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| Establishment of rapid detection model of fatty acid composition of oil-tea camellia seed oil by infrared spectroscopy |
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| DOI： |
| KeyWord:oil-tea camellia seed oil fatty acid composition infrared spectroscopy support vector machine artificial neural network model |
| FundProject:广东省林业科技计划项目(2019-02)；广东省林业科技创新项目(2017KJCX005) |
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| Abstract: |
| The fatty acid composition and infrared spectra of 86 kinds of oil-tea camellia seed oil samples were determined by gas chromatography and Fourier transform infrared spectrometer, and the nonlinear modeling methods of support vector machine (SVM) and BP artificial neural network (ANN) were used to construct the quantitative regression model of main fatty acids in oil-tea camellia seed oil. The results showed that the quantitative regression models of oleic acid and palmitic acid established by ANN were more accurate than those by SVM, the correlation coefficients(R) of the correction set and the prediction set were 0.998 7, 0.945 1 and 0.955 7, 0.926 2, respectively, and the relative standard deviations were less than 1% and 5%, respectively. The accuracies of linoleic acid quantitative analysis models established by SVM and ANN were both very high, and the relative standard deviation was both less than 1%. It showed that infrared spectroscopy was feasible for the rapid detection of main fatty acids in oil-tea camellia seed oil. |
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