Integrating Artificial Intelligence with Analytical Techniques for Enhanced Microplastics Analysis.

Area of Science:

• Environmental Science
• Analytical Chemistry
• Data Science

Background:

• Microplastics (MPs) are pervasive environmental contaminants with significant ecological and human health implications.
• Conventional detection methods like Raman and FTIR spectroscopy face challenges such as spectral overlap, low resolution, and lengthy analysis times.
• There is a critical need for advanced, efficient, and accurate methods for microplastic identification and quantification.

Purpose of the Study:

• To review recent advancements in microplastic detection techniques.
• To highlight the integration of artificial intelligence (AI) and machine learning (ML) in microplastic analysis.
• To assess the potential of AI/ML for improving the accuracy, speed, and scalability of microplastic monitoring and risk assessment.

Main Methods:

• Review of microscopic, spectroscopic (Raman, FTIR, NIR), and electrochemical detection approaches for microplastics.
• Focus on the application of AI/ML algorithms including support vector machines, decision trees, random forests, principal component analysis, and deep neural networks.
• Exploration of AI-assisted imaging, open-access spectral libraries (SLoPP, SLoPP-E), and data augmentation strategies.

Main Results:

• AI/ML algorithms significantly enhance classification accuracy, especially with noisy spectra and degraded polymers.
• AI-assisted imaging and spectral libraries improve the reproducibility and resolution of microplastic analysis.
• ML-driven electrochemical platforms offer sensor optimization, real-time data interpretation, and predictive modeling capabilities.

Conclusions:

• AI and ML offer transformative potential for overcoming limitations in conventional microplastic detection.
• These advanced computational methods are crucial for scalable monitoring and comprehensive risk assessment of microplastic pollution.
• The integration of AI/ML promises more accurate, efficient, and reliable identification and quantification of microplastics in diverse environmental matrices.