Multi-perspective interpretation for one-dimensional conventional neural network model to identify iron-bearing waste material

Identification of iron-bearing waste material (IWM) is challenging, as the physical and chemical properties of IWMs vary greatly with sources and lacking of common markers. Here, we proposed an interpretable method based on a one-dimensional convolutional neural network (1D CNN) and laser-induced breakdown spectroscopy (LIBS) that can recognize IWM. The proposed 1D CNN model obtained a test accuracy of 99.08% ± 1.37% with five-time repetition and showed sensitivities and specificities per class within the range of 98-99%. To shed light into the black-box nature of neural network, a multi-perspective interpretation of CNN algorithm towards the analysis of spectroscopy was performed to provide a more complete view of feature importance, intermediate CNN outputs and decision-making process. As for identifying IWM, the most critical elements captured by the CNN using a visualization algorithm (class activation map) were Na, Mn, Al, Ca, Fe, Mg, K, Cr, Zn, F and Cl. In the intermediate CNN outputs, it was found that the kernels in the same convolution extract different local informative variables and contribute to learning high-level features. In the final CNN decision process, the CNN was proved to accumulate more valuable information over the increased convolutions using another visualization algorithm (t-SNE) and the output was calculated through mathematical inference. This is the first study in which CNN-assisted LIBS spectroscopy was used to identify IWM while providing a multi-perspective interpretable method for CNN algorithm, which can lay a foundation for widening the application of CNN-based spectroscopic techniques in waste identification.