Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance.

Autor: Huda FR; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.; Faculty of Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC, V2C0C8, Canada., Richard FS; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia., Rahman I; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia., Moradi S; Faculty of Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC, V2C0C8, Canada., Hua CTY; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia., Wanwen CAS; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia., Fong TL; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia., Mujahid A; Institute of Sustainable and Renewable Energy (ISuRE), Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia., Müller M; Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia. mmueller@swinburne.edu.my.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Apr 17; Vol. 13 (1), pp. 6258. Date of Electronic Publication: 2023 Apr 17.
DOI: 10.1038/s41598-023-33207-x
Abstrakt: Microplastic (MP) contamination on land has been estimated to be 32 times higher than in the oceans, and yet there is a distinct lack of research on soil MPs compared to marine MPs. Beaches are bridges between land and ocean and present equally understudied sites of microplastic pollution. Visible-near-infrared (vis-NIR) has been applied successfully for the measurement of reflectance and prediction of low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) concentrations in soil. The rapidity and precision associated with this method make vis-NIR promising. The present study explores PCA regression and machine learning approaches for developing learning models. First, using a spectroradiometer, the spectral reflectance data was measured from treated beach sediment spiked with virgin microplastic pellets [LDPE, PET, and acrylonitrile butadiene styrene (ABS)]. Using the recorded spectral data, predictive models were developed for each microplastic using both the approaches. Both approaches generated models of good accuracy with R 2 values greater than 0.7, root mean squared error (RMSE) values less than 3 and mean absolute error (MAE) < 2.2. Therefore, using this study's method, it is possible to rapidly develop accurate predictive models without the need of comprehensive sample preparation, using the low-cost option ASD HandHeld 2 VNIR Spectroradiometer.
(© 2023. The Author(s).)
Databáze: MEDLINE
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