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| Rapid identification of Gannan oil-tea camellia seed oil adulteration based on near infrared spectroscopy |
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| DOI： |
| KeyWord:Gannan oil-tea camellia seed oil adulteration identification linear discriminant analysis (LDA) artificial neural network (ANN) |
| FundProject: |
| Author Name | Affiliation | | SHEN Lecheng, ZENG Xiuying, WEN Zhigang, ZHANG Yuancong, LIU Xianbiao, WANG Mei， LIU Ting, FAN Weihua, ZOU Hui | The State Centre of Quality Supervision and Inspection for Camellia Products/ Ganzhou Institute of Product Quality Supervision and Inspection, Ganzhou 341000, Jiangxi, China |
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| Abstract: |
| In order to explore the feasibility of rapid and non-destructive identification of adulterated oil-tea camellia seed oil based on near infrared spectroscopy, Gannan oil-tea camellia seed oil was selected as the research object, and the adulterated oil-tea camellia seed oil was prepared by blending different vegetable oils such as corn oil, peanut oil, rapeseed oil, sunflower seed oil and soybean oil. The spectral characteristics of the adulterated oil-tea camellia seed oil were collected by near infrared spectroscopy, and different pretreatment methods and main components were compared and determined. The identification model of oil-tea camellia seed oil adulteration was established by combining linear and nonlinear modeling methods. The identification accuracy(the percentage of pure and aduterated oil-tea camellia seed oil samples correctly identified, sensitivity (the percentage of pure oil-tea camellia seed oil samples correctly identified as pure oil-tea camellia seed oil) and specificity (the percentage of adulterated oil-tea camellia seed oil samples correctly identified as adulterated camellia seed oil) were used as the evaluation indexes of the model to select the best model. The results showed that the second derivative-linear discriminant analysis (SD-LDA) model was the optimal linear model, and the standard normal variable transformation-artificial neural network (SNV-ANN) model was the optimal nonlinear model, their identification accuracy, sensitivity and specificity were 97.58%, 100%, 97.33% and 98.99%, 100%, 98.88% respectively. The identification effect of SNV-ANN model was better than that of SD-LDA, which indicated that the nonlinear model was more suitable for the identification of oil-tea camellia seed oil adulteration, and the model could more accurately identify whether the oil-tea camellia seed oil was adulterated. |
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