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Challenges and prospects in the identification of micro and nano plastics using Raman spectroscopy: a comprehensive review
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This comprehensive review evaluates Raman spectroscopy—including handheld field devices—as a method for detecting and identifying microplastics and nanoplastics in environmental and biological samples, highlighting how AI and machine learning integration is improving detection speed and accuracy. Reliable analytical methods for characterizing these particles across diverse matrices are foundational to advancing understanding of human exposure and ecological risk from plastic pollution.
Raman spectroscopy (RS) is a widely employed technique for analyzing emerging environmental pollutants, microplastics and nanoplastics (MNPs), detection of biomolecules, identification of cells and pathogens. Detecting, identifying, and quantifying these particles in environmental samples and living organisms poses significant challenges due to their minute size, irregular shapes, diverse polymer compositions, surface coatings, and large surface areas that readily attract chemical and microbial contaminants. Raman Spectroscopy is a reliable, specific, fast, more sensitive method for the characterization of small sized particles. Moreover, the handheld Raman device is easily deployable in the field. This review addresses the key analytical strengths and the challenges that limit precise characterization of MNPs and provide recommendations to improve data reliability, that further include strategies to mitigate common quality control issues, particularly the challenge of distinguishing between plastic particles present in the sample and those introduced through contamination during sampling, processing, or analysis. Recently, the use of artificial intelligence (AI) and machine learning has been incorporated with Raman spectroscopy to facilitate the detection of MNPs and provide automation.
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