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Influence of soil types with different soil-forming process on the qualitative and quantitative detection of microplastics by near-infrared spectroscopy
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Scientists tested whether a light-based scanning tool (near-infrared spectroscopy) can reliably detect tiny plastic bits in different types of farm soil, and found that soil type matters a lot. Some soils, like Brown Earth, made it easy to spot even small amounts of microplastics, while others, like Fluvo-aquic Soil, made detection much less accurate. This matters because microplastics in farmland can work their way into the food we eat, and having a fast, reliable way to detect them is a key step toward understanding and reducing that risk to human health.
Microplastics (MPs) have become a pressing global environmental threat, with soils-acting as sinks for MPs from multiple sources-gaining increasing attention. Near-infrared (NIR) spectroscopy offers a promising tool for MPs detection due to its rapid, non-destructive, and field-applicable features. Although previous studies have focused on the effects of individual soil components on the NIR detection performance of MPs, there is still a lack of systematic research on how the complex background-formed by the coupling of multiple physicochemical properties in natural soils-affects detection performance. This study focuses on soil types with different soil-forming processes, selected five representative agricultural soils to systematically evaluate how the combinations of physicochemical properties they represented affect the performance of NIR-based qualitative and quantitative analysis of MPs in soils. The results demonstrated that soil type significantly affected both the spectral response and detection performance of MPs. Brown Pedocals and Brown Earth exhibited clearer characteristic absorption and stronger linear responses, achieving higher identification accuracy under low (<1.5 %) or zero MPs concentrations and the best quantitative performance (R ≥ 0.988, prediction set root mean square error (RMSEP) ≤ 0.110 %). In contrast, Phaeozem and Red Soil were more prone to misclassification at low concentrations, while Fluvo-aquic Soil showed the poorest quantitative performance. This study is the first to reveal, at a holistic level, the critical constraints posed by natural soil complexity on the NIR detection of MPs, offering targeted empirical evidence to support the application of NIR technology in real-world soil environments.
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