We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Quantitative image analysis of microplastics in bottled water using artificial intelligence
AI summary Read the abstract
Scientists developed an artificial intelligence tool that can automatically detect and count microplastics in bottled water using microscope images. The AI-assisted method proved faster and more consistent than manual counting by human analysts. Better detection tools like this are important for accurately measuring how many microplastics people are consuming through drinking water and assessing the associated health risks.
The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 μm, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient (R) of 0.99. Method precision was demonstrated by a repeatability of 9-16% RSD (n = 7) and within-laboratory reproducibility of 15-27% RSD (n = 21). Accuracy based on recovery was 92 ± 15% and 98 ± 23% at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.
More Papers Like This
Quantitative image analysis of microplastics in bottled water using Artificial Intelligence
AI summary Read the abstract
Researchers developed an artificial intelligence-based image analysis approach to quantitatively detect microplastics in bottled water, addressing the absence of validated analytical methods and enabling more reliable assessment of consumer exposure to microplastic contamination.
Microplastic Contamination Detection in Water using Deep Learning Models
AI summary Read the abstract
Scientists trained a computer program (using AI image recognition) to automatically spot tiny plastic particles in water samples, and it correctly identified microplastics 99.5% of the time—faster and easier than the manual microscope checks experts currently rely on. This matters because microplastics in our water supply are linked to potential health risks, so having a quick, accurate way to detect them could help water treatment facilities catch contamination sooner and keep drinking water safer.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
AI summary Read the abstract
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Deep Learning-Based Prediction and Classification of Microplastics in Water Samples
AI summary Read the abstract
Researchers built an AI tool that can automatically spot and identify tiny plastic particles in drinking water samples, correctly catching them about 92% of the time — much faster than manual lab methods. This matters because it could help water utilities quickly find contamination hotspots and monitor water quality in real time, making it easier to protect drinking water from microplastic pollution before it reaches your tap.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
AI summary Read the abstract
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.