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| Fast identification of vegetable oils by infrared spectroscopy combined with neural network |
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| DOI： |
| KeyWord:infrared spectroscopy vegetable oil feature extraction machine learning neural network |
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| Abstract: |
| In order to realize the fast identification of vegetable oil, attenuated total reflection-Fourier transform infrared spectroscopy technology and depth learning algorithm were used to carry out spectral pattern recognition of vegetable oil. The spectral data of 8 kinds of vegetable oil samples were obtained in the experiment. The standard normal variate and the first derivative preprocessing method were used to eliminate the background interference. At the same time, competitive adaptive reweighted sampling model was constructed to extract the features spectral data of each sample. Long short-term memory neural network(LSTM) and BP neural network improved by Levenberg-Marquardt algorithm were established to predict and compare the types of vegetable oil after extracting feature wavelength respectively, and the latter was used for identification of actual samples. The results showed that the recognition accuracy of the LSTM model could be effectively improved by extracting the feature wavelengths, and the best recognition rate increased from 30%-40% before extracting the feature wavelengths to 80%-90%, and the model running time was shortened from 111 min 25 s before extracting the feature wavelengths to 1 min 45 s. Compared with the LSTM model, BP neural network improved by Levenberg-Marquardt algorithm had a higher classification recognition accuracy of 99.852%, and the recognition accuracy was 100% when used for the recognition of actural samples. The experimental results can provide some reference for nondestructive rapid testing of vegetable oils. |
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