| 孙婷婷1,2，刘剑波3，沈银梅3，等.基于机器学习算法的食用植物油掺伪鉴别的研究进展[J].中国油脂,2021,46(3):103~108.[SUN Tingting1,2, LIU Jianbo3, SHEN Yinmei3, etc.Progress on adulteration identification of edible vegetable oilsbased on machine learning algorithms[J].China Oils and Fats,2021,46(3):103~108.] |
| 基于机器学习算法的食用植物油掺伪鉴别的研究进展 |
| Progress on adulteration identification of edible vegetable oilsbased on machine learning algorithms |
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| DOI： |
| 中文关键词: 食用植物油 掺伪鉴别 机器学习算法 |
| 英文关键词:edible vegetable oil adulteration identification machine learning algorithms |
| 基金项目:湖南省市场监督管理局科技计划项目（2020KJJ H55）；湖南省科技重大专项（2018NK1030）；湖南省教育厅科学研究重点项目（18A154）；湖南省自然科学青年基金（2019JJ51003）；湖南省科技创新平台与人才计划项目（2019TP1029） |
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| 中文摘要: |
| 市场上存在用低值低价油脂掺伪高值高价食用植物油的现象，这不仅损害食用植物油生产者和消费者利益，也不利于我国食用油脂产业的健康发展。许多学者将机器学习算法应用到食用植物油掺伪鉴别的研究中，取得了显著的研究成果。为了对食用植物油掺伪鉴别的研究和应用提供一定的理论依据和方法参考，总结了国内外现阶段使用机器学习算法进行食用植物油掺伪鉴别的研究进展，这些机器学习算法包括主成分分析、判别分析、支持向量机、随机森林、人工神经网络等。对所述机器学习算法应用于食用植物油掺伪鉴别研究的优缺点进行了分析，在实际应用中应结合实际情况，综合考量选择合适的算法。 |
| 英文摘要: |
| The phenomenon of adulteration of high value and high price edible vegetable oils with low value and low price oils exists in the market, which not only damages the interests of edible vegetable oil producers and consumers, but also is harmful to the healthy development of the edible oil industry in China. Many scholars have applied machine learning algorithms in the adulteration identification of edible vegetable oils and achieved significant research results. In order to provide a theoretical basis and methodological reference for the research and application of adulteration identification of edible vegetable oil, the research progress on the application of machine learning algorithms (principal component analysis, discrimination analysis, support vector machine, random forest, artificial neural network, etc.) to identify the edible vegetable oil adulteration was summarized. The advantages and disadvantages of the machine learning algorithms used in the study of adulteration identification of edible vegetable oil were analyzed, and the appropriate algorithm should be selected based on the actual situation in practical application. |
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