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Purpose <p>Aiming at the problems of spectral overlap caused by the coexistence of multiple microplastics (MPs) in soil and low efficiency of traditional detection methods, this study explores the feasibility of an efficient detection method combining near-infrared (NIR) spectroscopy and machine learning (ML) for the simultaneous qualitative and quantitative analysis of multiple MPs in soil.</p> Methods <p>Taking polypropylene (PP), polyethylene terephthalate (PET) and polyvinyl chloride (PVC) as target pollutants, NIR spectra were collected for eight types of samples (MPs-Free, single/two/three types of MPs-contaminated). After spectral preprocessing with the multivariate scatter correction + standard normal variate (MSC + SNV) method, four ML models, namely partial least squares (PLS), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed for the qualitative and quantitative analysis of soil MPs, and their performance was systematically compared.</p> Results <p>In qualitative classification, all ML models achieved excellent performance with overall accuracies higher than 94%. Among them, PLS performed best, with a macro-averaged F1-score of 98.50 ± 1.16% and an accuracy of 98.55 ± 0.61%, followed by Linear-SVM with 98.02 ± 1.20% accuracy. XGBoost and RF yielded accuracies of 95.00 ± 1.95% and 94.14 ± 1.99%, respectively. In quantitative prediction, model performance was significantly affected by the type and number of coexisting MPs. For single-type MPs, SVM and PLS showed optimal accuracy and stability; for two-type MPs, SVM outperformed other models with higher RPD, lower limit of detection (LOD), and better low-concentration prediction; for three-type MPs, RF and PLS were more suitable for PP and PET, while SVM achieved the best performance for PVC (RPD = 4.6503). Overall, model prediction accuracy and cross-validation stability decreased gradually, while LOD increased slightly with an increasing number of coexisting MPs types.</p> Conclusion <p>The combination of NIR spectroscopy and ML algorithms can reliably achieve simultaneous qualitative and quantitative analysis of multiple MPs in soil. Different models show distinct adaptability to the complexity of mixed MPs systems: linear models (PLS) excel in single-type and simple mixture scenarios, while SVM presents strong robustness in both binary and ternary mixtures. The established NIR-ML framework offers a simple, efficient, and low-cost strategy for rapid screening of multi-component MPs pollution in soil, with good application potential in environmental monitoring and risk assessment.</p>

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Coupling near infrared spectroscopy with machine learning algorithms for simultaneously detecting multiple microplastics in soil

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Jia Long,
  • Xi Li,
  • Renjie Yang,
  • Guimei Dong,
  • Yaping Yu,
  • Huiyong Shan

摘要

Purpose

Aiming at the problems of spectral overlap caused by the coexistence of multiple microplastics (MPs) in soil and low efficiency of traditional detection methods, this study explores the feasibility of an efficient detection method combining near-infrared (NIR) spectroscopy and machine learning (ML) for the simultaneous qualitative and quantitative analysis of multiple MPs in soil.

Methods

Taking polypropylene (PP), polyethylene terephthalate (PET) and polyvinyl chloride (PVC) as target pollutants, NIR spectra were collected for eight types of samples (MPs-Free, single/two/three types of MPs-contaminated). After spectral preprocessing with the multivariate scatter correction + standard normal variate (MSC + SNV) method, four ML models, namely partial least squares (PLS), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed for the qualitative and quantitative analysis of soil MPs, and their performance was systematically compared.

Results

In qualitative classification, all ML models achieved excellent performance with overall accuracies higher than 94%. Among them, PLS performed best, with a macro-averaged F1-score of 98.50 ± 1.16% and an accuracy of 98.55 ± 0.61%, followed by Linear-SVM with 98.02 ± 1.20% accuracy. XGBoost and RF yielded accuracies of 95.00 ± 1.95% and 94.14 ± 1.99%, respectively. In quantitative prediction, model performance was significantly affected by the type and number of coexisting MPs. For single-type MPs, SVM and PLS showed optimal accuracy and stability; for two-type MPs, SVM outperformed other models with higher RPD, lower limit of detection (LOD), and better low-concentration prediction; for three-type MPs, RF and PLS were more suitable for PP and PET, while SVM achieved the best performance for PVC (RPD = 4.6503). Overall, model prediction accuracy and cross-validation stability decreased gradually, while LOD increased slightly with an increasing number of coexisting MPs types.

Conclusion

The combination of NIR spectroscopy and ML algorithms can reliably achieve simultaneous qualitative and quantitative analysis of multiple MPs in soil. Different models show distinct adaptability to the complexity of mixed MPs systems: linear models (PLS) excel in single-type and simple mixture scenarios, while SVM presents strong robustness in both binary and ternary mixtures. The established NIR-ML framework offers a simple, efficient, and low-cost strategy for rapid screening of multi-component MPs pollution in soil, with good application potential in environmental monitoring and risk assessment.