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Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds

文献类型： 外文期刊

作者： Huang, Jing 1 ; Zhang, Xuenan 1 ; Yang, Hang 3 ; Li, Zhenbiao 1 ; Xue, Zhengfang 1 ; Wang, Qingqing 1 ; Zhang, Xinyuan 3 ; Ding, Shenghua 4 ; Luo, Zisheng 1 ; Xu, Yanqun 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Minist Agr & Rural Affairs, Key Lab Agroprod Postharvest Handling, Hangzhou 310058, Peoples R China

2.Zhejiang Univ, Innovat Ctr Postharvest Agroprod Technol, Hangzhou 310058, Peoples R China

3.Jinan Univ, Sch Intelligent Syst Sci & Engn, Zhuhai 519070, Peoples R China

4.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Peoples R China

关键词： strawberry; maturity; variety; volatile organic compounds; machine learning; neural networks

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2025 年 14 卷 2 期

页码：

收录情况： SCI

摘要： Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from 90.00% to 98.33%, with the NN model demonstrating the best performance. Specifically, it achieved 96.67% accuracy for single-maturity classification, 98.33% for single-variety classification, and 96.67% for dual maturity and variety classification, along with 98.09% precision, 97.92% recall, and 97.91% F1 score. Feature importance analysis indicated that the NN model exhibited the most balanced reliance on various VOCs, contributing to its optimal performance with the broad-spectrum VOC detection method, GC-IMS. Overall, these findings underscore the potential of NN modeling for accurate and efficient fruit classification based on integrated VOC profiles.

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