芳香
电子鼻
化学
指纹(计算)
色谱法
固相微萃取
质谱法
气相色谱-质谱法
食品科学
人工智能
计算机科学
作者
Xi He,Huang Yangming,Elżbieta Górska-Horczyczak,Agnieszka Wierzbicka,Henryk H. Jeleń
出处
期刊:Food Chemistry
[Elsevier]
日期:2021-02-01
卷期号:337: 128002-128002
被引量:39
标识
DOI:10.1016/j.foodchem.2020.128002
摘要
Solid-phase microextraction – mass spectrometry (SPME-MS) and fast gas chromatography based electronic nose (GC-E-Nose) were used and compared for their suitability to distinguish Baijiu of various aroma types and geographical origin. Baijiu is a traditional Chinese distilled spirit produced with complex consortia of microorganisms, which results in very complex aroma compounds profiles. A total of 65 Baijiu samples representing 6 aromas were investigated. Strong aroma types from 3 regions were examined for their origin. Data acquired on two analytical systems were processed using uniform statistical approach. Data were pre-processed for multi-classification (OPLS-DA) models as well as for binary classification (PLS-DA) ones. Aroma and regional classification performed using OPLS-DA indicated that the approach based on SPME-MS had better fitness and prediction ability compared with GC-E-Nose. The total correct classification rate for SPME-MS was 94.44% for aroma and 100% for region, whereas for GC-E-Nose these values were 91.53% and 93.94% respectively.
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