Identification of marine microplastics by laser-induced fluorescence spectroscopy: 1-Dimensional convolutional neural network and continuous convolutional model

Marine microplastic pollution is a serious threat to ecosystems and human health, and its identification is of great significance for determining the source and extent of pollution. Conventional methods such as Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy are effective but they are time-consuming and their equipment is expensive. Laser induced fluorescence can reflect the molecular structure through the fluorescence characteristics of aromatic groups and hydrocarbon chains. This method has the characteristics of non-destructive, rapid and efficient, which can be used for the identification of microplastics. This study investigated 2400 LIF spectra of six types of marine microplastics excited by a 405 nm laser. A 1-dimensional convolutional neural network (1D-CNN) and an optimized continuous convolution (Cont-conv) model were used for classification. The accuracy of 1D-CNN is 97.5 %, demonstrating good performance, while the accuracy of the Cont-conv model can reach up to 99.5 %. The results show that the Cont-conv model effectively enhances the model's ability to extract features through continuous convolution operations and achieves faster convergence. CNN models trained on commercial microplastic samples were applied to the identification of field-collected marine microplastics, and also achieved good results. This study presents an innovative and efficient automated classification method for the detection of marine MPs, which offers the potential for integration with portable devices.