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ELM combined with differential Raman spectroscopy for the detection of microplastics in organisms
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Researchers developed a rapid detection method for microplastics in marine organisms by combining differential Raman spectroscopy with machine learning algorithms. The approach addressed common problems like fluorescence interference and high false detection rates that plague conventional microplastic identification methods. The study demonstrates that this combined technique can accurately identify microplastic particles in biological tissue samples with significantly improved efficiency.
Aiming at the problems of low extraction efficiency, high false detection rate, weak Raman signal and serious interference by fluorescence signal in the detection of microplastics in marine organisms, this paper establishes a set of rapid detection methods for microplastics in organisms, including confocal Raman spectroscopy, fluorescence imaging, differential Raman spectroscopy, and rapid identification of microplastics based on the ELM modeling assistance. Firstly, to address the problem of low extraction efficiency of microplastics, we explored and optimized the digestion method of tissues, which effectively improved the digestion effect of fish tissues and excluded the influence of tissues on microplastics detection. Aiming at the problems of high misdetection rate and low pre-screening efficiency of microplastics, fluorescence imaging technology is adopted to realize the visualization and detection of microplastics, which effectively improves the detection efficiency and precision of microplastics. Based on the confocal microscopy Raman spectroscopy detection system built independently in the laboratory, using 784/785 nm as the excitation light, the differential Raman spectroscopy technique effectively excludes the interference of fluorescence signals in the Raman spectra, and improves the signal-to-noise ratio of the Raman spectra, and the recovery rate of the Raman characteristic peaks in the differential Raman spectroscopy reaches 100 % compared to the traditional baseline correction method, which is 33.3 % higher than that of the baseline correction method. Finally, a microplastic identification model is constructed based on ELM to assist in realizing the rapid and accurate identification of microplastics. The more complete detection method of microplastics in marine organisms proposed in this paper can realize the rapid and nondestructive, efficient and accurate detection of microplastics in fish, which can help to further promote the development of marine microplastics monitoring technology.
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