Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
This study evaluated the effectiveness of coupling machine learning algorithms with short-wave infrared hyperspectral imaging in detecting two types of microplastics - polyamide and polyethylene - with the maximum particle sizes of 50 and 300 μm, respectively, across three concentration ranges (0.0...
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| Language: | English |
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Elsevier
2025-07-01
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| Series: | Soil & Environmental Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949919425000305 |
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| author | Huan Chen Taesung Shin Bosoon Park Kyoung Ro Changyoon Jeong Hwang-Ju Jeon Pei-Lin Tan |
| author_facet | Huan Chen Taesung Shin Bosoon Park Kyoung Ro Changyoon Jeong Hwang-Ju Jeon Pei-Lin Tan |
| author_sort | Huan Chen |
| collection | DOAJ |
| description | This study evaluated the effectiveness of coupling machine learning algorithms with short-wave infrared hyperspectral imaging in detecting two types of microplastics - polyamide and polyethylene - with the maximum particle sizes of 50 and 300 μm, respectively, across three concentration ranges (0.01–0.10, 0.10–1.0, and 1.0–12 %) in soils. Using indium gallium arsenide (InGaAs; 800–1600 nm) and mercury cadmium telluride (MCT; 1000–2500 nm) sensors, we applied logistic regression and support vector machines by employing both linear and nonlinear kernels to analyze spectral features extracted via principal component analysis and partial least squares. The results demonstrated that the overall accuracy for detecting 0.01–12% microplastics was 93.8 ± 1.47% using the MCT sensor, which was higher than 68.8 ± 3.76 % using the InGaAs sensor. Both sensors showed high accuracy (>94 %) when detecting high levels at 1.0–12%) of microplastics in soil. But these accuracies greatly declined as the spiked microplastics concentrations decreased from 1.0–12 to 0.10–1.0% and further to 0.01–0.10%. Moreover, this decline was more pronounced for the InGaAs sensor compared to the MCT sensor and for sub-wavelength spans compared to the full wavelength span under each sensor. The MCT sensor consistently outperformed the InGaAs sensor across all three concentration ranges, potentially due to its extended coverage of 1600–2500 nm and high sensitivity of the detector. Our study highlights the feasibility of the MCT hyperspectral imaging system for rapid and effective detection of microplastics in soils non-invasively at concentrations as low as 0.01%. |
| format | Article |
| id | doaj-art-dbf613608c9e4ee9b714ef84e115d868 |
| institution | Kabale University |
| issn | 2949-9194 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soil & Environmental Health |
| spelling | doaj-art-dbf613608c9e4ee9b714ef84e115d8682025-08-20T03:50:49ZengElsevierSoil & Environmental Health2949-91942025-07-013310015710.1016/j.seh.2025.100157Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imagingHuan Chen0Taesung Shin1Bosoon Park2Kyoung Ro3Changyoon Jeong4Hwang-Ju Jeon5Pei-Lin Tan6Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC, 29634, USAUSDA Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA, 30605, USAUSDA Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA, 30605, USA; Corresponding author.USDA Agricultural Research Service, Coastal Plains Soil, Water & Plant Research Center, Florence, SC, 29501, USARed River Research Station, Louisiana State University Agricultural Center, Bossier City, LA, 71112, USARed River Research Station, Louisiana State University Agricultural Center, Bossier City, LA, 71112, USADepartment of Forestry and Environmental Conservation, Clemson University, South Carolina, 29634, USAThis study evaluated the effectiveness of coupling machine learning algorithms with short-wave infrared hyperspectral imaging in detecting two types of microplastics - polyamide and polyethylene - with the maximum particle sizes of 50 and 300 μm, respectively, across three concentration ranges (0.01–0.10, 0.10–1.0, and 1.0–12 %) in soils. Using indium gallium arsenide (InGaAs; 800–1600 nm) and mercury cadmium telluride (MCT; 1000–2500 nm) sensors, we applied logistic regression and support vector machines by employing both linear and nonlinear kernels to analyze spectral features extracted via principal component analysis and partial least squares. The results demonstrated that the overall accuracy for detecting 0.01–12% microplastics was 93.8 ± 1.47% using the MCT sensor, which was higher than 68.8 ± 3.76 % using the InGaAs sensor. Both sensors showed high accuracy (>94 %) when detecting high levels at 1.0–12%) of microplastics in soil. But these accuracies greatly declined as the spiked microplastics concentrations decreased from 1.0–12 to 0.10–1.0% and further to 0.01–0.10%. Moreover, this decline was more pronounced for the InGaAs sensor compared to the MCT sensor and for sub-wavelength spans compared to the full wavelength span under each sensor. The MCT sensor consistently outperformed the InGaAs sensor across all three concentration ranges, potentially due to its extended coverage of 1600–2500 nm and high sensitivity of the detector. Our study highlights the feasibility of the MCT hyperspectral imaging system for rapid and effective detection of microplastics in soils non-invasively at concentrations as low as 0.01%.http://www.sciencedirect.com/science/article/pii/S2949919425000305PolyamidePolyethyleneMachine learningPartial least squaresPrincipal conponent analysisLinear and nonliearn kernels |
| spellingShingle | Huan Chen Taesung Shin Bosoon Park Kyoung Ro Changyoon Jeong Hwang-Ju Jeon Pei-Lin Tan Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging Soil & Environmental Health Polyamide Polyethylene Machine learning Partial least squares Principal conponent analysis Linear and nonliearn kernels |
| title | Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging |
| title_full | Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging |
| title_fullStr | Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging |
| title_full_unstemmed | Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging |
| title_short | Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging |
| title_sort | accurate detection of low concentrations of microplastics in soils via short wave infrared hyperspectral imaging |
| topic | Polyamide Polyethylene Machine learning Partial least squares Principal conponent analysis Linear and nonliearn kernels |
| url | http://www.sciencedirect.com/science/article/pii/S2949919425000305 |
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