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固相微萃取-气相色谱/质谱结合化学计量学方法分析四种茶油的挥发性成分 被引量:8

Analysis of Volatile Components from Four Types of Camellia Oils by SPME-GC/MS Coupled with Chemometric Methods
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摘要 采用固相微萃取-气相色谱/质谱(SPME-GC/MS)联用技术对4种茶油的挥发性成分进行测定,共鉴别出81种挥发性成分,其中精制茶油含31种共有成分,粗制茶油含43种共有成分,外用茶油含33种共有成分,过期茶油含50种共有成分。4种茶油中醛类挥发性成分相对含量较高,是构成茶油风味的主要挥发性成分。利用主成分分析、聚类分析两种化学计量学方法分析评价了4种茶油挥发性成分的差异,前6种主成分累积贡献率达到79.6%,且4种茶油得到了较好的聚类。 Volatile components of 4 types camellia oils were detected by solid phase microextraction-gas chromatography-mass spectrometry(SPME-GC/MS).81 kinds of volatile components were extracted from refined camellia oils,crude camellia oils,external using camellia oils and expired camellia oils.Among the common aroma components,31 kinds of common components were found in refined camellia oils,43 kinds of common components were found in crude camellia oils,33 kinds of common components were found in external using camellia oils,and 50 kinds of common components were found in expired camellia oils.The relative content of aldehyde components were the highest in 4 types of camellia oils,which was the main volatile components of camellia oil.There was a great difference in volatile components of 4 types of camellia oils,and principal components analysis(PCA)and clustering analysis were used to analyze and evaluate the differences of volatile components in 4 kinds of camellia oils.The PCA results showed that 6 principal components could reflect most details on camellia oils with a cumulative contribution rate of 79.6%,and the 4 different kinds of camellia oils got agood clustering effect.Accordingly,SPME-GC/MS combined with chemometrics provides a method for quality identification and control for camellia oils.
作者 钟培培 喻晴 王远兴 ZHONG Pei-pei;YU Qing;WANG Yuan-xing(State Key Laboratory of Food Science and Technology,Nanchang University,Nanchang 33004)
出处 《分析科学学报》 CAS CSCD 北大核心 2018年第5期589-595,共7页 Journal of Analytical Science
基金 国家自然科学基金(No.31560478) 江西省重点研发计划(No.20161BFF60095)
关键词 茶油 固相微萃取-气相色谱/质谱联用 挥发性成分 主成分分析 聚类分析 Camellia oil SPME-GC/MS Volatile compounds Principal components analysis Clustering analysis
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