Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing

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.