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采用 HS-SPME-GC-MS 结合多元统计分析快速鉴别中国啤酒花品种(蛇麻)的挥发性指纹图谱。

Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

机构信息

College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi, Xinjiang, China.

Key Laboratory of Food Science and Safety, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, China.

出版信息

J Sci Food Agric. 2018 Aug;98(10):3758-3766. doi: 10.1002/jsfa.8889. Epub 2018 Mar 2.

DOI:10.1002/jsfa.8889
PMID:29344957
Abstract

BACKGROUND

Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier.

RESULTS

We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops.

CONCLUSION

Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry.

摘要

背景

啤酒花赋予啤酒风味,其挥发性成分决定了各种啤酒花品种和质量的特点。指纹图谱分析,特别是风味指纹图谱分析,通常用于识别“风味产品”,因为风味描述的不一致可能导致啤酒质量的定义不正确。与风味指纹图谱分析相比,挥发性指纹图谱分析更简单、更容易。

结果

我们采用顶空固相微萃取-气相色谱-质谱联用技术结合相似性分析和主成分分析(PCA)进行挥发性指纹图谱分析,以评估和区分三种中国主要啤酒花。鉴定出 84 种挥发性物质,分为七类。基于相似性分析的挥发性指纹图谱分析没有产生明显的结果。相比之下,基于 PCA 的挥发性指纹图谱分析可以识别啤酒花的品种和质量。潜在变量解释了三种啤酒花品种的差异。此外,聚类树状图和主成分得分图描述了啤酒花的差异和分类。

结论

挥发性指纹图谱分析结合多元统计分析可以快速区分三种中国主要啤酒花的不同品种和质量。此外,该方法可作为其他领域的参考。 © 2018 化学工业协会。

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