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| Comparative analysis of qualitative identification models for oil-tea camellia seed oil adulteration based on characteristic fatty acid and triglyceride |
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| DOI： |
| KeyWord:oil-tea camellia seed oil decision tree model multilayer perceptron artificial neural network model qualitative identification fatty acid triglyceride |
| FundProject: |
| Author Name | Affiliation | | SUN Tingting1,2, LIU Jianbo3, REN Jiali1,2, ZHONG Haiyan1,2, ZHOU Bo1,2 | 1.Hunan Key Laboratory of Forestry Edible Sources Safety and Processing, Changsha 410004, China
2.School of Food Science and Engineering, Central South University of Forestry and Technology,Changsha 410004, China
3.Food and Drug Inspection Institute of Yueyang City Inspection and Testing Center, Yueyang 414000, Hunan, China |
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| Abstract: |
| In order to solve the qualitative identification problem of adulterated oil-tea camellia seed oil with other vegetable oils, soybean oil, peanut oil, sunflower seed oil, cottonseed oil, grape seed oil, rapeseed oil, palm oil and rice bran oil were mixed into oil-tea camellia seed oil respectively, two different adulteration gradients of high and low were set up, and based on characteristic fatty acid and triglyceride indicators, the effects of the binary decision tree model, multi-classification decision tree model and multilayer perceptron artificial neural network (MLP-ANN) model for qualitative identification of adulterated oil-tea camellia seed oil were compared and analysed using Python language. The results showed that the accuracy of the binary decision tree model for qualitative identification of oil-tea camellia seed oil adulterated with other vegetable oils under high and low adulteration gradients was above 0.95. The accuracy and precision of the multi-classification decision tree model reached 0.95 at high adulteration gradient, but only 0.90 at low adulteration gradient. Under high and low adulteration gradients, the average precision of MLP-ANN model for qualitative identification of adulterated oil-tea camellia seed oil reached 0.98, and the accuracy reached 0.97 and 0.98 respectively. Compared with the decision tree model, the MLP-ANN model can well realize the qualitative identification of adulterated oil-tea camellia seed oil. |
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