We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Machine learning-driven estimation of microplastic percentage yield for rapid and accurate quantification
Summary
Scientists used artificial intelligence to make it faster and more accurate to measure how much microplastic is actually in soil samples—a process that's historically been tricky and inconsistent. This matters because microplastics in farm soil can end up in our food and water, so having reliable measurement tools helps researchers better track contamination and understand potential risks to human health. While this study focuses on lab methods rather than health effects directly, better detection is a key step toward understanding how much plastic pollution we're really exposed to.
Accurate microplastic (MP) quantification in agricultural soils is critical for environmental risk assessment, yet variability in extraction efficiency remains a significant barrier. This study This study investigated machine learning (ML) algorithms to predict MP percentage yield and develop interval-wise correction factors for improved MP quantification. Soils were spiked with four MP types and subjected to density separation using seven brine solutions (e.g., zinc chloride and sodium iodide; 1.00–1.58 g/cm 3 ). Pearson correlation and feature importance scores identified brine density as the dominant driver (score = 0.52), while leave-one-condition-out (LOCO) analysis further validated the robust non-linear superiority of ensemble ML models. Among the six ML model, the random-forest (RF) algorithm exhibited the highest coefficient of determination (R 2 ) at 0.991 with the lowest error metrics. Furthermore, RF-driven interval-wise correction factors demonstrated superior adjustment efficiency, effectively aligning predicted yields with target values across diverse recovery scenarios. The proposed integrated framework offers a scalable approach for supporting standardized extraction protocols. The findings of this study can help for an understanding of the complex interaction between experimental parameters and recovery MP yields, ultimately facilitating more precise laboratory-scale MP monitoring.