| 吕壮，黄金，兰梓溶，代婷玉，许宙，陈茂龙，焦叶，文李，程云辉，丁利.机器学习算法在食用植物油掺伪鉴别中应用的
研究进展[J].中国油脂,2025,50(7):.[LYU Zhuang, HUANG Jin, LAN Zirong, DAI Tingyu, XU Zhou,
CHEN Maolong, JIAO Ye, WEN Li, CHENG Yunhui, DING Li.Research progress on application of machine learning algorithms in adulteration identification of edible vegetable oils[J].China Oils and Fats,2025,50(7):.] |
| 机器学习算法在食用植物油掺伪鉴别中应用的
研究进展 |
| Research progress on application of machine learning algorithms in adulteration identification of edible vegetable oils |
| |
| DOI：10.19902/j.cnki.zgyz.1003-7969.240160 |
| 中文关键词: 食用植物油 机器学习算法 掺伪鉴别 |
| 英文关键词:edible vegetable oil machine learning algorithm adulteration identification |
| 基金项目:湖南省研究生创新项目（CX20220949） |
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| Author Name | Affiliation | | LYU Zhuang, HUANG Jin, LAN Zirong, DAI Tingyu, XU Zhou,
CHEN Maolong, JIAO Ye, WEN Li, CHENG Yunhui, DING Li | School of Food Science and Biological Engineering, Changsha University of
Science & Technology, Changsha 410114, China |
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| 摘要点击次数: 113 |
| 全文下载次数: 59 |
| 中文摘要: |
| 市场上存在利用低价食用植物油掺伪或冒充高价食用植物油的现象，机器学习算法可应用于食用植物油的掺伪鉴别中。旨在为食用植物油掺伪鉴别研究中算法选择提供一定的理论依据，简要介绍了机器学习算法的分类及其在食用植物油掺伪鉴别中应用的流程，并对国内外机器学习算法在橄榄油、山茶油及其他植物油掺伪鉴别中的应用进行分析总结，同时探讨了支持向量机、随机森林、逻辑回归、人工神经网络、主成分分析等机器学习算法在食用植物油掺伪鉴别研究中的优缺点。需要综合考虑数据的特点、模型的性能和实际应用的需求选择合适的机器学习算法，以提高食用植物油掺伪鉴别的准确性和鲁棒性，为食用植物油市场的健康发展提供有力的技术支持。 |
| 英文摘要: |
| There exists a phenomenon in the market where low-priced edible vegetable oils are adulterated or counterfeited as high-priced edible vegetable oils. Machine learning algorithms can be applied in the adulteration identification of edible vegetable oils. In order to provide a theoretical basis for the selection of algorithms in the adulteration identification of edible vegetable oils, the classification of machine learning algorithms and their application process in the adulteration identification of edible vegetable oils were briefly introduced. The application of machine learning algorithms in the research on olive oil, oil-tea camellia seed oil, and other vegetable oils adulteration identification both in domestic and international studies were analyzed and summarized, and the advantages and disadvantages of machine learning algorithms such as support vector machines, random forests, logistic regression, artificial neural networks, and principal component analysis in the study of adulteration identification of edible vegetable oils were discussed. Selecting an appropriate algorithm requires a comprehensive consideration of the characteristics of the data, the performance of the model, and the needs of practical applications to improve the accuracy and robustness of adulteration identification of edible vegetable oils, and provide strong technical support for the healthy development of edible vegetable oils market. |
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