法

  • 胡雪 ,
  • 李锦松 ,
  • 唐永清 ,
  • 张良 ,
  • 钱宇 ,
  • 赵金松
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  • 1(四川轻化工大学 生物工程学院,四川 自贡,643000)
    2(泸州老窖集团有限责任公司,四川 泸州,646000)
    3(四川大学 化学学院,四川 成都,610064)
    4(川酒集团,四川 成都,610000)
硕士研究生(钱宇讲师和赵金松教授级高级工程师为共同通讯作者,E-mail:qianyu12@mails.ucas.ac.cn;420940314@qq.com)

网络出版日期: 2021-05-20

基金资助

四川省科技计划项目(2019YJ0698);四川轻化工大学人才项目(2017RCL48)

收起

Classification of strong-flavor Baijiu based on GC-MS combined with chemometric method

  • HU Xue ,
  • LI Jinsong ,
  • TANG Yongqing ,
  • ZHANG Liang ,
  • QIAN Yu ,
  • ZHAO Jinsong
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  • 1(College of Biological Engineering, Sichuan University of Science & Engineering, Zigong 643000, China)
    2(Luzhou Laojiao Group Co., LTD., Luzhou 646000, China)
    3(College of Chemistry, Sichuan University, Chengdu 610064, China)
    4(Sichuan Liquor Group, Chengdu 610000, China)

Online published: 2021-05-20

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摘要

为实现不同品牌浓香型白酒的快速分类和鉴别,该研究以6个不同品牌的浓香型白酒为研究对象,采用气相色谱-质谱法对样品的风味成分进行定性与定量研究,并结合聚类分析,主成分分析及偏最小二乘判别分析等化学计量学手段对白酒的品牌进行识别和分类。结果显示,不同品牌的浓香型白酒间风味物质的含量和数量均存在显著差异(P<0.05);聚类分析,主成分分析和偏最小二乘判别能将6个品牌的酒样进行有效区分和聚类;利用偏最小二乘判别分析模型,预测结果准确率达100%。综上所述,利用风味成分之间的差异及结合适当的化学计量学手段可以快速、准确的对白酒样品的品牌进行区分和鉴别,可以为白酒的溯源及真实性鉴定提供理论支撑。

关键词： 浓香型白酒; 分类; 气相色谱-质谱串联; 聚类分析; 主成分分析; 偏最小二乘判别分析

本文引用格式

胡雪 , 李锦松 , 唐永清 , 张良 , 钱宇 , 赵金松 . 基于GC-MS结合化学计量学的浓香型白酒分类方法[J]. 食品与发酵工业, 2021 , 47(8) : 212 -217 . DOI: 10.13995/j.cnki.11-1802/ts.025511

Abstract

In order to construct a method for the rapid classification and identification of different commercial strong-flavor Baijiu, the flavor components in strong-flavor Baijiu of six different brands were studied qualitatively and quantitatively by gas chromatography-mass spectrometry (GC-MS). Furthermore, different brands of liquor were identified and classified by chemometric methods, such as hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA). The results showed that there were significant differences (P<0.05) in the content and quantity of flavor components from different commercial strong-flavor Baijiu. Using HCA, PCA and PLS-DA methods, the brands of the samples were effectively distinguished and clustered. In addition, the accuracy of the prediction results obtained using the PLS-DA model was 100%. In conclusion, the differences between flavor components and appropriate chemometric can be used to distinguish and identify the brands of strong-flavor Baijiu quickly and accurately, which can provide theoretical support for the traceability and authenticity identification of liquor.

Key words： strong-flavor Baijiu; classification; GC-MS; hierarchical cluster analysis; principal component analysis; partial least squares discrimination analysis

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