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| Comparative study on the identification of trace vegetable oils based on gas chromatography combined with chemometrics |
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| DOI： |
| KeyWord:vegetable oil trace fatty acid gas chromatography chemometrics |
| FundProject:中国人民公安大学刑事科学技术双一流创新研究专项（2023SYL06） |
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| Abstract: |
| In order to accurately identify trace oil evidence in the field of forensic science, and provide technical support for relevant cases involving the identification of trace amounts of vegetable oil evidence, eight trace vegetable oils (flaxseed oil, oil-tea camellia seed oil, rapeseed oil, corn oil, peanut oil, sesame seed oil, soybean oil, and sunflower seed oil) left on different carriers and stored at 4, 25, 38 ℃ for 1, 3, 7, 14, 30, 45 d, and 60 d respectively were used as research object. The fatty acid composition was determined by gas chromatography, and five main fatty acids (hexadecanolic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid) from eight vegetable oils were selected as identification indicators to construct three vegetable oil recognition models (Fisher discriminant analysis, convolutional neural network, and random forest) using chemometrics methods. The results showed that Fisher discriminant analysis, convolutional neural network, and random forest models could all achieve accurate recognition of eight vegetable oils, among which the random forest model could evaluate the importance of each fatty acid to the classification results, and the recognition accuracy was the highest, reaching 98.2%. In conclusion, the random forest model has simple parameter settings and high recognition accuracy, and can effectively solve the problem of difficult identification types of trace vegetable oils. |
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