本研究以罗布斯塔生咖啡豆为研究对象，采用顶空固相微萃取-气相色谱质谱联用和电子鼻技术对生咖啡豆贮藏过程中挥发性成分进行分析。结果表明，顶空固相微萃取-气相色谱质谱联用技术在生咖啡豆中共鉴定出56种挥发性物质，随着贮藏时间的延长，酸类物质含量先增大后减少，酯类物质含量逐渐增大，醛类物质含量先减小后增大，贮藏前到60 ℃贮藏结束后醛类化合物由13.72%增加至25.46%；加速贮藏温度越高，挥发性物质含量增加速率越快，总挥发性物质含量由24.12 μg/g DW增加至31.34 μg/g DW。电子鼻数据分析能较好的区分不同贮藏期的样品，不同阶段样品各自聚为一类，且在60 ℃时样品聚集更为紧密；通过挥发性化合物含量变化、电子鼻传感器特征响应值和主成分分析，初步推测生咖啡豆加速贮藏期间风味品质劣变的特征性化合物为己醛、苯甲醛、苯乙醛、乙酸、己酸及苯甲酸。因此顶空固相微萃取-气质联用、电子鼻技术结合主成分分析可综合评价生咖啡豆贮藏期的风味特征。

In this study, Robusta green coffee beans were used as the research object, and head space-solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC/MS) and electronic nose technology were used to analyse the volatile compounds of the coffee beans during an accelerated storage. Results showed that a total of 56 volatile compounds were identified in the green coffee bean samples. With the extension of storage time, the content of acids increased first and then decreased, the content of the ester compounds increased gradually, whilst the content of aldehydes decreased first and then increased (increased from 13.72% before storage to 25.46% after the storage at 60 ℃). The higher the accelerated storage temperature, the faster the increasing rate of volatiles (the volatile content increased from 24.12 μg/g DW to 31.34 μg/g DW). Electronic nose data analysis could discriminate samples subjected to different storage periods. The samples derived from different storage stages were clustered into different classes respectively, with a more compact cluster achieved at 60 ℃. Through the analyses of the changes in the contents of volatile compounds and the characteristic response values of electronic nose sensors, as well as the principal component analysis (PCA), it can be preliminarily speculated that hexanal, benzaldehyde, phenylacetaldehyde, acetic acid, hexanoic acid, and benzoic acid were the characteristic compounds that contributed greatly to flavor deterioration of green coffee beans during the accelerated storage. Thus, HS-SPME/GC-MS, electronic nose technology combined with PCA can evaluate comprehensively the flavor characteristics of green coffee beans during storage.

国家自然科学基金面上项目（31872888）；海南省自然科学基金创新研究团队项目（2019CXTD416）；中央级公益性科研院所基本科研业务费创新团队项目（1630142017005）