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Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction
Md Zayed Bin Zahir Arju1, Nafisa Amin Hridi1, Lamiya Dewan1
1Department of Electrical and Electronic Engineering, University of Dhaka Dhaka-1000 Bangladesh mainul.eee@du.ac.bd mahabib@du.ac.bd.
Abstract:
Microplastic (MP) contamination has become a major concern in recent times, posing a significant threat to the environment and human health. Existing techniques for MP detection require access to expensive and specialized microscopy setups and often demand long turnaround time and extensive labor. Herein, we propose a simple platform for MP detection, where MPs are extracted from salt, sugar, teabag, toothpaste and toothpowder samples, and imaged using a low-cost mobile phone-based microscopy setup. The extraction process involves the isolation of MPs from their matrices using the well-established density separation technique with ZnCl2 solution (1.7 g cm-3) and hydrogen peroxide (H2O2) to oxidize organic matter. A commercially available miniaturized microscopy attachment (TinyScope, $10) is fixed on top of an ordinary cell phone camera and is used to capture about 2490 images of MPs obtained from five different product categories. The YOLOv5 deep learning model was used to detect microplastics in images. It was trained on a dataset of 1990 images, validated with 250 images, and tested on a separate set of 250 images. The presence of plastic content in the detected samples was confirmed by performing attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and the morphologies of the MPs were determined using the field-emission scanning electron microscopy (FE-SEM). Results show that the deep-learning enabled image processing approach can identify MPs with an accuracy of 98%. Overall, the fast, accurate, and affordable detection of MPs in low-resource settings can lead to the monitoring of MP content in consumer products on a more frequent basis.
Insights
This study introduces a low-cost mobile phone microscope for detecting microplastics (MPs) in everyday products. The method achieves 98% accuracy, offering a fast and affordable solution for MP monitoring.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Materials Science
Background:
- Microplastic (MP) contamination is a growing environmental and health concern.
- Current MP detection methods are costly, time-consuming, and labor-intensive.
- There is a need for accessible and efficient MP detection techniques.
Purpose of the Study:
- To develop a simple, low-cost platform for microplastic detection using mobile phone microscopy.
- To extract and image MPs from common consumer products like salt, sugar, and toothpaste.
- To utilize deep learning for accurate MP identification in images.
Main Methods:
- MPs were extracted from various matrices using density separation with ZnCl2 and H2O2.
- Images were captured using a mobile phone equipped with a $10 microscopy attachment (TinyScope).
- The YOLOv5 deep learning model was trained and validated for MP detection, achieving 98% accuracy.
Main Results:
- Microplastics were successfully detected in salt, sugar, teabags, toothpaste, and toothpowder.
- The YOLOv5 model demonstrated high accuracy in identifying MPs from captured images.
- ATR-FTIR and FE-SEM confirmed the presence and morphology of plastic particles.
Conclusions:
- The developed mobile phone-based microscopy platform offers a fast, accurate, and affordable method for MP detection.
- This approach is suitable for low-resource settings, enabling frequent monitoring of MP content in consumer goods.
- The study highlights the potential of accessible technology for addressing environmental contamination issues.
