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| Progress on adulteration identification of edible vegetable oilsbased on machine learning algorithms |
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| DOI： |
| KeyWord:edible vegetable oil adulteration identification machine learning algorithms |
| FundProject: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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| Abstract: |
| 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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