A Novel Approach for Fast Microplastic Quantification in Sediments Using Machine Learning—Spectrometer Combinations

The accumulation of microplastics in surface soils and sediments has raised significant concerns due to their potential environmental risks. Conventional quantitative methods for microplastics often require time-consuming pretreatment and statistical counting, rather than providing direct concentration data, complicating cross-study comparisons. To rapidly investigate microplastic pollution in environmental samples, machine learning (ML) algorithms combined with spectrometers have been employed to estimate microplastic concentrations without the need for extraction. While previous research has primarily focused on microplastic-spiked soils, this study explores the use of river and loess sediments spiked with four commonly used plastic polymers: polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC) at concentrations ranging from 0.1 wt