Identification of antioxidants in edible oil by two-dimensional correlation spectroscopy combined with deep learning

The quality of edible oil is an essential part of food safety which is highly concerned by people. In this study, perturbation Raman spectroscopy combined with deep learning was used to identify antioxidants in edible oils. Convolutional neural network (CNN) and recurrent neural network (RNN) are two classical network structures in deep learning. First of all, we explored the identification effect of antioxidants in edible oils using one-dimensional Raman data combined with one-dimensional CNN and RNN. At the same time, we also compared the identification effect of the data set under a single heating time disturbance. Then two-dimensional correlation spectroscopy combined with a two-dimensional CNN model was used to identify the types of antioxidants. It was found that the final classification accuracy reached 97%, which was nearly 10% higher than the one-dimensional CNN model. This showed that the two-dimensional correlation spectral analysis based on external disturbance can “amplify” the subtle differences in the spectral data. In addition, the traditional chemometric method, partial least squares discriminant analysis (PLS-DA), was used as a control experiment. According to this study, it can be seen that the perturbation spectrum combined with deep learning was feasible in the detection of trace substances in edible oils. •Two dimensional correlation spectra can improve the spectral resolution.•The disturbance of single heating time can't increase the classification effect.•Disturbance spectrum combined with CNN can improve the classification effect.