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AI Driven Optical Imaging and Spectroscopic Techniques
Summary
Researchers reviewed how AI-enhanced optical imaging and spectroscopic methods — including Raman and FTIR combined with machine learning — enable automated, high-throughput classification of microplastics in complex environmental samples, supporting real-time pollution hotspot mapping and predictive modeling for regulatory and public health applications.
The growing prevalence of microplastics in aquatic and terrestrial ecosystems represents a critical environmental and public health issue, as these minute plastic fragments infiltrate food chains, disrupt ecosystems, and pose potential health risks to humans. Addressing this pervasive problem demands advanced tools capable of delivering precise, real-time detection and monitoring. AI-driven optical imaging and spectroscopic techniques have emerged as revolutionary solutions, offering unparalleled accuracy, sensitivity, and scalability in microplastic detection. Optical imaging, enhanced by sophisticated AI algorithms, enables high-resolution visualization and automated classification of microplastic particles, effectively distinguishing them from other debris in complex environmental samples. Spectroscopic approaches, such as Raman and Fourier-transform infrared (FTIR) spectroscopy, provide detailed chemical and structural insights, allowing for precise identification of microplastic composition and origin. By integrating these advanced techniques with machine learning and data analytics, the throughput and reliability of detection processes are significantly enhanced. This chapter explores into the synergy between AI and these cutting-edge technologies, exploring their applications in environmental monitoring, industrial quality control, and public health initiatives. Real-time systems powered by AI facilitate dynamic mapping of pollution hotspots, predictive modeling for mitigation strategies, and proactive policy development. The chapter also critically examines challenges such as scalability, computational resource demands, and ethical considerations associated with data use, providing a comprehensive analysis of the field. Looking forward, innovations in AI algorithms, sensor technology, and regulatory frameworks promise to expand the potential of these intelligent systems. This chapter underscores their transformative role in addressing the global microplastic crisis and fostering sustainable environmental practices.