ArticleFoods (Basel, Switzerland)2022
Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Geographical origin traceability of kiwifruit products using stable isotope and multi-element analysis with multivariate modeling: Feature extraction, selection of model and variable, and discrimination.Food chemistry: X · 2025Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Enhancing Phytochemical Compounds, Functional Properties, and Volatile Flavor Profiles of Pomelo (Foods (Basel, Switzerland) · 2023Article
- Factors Affecting Purchase Intention of Hanfu: Considering Product Identification, Cultural Motivation, and Perceived Authenticity.Behavioral sciences (Basel, Switzerland) · 2023Article
- Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples.Entropy (Basel, Switzerland) · 2023Article
- Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review.Foods (Basel, Switzerland) · 2023Review
- Unmasking the 'Asymptomatic' COVID-19: A Nose Question.Life (Basel, Switzerland) · 2022Article
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3 authors.
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Abstract
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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