Detection of antioxidants in edible oil by two-dimensional correlation spectroscopy combined with convolutional neural network

Abstract

In this study, the qualitative identification and quantitative prediction of antioxidants in edible oil were achieved by the two-dimensional correlation spectroscopy combined with convolutional neural network (CNN). The CNN model obtained classification accuracy of 97.56%, which is significantly higher than that of the partial least squares discriminant analysis (PLS-DA) model. Comparing the performance of the established partial least squares (PLS), CNN, and CNN combined with PLS (CNN_PLS) regression models, it is found that the CNN model has the best quantitative performance among the three regression models, with a root mean square errors for prediction (RMSEP) of 0.0232 and a coefficient of determination for prediction (R2p) of 0.9703. At the same time, it is found that the prediction ability of the CNN_PLS model is affected by the feature extraction ability of CNN models and the output dimension of convolution layers in CNN models. The results demonstrate the potential of the proposed method for the detection of antioxidant in edible oils.