Advancing microplastics detection and prediction: Integrating traditional methods with machine learning for environmental and food safety application

Zhang, C., Xiao, L., Wang, J.J., Song, Q. and Miao, S. Advancing microplastics detection and prediction: integrating traditional methods with machine learning for environmental and food safety application. Trends in Food Science & Technology, 2025, Vol 159, p.104964.

Abstract

Background Microplastics (MPs) have emerged as a significant environmental threat, necessitating the development of advanced detection and analysis approaches. Traditional identification techniques are limited by accuracy and processing efficiency constraining, hindering a comprehensive understanding of the prevalence and impact of MPs in both environment and food. Scope and approach Machine learning (ML) and deep learning (DL) models have gained attention in MPs research, offering the potential to enhance MPs detection accuracy and predictive capabilities. This review comprehensively explores the integration of ML and DL models into MPs research, particularly on the applications in detection and prediction. We critically assess the current limitations of ML approaches, such as the challenges of limited datasets that restrict the effectiveness of ML approaches. To address these issues, we highlight the significance of data augmentation and synthetic data generation as crucial strategies for improving model robustness and overcoming the limitations in small and imbalanced datasets. Key findings and conclusions This review highlights the significant potential of combining ML models with detection and prediction methods in MPs research. The incorporation of data augmentation techniques is emphasized as crucial for enhancing model performance. This article also highlights the limitations of current ML approaches for MPs analysis, emphasizing the need for further research on real-world samples and nanoscale MPs. Furthermore, it underscores the promising future applications of these techniques in food safety.

Funder

Teagasc