From 1-D to 3-D: LIBS Pseudohyperspectral Data Cube Deep Learning Mechanism Used in Nuclear Metal Materials Classification

In this paper, we propose a new spectral data mechanism called LIBS pseudohyperspectral data cube. This mechanism allows for the utilization of multidimensional information from laser-induced plasma, transforming 1-D LIBS spectra into 3-D data cube. Specifically, two additional dimensions are introduced to capture spectral variations information, allowing more features to be learned during pretraining. Proposed mechanism can make the LIBS system more robust when handling unstable spectra acquired onsite, and can also allow LIBS take full advantage of deep learning algorithms. In the context of nuclear power plants, traditional LIBS classification faces significant challenges due to unstable spectra, which reduce the accuracy of classifying similar or tiny extreme condition materials. By combining deep learning algorithms with LIBS pseudohyperspectral data cube, we can capture spectral and other dimensional features to enhance classification accuracy. Experimental results show that, compared to traditional 1-D data processing, the new method significantly improves the classification accuracy of unstable spectra. Moreover, by incorporating an attention mechanism, the model can adaptively adjust the weights of different features, further improving classification accuracy to over 99%. Visualizing the attention mechanism's weight matrix allows us to identify the importance of different features in classification. Additionally, t-SNE visualizations demonstrate the clustering of different categories in the feature space, further validating the performance of the new method. We believe this data cube mechanism offers an effective new approach for applying deep learning algorithms and enhancing data dimensionality in the LIBS field.