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| Identifying types of edible vegetable oil by molecular spectroscopic technology combined with deep learning model |
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| DOI： |
| KeyWord:edible vegetable oil molecular spectroscopy deep learning type recognition |
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| Abstract: |
| To achieve rapid and non-destructive identification of edible vegetable oil, attenuated total reflection-Fourier transform infrared spectroscopy was used to obtain 340 spectral data of 10 edible vegetable oil samples. After preprocessing, the noise and background interference in the spectral data were eliminated. Three principal components were extracted by principal component analysis, and base on which, the KNN model and the BP neural network model optimized based on the SSA algorithm were constructed for identification and their effects were compared. The results showed that the recognition rate of the KNN model could reach 97.7%. The BP neural network model optimized based on the SSA algorithm, with a recognition rate of 100%, had the best classification effect, while the recognition rate of traditional BP neural network model was only 87.6%. In summary, a new method for identifying edible vegetable oil types using molecular spectroscopy technology combined with deep learning models can realize the accurate identification of edible vegetable oil types. |
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