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| Spectral pattern recognition of vegetable oils based on data fusion strategy |
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| DOI： |
| KeyWord:vegetable oil spectral data fusion feature extraction recognition |
| FundProject:湖南省自然科学基金面上项目（2023JJ30221） |
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| Download times: 1733 |
| Abstract: |
| In order to achieve the rapid and non-destructive recognition of different vegetable oils, and explore the feasibility and application value of data fusion technology in improving the classification accuracy of spectral models, a total of 180 vegetable oil samples from 7 kinds were recognized and classified by attenuated total reflectance-Fourier transform infrared spectroscopy, surface-enhanced Raman spectroscopy and multi-source data fusion. The differences of Bayes discriminant analysis （BDA）and multilayer perceptron neural network（MLP） in classifying all samples were compared and discussed based on single model, data layer fusion model and feature layer fusion model. Besides, the differences of principal component analysis, generalized least square, maximum likelihood and principal axis factorization in feature extraction were investigated. The results showed that the spectral data fusion had significant advantages in recognizing vegetable oils. The ability of BDA model to distinguish each sample was more predominant than that of MLP. Compared with another three algorithms, principal component analysis showed the more positive results in extracting features. The BDA model based on feature layer fusion from PCA feature extraction was considered as the optimal model,and it could achieve 100% accurate differentiation of 180 samples and 100% accurate differentiation of 5 brands of peanut oil, realizing the two-level recognition and classification of samples from kind to brand. |
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