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Efficient Prediction of Microplastic Counts from Mass Measurements
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Researchers evaluated machine learning models including linear regression, kernel ridge regression, and decision trees for predicting microplastic particle counts from aggregate mass measurements, testing on synthetic and experimental datasets. They found that kernel ridge regression performed best, with lower prediction error for larger and more homogeneous samples, and that organic contamination did not substantially reduce predictive accuracy.
Microplastics must be characterized and quantified to assess their impact. This is complicated by the time-consuming and error-prone nature of current quantification procedures. This study evaluates the use of machine learning to estimate the number of microplastic particles on the basis of aggregate particle weight measurements. Synthetic data sets are used to test the performance of linear regression, kernel ridge regression, and decision trees. Kernel ridge regression, which achieves the best performance, is tested on several experimental datasets. The numerical results show that the algorithm is better at predicting the counts of larger and more homogeneous samples and that contamination by organics does not significantly increase error. In mixed samples, prediction error is lower for heavier particles, with an error rate comparable to or better than that of traditional manual counting.
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