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AI Driven Optical Imaging and Spectroscopic Techniques
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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.
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Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
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This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
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Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy
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Researchers developed a machine learning system to identify microplastics in environmental samples using Raman spectroscopy — a technique that identifies materials by how they scatter light — training it on over 64,000 spectra and achieving recall above 99% and precision above 97%. Combining the AI with human review reduced analysis time from several hours to under one hour per sample, making microplastic monitoring far more practical at scale.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
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Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
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Researchers developed a machine learning approach combined with micro-FTIR imaging to automate the analysis of microplastics in environmental samples, reducing the need for expert spectral interpretation. The system can process large datasets more efficiently than current spectral library matching methods, enabling scalable microplastic monitoring.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.