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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.
The accumulation of microplastics (MPs) resulting from disposal of plastic waste into water sources, poses a significant threat to aquatic organisms. These are readily ingested by organisms, leading to the accumulation of harmful substances, disrupting their biological processes. Current methods for identifying microplastics have notable drawbacks, including low resolution, extended imaging time, and restricted particle size analysis. Integrating Raman spectroscopy with machine learning (ML) proves to be an effective approach for identifying and classifying MPs, especially in scenarios where they are found in environmental media or mixed with various types. Machine learning (ML) can be vital tool in assisting Raman analysis, owing to its robust feature extraction capabilities. This comprehensive review outlined the utilization of various machine learning techniques in conjunction with Raman spectral features for diverse investigations related to microplastics. The methodologies discussed encompass Principal Component Analysis, K-Nearest Neighbour, Random Forest, Support Vector Machine, and various deep learning algorithms.
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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.
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Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
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Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
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