Mixed Contaminants Identification Based on 3D Fluorescent Spectroscopy With TTE Model

In this letter, a method of mixed contaminants identification based on 3D fluorescent spectroscopy with a novel generative model is proposed. To comprehensively characterize the 3D fluorescent spectra of contaminants, local and global features can be respectively extracted to represent the fluorescent peaks of substances and the overall trend of 3D fluorescent spectroscopy. Local features involving substance properties can be beneficial for improving authenticity, whereas global features can increase diversity as auxiliary information. For that, a Text-to-EEM (TTE) generative model is presented with convolution and retentive self-attention mechanisms to capture local and global features, respectively. In addition, spectral data can be generated from textual descriptions using text generation modules. Experiments using CNN identification network show that the maximum accuracy of samples generated by the TTE model was 67% for all correct and 20% for partially correct, better than GAN, WGAN, and WAE.