We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Leveraging machine learning for microplastic detection, global distribution analysis, and management
AI summary Read the abstract
This review examines how machine learning and AI are being combined with analytical techniques like Raman spectroscopy and FTIR to improve microplastic detection, characterization, and monitoring at global scale. The authors identify India, China, and the USA as major contributing countries to global microplastic pollution and highlight specific hotspots. The study suggests that AI-driven approaches could make microplastic monitoring more efficient, accurate, and scalable compared to traditional methods.
The extensive occurrence of microplastics (MPs) in both aquatic and terrestrial environments requires enhanced detection, characterization, monitoring, and management approaches. Researchers have made significant progress in making MPs detection more efficient, accurate, and scalable by combining artificial intelligence (AI) with analytical techniques like Raman spectroscopy (RS), Fourier transform infrared spectroscopy (FTIR), image processing (IP), and hyperspectral imaging (HSI). India, China, and the USA have been recognized as major contributing countries in terms of global MPs pollution. Netravathi River in India has the maximum MP pollution of 288 pieces/m, 96 pieces/kg, and 84.45 pieces/kg, respectively in water, sediment and soil. In China, the Inland freshwater lakes of Wuhan have the maximum MPs pollution of 1660.0 ± 639.1-8925 ± 1591 n/m in water. In the USA, San Francisco Bay, California, has the maximum MPs pollution of 15,000-2,000,000 particles/km. Furthermore, the application of Machine Learning (ML) algorithms incorporated FTIR, Raman, and HSI have provided better efficacy (99 %, 99.1 %, and 97 % respectively) in detection and characterization of MPs. This study emphasizes the need to understand the foundational concepts, data resources, preprocessing methods, and limitations of the ML algorithms employed in the identification, detection, distribution, and management of MPs. Also, novel prospects for research and development on combining ML technologies were explored. Overall, AI and environmental science can revolutionize MPs research by providing powerful tools for real-time monitoring and mitigation, preserving ecosystem health.
More Papers Like This
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
AI summary Read the abstract
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
AI summary Read the abstract
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.
AI Driven Optical Imaging and Spectroscopic Techniques
AI summary Read the abstract
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.
Integrating Artificial Intelligence with Analytical Techniques for Enhanced Microplastics Analysis
AI summary Read the abstract
This review demonstrates how integrating machine learning techniques—including support vector machines, random forests, and deep neural networks—with established spectroscopic and electrochemical methods substantially improves microplastic identification accuracy under challenging real-world conditions like noisy spectra and polymer degradation. AI-driven platforms combined with open-access spectral libraries represent a transformative advance toward scalable, real-time environmental monitoring of microplastics.
Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
AI summary Read the abstract
This review examines traditional and machine learning approaches to detecting and classifying microplastics in food systems, highlighting the limitations of FTIR, Raman spectroscopy, and SEM in complex food matrices. It identifies AI-assisted methods as promising tools for improving detection accuracy and throughput.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.