Visual Inspection of Food Packaging Paper by X-Ray Fluorescence Spectroscopy Combined with Deep Learning Algorithm

To quickly classify and identify common food packaging paper at the scene of the case, a visual inspection method of food packaging paper based on X-ray fluorescence spectroscopy (XRF) and deep learning algorithm is proposed. First, the inorganic elements in 44 samples of food packaging paper from different sources were detected via XRF, and artificial classification and cluster analysis were performed based on the content of the main constituent elements. Second, to test the clustering effect and visualize data classification, two-dimensionality reduction algorithms, principal component analysis, and t-distribution random neighborhood embedding are used. Finally, 80% of the samples are randomly selected as the training set to construct the artificial neural network, and relevant experiments are carried out. The experimental results show that classification accuracy of the proposed method on the test set is 88.9%, which can be used as a reference for future practical applications of public security business.