Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China
文献类型： 外文期刊
作者： Wu, Xinye 1 ; Fauconnier, Marie-Laure 2 ; Bi, Jinfeng 1 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storag, Minist Agr & Rural Affairs, POB 5109, Beijing 100193, Peoples R China
2.Univ Liege, Gembloux Agro Biotech, Lab Chem Nat Mol, Passage Deportes 2, B-5030 Gembloux, Belgium
关键词： apple; flash GC E-nose; volatile; multivariate analysis; discrimination; decision tree
期刊名称：FOODS （ 影响因子：5.561； 五年影响因子：5.94 ）
ISSN：
年卷期： 2022 年 11 卷 11 期
页码：
收录情况： SCI
摘要： Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory.
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