摘要
采用顶空固相微萃取结合全二维气相色谱-质谱(Headspace solid-phase microextraction-comprehensive two dimensional gas chromatography/mass spectrometry HS-SPME-GC×GC-MS)技术,对4种保健黄酒(黄精酒、黄米酒、藜麦酒和苦荞酒)中挥发性物质的种类、含量分进行分析,并且通过主成分分析法很好地区分不同原料的保健黄酒,找出重要的组分差异特征,探究其风味成分。结果表明,GC×GC-MS检测到4种保健黄酒中挥发性组分156种,选取匹配度大于800的挥发性组分,4种保健黄酒中共鉴定出140种挥发性组分,其中包括酯类、醇类、醛酮类、酸类、烃类、含氮化合物、苯系芳烃及其它化合物等。该方法可以通过鉴定黄酒挥发性组分,寻找挥发性组分与黄酒品质之间的关系,为保健黄酒的生产优化提供一定的理论依据。
Headspace solid-phase microextraction-comprehensive two-dimensional gas chromatography/mass spectro-metry(HS-SPME-GC×GC-MS)was used to analyze the volatile components of four kinds of health yellow rice wines:Huangjing wine,yellow rice wine,Limai wine and tartary buckwheat wine.The types,contents and aroma components of volatile substances were analyzed,and the differents of them were well distinguished by princip-al component analysis method,the important component differences were found out,the flavor components wereexplored.The results showed that GC×GC-MS detected 156 volatile components in four yellow wines,and sel-ected volatile components whose matching degree was greater than 800,a total of 140 kinds of volatile compo-nents were identified in them for composition.The volatile components including esters,alcohols,aldehydes andketones,acids,hydrocarbons,nitrogen-containing compounds,benzene-based aromatic hydrocarbons and other co-mpounds,etc.This method can identify the volatile components of yellow wine and the relationship between the sexual components and the quality of health yellow rice wine,provides a certain theoretical basis for the optimization of the production.
作者
吕雅娟
盖青青
LV Yajuan;GAI Qingqing(Fenyang College of Shanxi Medical University,Fenyang 032200,Shanxi,China;National Institute of Clean-and-low-carbon Energy,Beijing 102209,China)
出处
《酿酒》
CAS
2024年第5期81-87,共7页
Liquor Making
基金
2021年度第二批吕梁市科技计划项目2021SHFZ-2-95。
关键词
保健黄酒
顶空固相微萃取
全二维气相色谱-质谱
挥发性组分
主成分分析
Health yellow rice wine
Headspace solid phase microextraction
Two-dimensional gas chromatography-mass spectrometry(GC×GC-MS)
Volatile components
principal component analysis
