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Updated: Mar 27, 2026

Sampling and Identification of Microplastics in Groundwater

Sampling and Identification of Microplastics in Groundwater

Published on: November 7, 2025

Integrating Artificial Intelligence with Analytical Techniques for Enhanced Microplastics Analysis.

Baljinder Singh¹, Riyapi Das¹, Ajay Kumar¹

¹Department of Biochemistry School of Basic Sciences, Central University of Punjab, Bathinda, India.

Critical Reviews in Analytical Chemistry

|March 25, 2026

Summary

Artificial intelligence (AI) and machine learning (ML) are revolutionizing microplastic (MP) detection by overcoming limitations of traditional methods. These advanced techniques improve accuracy and enable scalable monitoring for ecological and human health risk assessment.

Keywords:

Artificial intelligence detection machine learning microplastics

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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.