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

• Published in: Journal of food composition and analysis 2023-06, Vol.119, p.105262, Article 105262
• Main Authors: Wu, Xijun, Xu, Baoran, Niu, Yudong, Gao, Shibo, Zhao, Zhilei, Ma, Renqi, Liu, Hailong, Zhang, Yungang
• Format: Article
• Language: English
• Subjects: Antioxidant; CNN; Edible oil; Qualitative identification; Quantitative analysis; Two-dimensional correlation spectrum
• ISSN: 0889-1575; 1096-0481
• DOI: 10.1016/j.jfca.2023.105262

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. [Display omitted] •A two-dimensional correlation spectral data set is established.•Two dimensional correlation spectroscopy and CNN is used to identify antioxidants.•CNN combined with PLS is used for quantitative analysis of antioxidants.•The CNN model can achieve good results in the quantitative analysis of antioxidants.