Non-destructive prediction and pixel-level visualization of polysaccharide-based properties in ancient paper using SWNIR hyperspectral imaging and machine learning DOI
Yan Wu, Bin Wang, Jian Chen

et al.

Carbohydrate Polymers, Journal Year: 2024, Volume and Issue: 352, P. 123198 - 123198

Published: Dec. 30, 2024

Language: Английский

NON-DESTRUCTIVE IDENTIFICATION OF MICROPLASTICS IN SOIL USING SPECTROSCOPY AND HYPERSPECTRAL IMAGING DOI
Muhammad Fahri Reza Pahlawan, Ye-Na Kim,

Umuhoza Aline

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118216 - 118216

Published: Feb. 1, 2025

Language: Английский

Citations

0
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil DOI Creative Commons
Muhammad Fahri Reza Pahlawan, Ye-Na Kim, Rudiati Evi Masithoh

et al.

BIO Web of Conferences, Journal Year: 2025, Volume and Issue: 167, P. 05006 - 05006

Published: Jan. 1, 2025

Microplastics in soil significantly threatens ecology, impacting plant growth, soil, and humans health through the food chain. Conventional methods to detect microplastic usually require complicated time-consuming steps. This study used non-destructive hyperspectral imaging techniques visible-near infrared (VNIR, 400-1000 nm) short-wave-infrared (SWIR, 1000-2000) identify surface. Seven cryo-milled polymer were used. Partial least squares discriminant analysis (PLS-DA), linear (LDA), support vector classification (SVC) with linear, polynomial, radial basis function kernels develop calibration model. The result shows that both VNIR SWIR regions, models kernel (PLS-DA, LDA, SVC-linear) superior non-linear model (SVC-poly SVC-RBF). masked image of SVC-linear using SNV spectra was other but could only differentiate from soil. LDA yield original performed perfectly, outperforming a clear each validation image. provides initial insights into detection by (HSI), presenting practical, method for efficient identification polymers without sample preparation.

Language: Английский

Citations

0
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment DOI
Michael Seidel,

Christopher Hutengs,

J. M. Bauer

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113301 - 113301

Published: March 20, 2025

Language: Английский

Citations

0
Microplastics in soil: A comprehensive review of occurrence, sources, fate, analytical techniques and potential impacts DOI Creative Commons

Khaoula En-Nejmy,

Bouchra El Hayany, Mutaz Al-Alawi

et al.

Ecotoxicology and Environmental Safety, Journal Year: 2024, Volume and Issue: 288, P. 117332 - 117332

Published: Nov. 30, 2024

Through their accumulation in soils, microplastics have recently become a matter of concern. The aim this review is to assemble and investigate the recent studies about soil by focusing on sources, occurrence, fate soil, analytical methods. objective also clarify elucidate potential impacts fauna, plants microorganisms. In paper, articles reporting quantity characteristics at 62 sites situated across 17 countries were reviewed. land type, microplastic abundances, types sizes compared. We summarized discussed sampling methods used variation concentration according sources. data showed that from available global ranged 0 3573×10

Language: Английский

Citations

3
Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean DOI
Zhimin Liu, Weijun Wang, Yibo Geng

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 478, P. 135555 - 135555

Published: Aug. 23, 2024

Language: Английский

Citations

2
An Introduction to Machine Learning Tools for the Analysis of Microplastics in Complex Matrices DOI Creative Commons
Brian Coleman

Environmental Science Processes & Impacts, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

This work introduces the reader to machine learning principles and highlights its usage in examining microplastics soil samples.

Language: Английский

Citations

2
Integrating Automated Machine Learning and Metabolic Reprogramming for the Identification of Microplastic in Soil: A Case Study on Soybean DOI
Zhimin Liu, Weijun Wang, Yibo Geng

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Machine learning based workflow for (micro)plastic spectral reconstruction and classification DOI
Yanlong Liu, Ziwei Zhao,

Chunyang Hu

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 369, P. 143835 - 143835

Published: Dec. 1, 2024

Language: Английский

Citations

0
Non-destructive prediction and pixel-level visualization of polysaccharide-based properties in ancient paper using SWNIR hyperspectral imaging and machine learning DOI
Yan Wu, Bin Wang, Jian Chen

et al.

Carbohydrate Polymers, Journal Year: 2024, Volume and Issue: 352, P. 123198 - 123198

Published: Dec. 30, 2024

Language: Английский

Citations

0