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GC-IMS分析典型浓香型白酒挥发性成分差异 被引量:7

Difference of volatile components of typical strong-flavor Baijiu analyzed by gas chromatography-ion mobility spectrometry
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摘要 以五粮液、泸州老窖、剑南春、洋河、中华杜康、古井贡酒等8种典型浓香型白酒为试样,采用气相色谱-离子迁移谱(GC-IMS)技术分析白酒中挥发性物质成分,同时采用指纹图谱、动态主成分分析(DPCA)和欧氏距离对样品进行鉴别。结果表明,样品中共检测出挥发性成分34种,其中酯类17种,醛类7种,醇类6种,酮类2种,酸类1种和吡嗪类1种;指纹图谱结果表明,不同样品中均检出己酸乙酯、戊酸乙酯、丙酸乙酯、丁酸乙酯、乙酸丁酯等物质,其中己酸乙酯为浓香型白酒的标志性挥发性成分,不同样品其他挥发性物质成分存在特征差异,五粮液酒样中挥发性物质成分在种类和含量上最突出;DPCA和欧氏距离结果表明,该方法能区分不同类型浓香型白酒特征挥发性成分。 Using eight typical strong-flavor Baijiu(Chinese liquor),such as Wuliangye,Luzhoulaojiao,Jiannanchun,Yanghe,Zhonghuadukang and GujingGongjiu as test samples,the volatile flavor compounds in Baijiu were analyzed by gas chromatography-ion mobility spectrometry(GC-IMS)technology,and the samples were identified by fingerprint,dynamic principal component analysis(DPCA)and Euclidean distance.The results showed that a total of 34 volatile components were detected in samples,including 17 esters,7 aldehydes,6 alcohols,2 ketones,1 acid and 1 pyrazine.Results of fingerprint showed that ethyl hexanoate,ethyl valerate,ethyl propionate,ethyl butyrate,butyl acetate were detected in different samples.Among them,ethyl hexanoate was the signature volatile component in strong-flavor Baijiu.Other volatile components in different samples existed characteristic differences,and the types and contents of volatile components in Wuliangye samples were the most prominent.Results of DPCA and Euclidean distance showed that the method could distinguish characteristic volatile components of different types of strong-flavor Baijiu.
作者 付勋 聂青玉 张艳 李翔 刘丹 谭鹏昊 冯婷婷 唐廷廷 FU Xun;NIE Qingyu;ZHANG Yan;LI Xiang;LIU Dan;TAN Penghao;FENG Tingting;TANG Tingting(College of Agriculture Science and Technology,Chongqing Three Gorges Vocational College,Wanzhou 404155,China)
出处 《中国酿造》 CAS 北大核心 2021年第11期178-183,共6页 China Brewing
基金 重庆三峡职业学院院级科研重点项目(cqsx202001) 重庆市教委科学技术研究项目资助(KJ1503407)。
关键词 GC-IMS 浓香型白酒 挥发性成分 指纹图谱 动态主成分分析 gas chromatography-ion mobility spectrometry strong-flavor Baijiu volatile components fingerprint dynamic principal component analysis
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