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| Classification and recognition of edible vegetable oils based on olfactory visualization technology |
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| DOI： |
| KeyWord:olfactory visualization edible vegetable oil classification and recognition Support Vector Machine |
| FundProject:湖南省科技计划重点研发项目（2022NK2048）；湖南省教育厅科学项目（20A515）；湖南省自然科学基金（2020JJ4142）；湖南省林业杰青培养科研项目（XLK202108-7） |
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| Abstract: |
| In order to distinguish oil-tea camellia seed oil from three common edible vegetable oils (rapeseed oil, soybean oil and corn oil), visual sensor array was prepared, and four different edible vegetable oils were classified and identified by olfactory visualization technology. Principal component analysis (PCA) was used to reduce the dimension of the characteristic data of the four oil samples. The data after PCA dimensionality reduction was imported into three classification models namely K-Nearest Neighbor (KNN), Extreme Learning Machine (ELM), and Support Vector Machine (SVM), and the model parameters were optimized, and the classification results of the three classification models were compared. The results showed that the established SVM classification model had the best performance. When the number of input principal component vectors was 7, c=1.741 1, and g=4.549 8, the classification and recognition accuracy of the test set of the SVM classification model was 95.8%, and the 5-fold validation accuracy was 89.6%. The visual sensor array can achieve the classification and recognition of four edible vegetable oils, and the olfactory visualization technology is feasible for the classification and identification of edible vegetable oils. |
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