Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map
Microplastic (MP) contamination is a growing environmental concern with significant impacts on ecosystems, the economy, and potentially human health. However, accurately detecting and characterizing MPs in biological samples remains a challenge due to the complexity of biological matrices and analyt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/13/7/237 |
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| author | Sureerat Makmuang Abderrahmane Aït-Kaddour |
| author_facet | Sureerat Makmuang Abderrahmane Aït-Kaddour |
| author_sort | Sureerat Makmuang |
| collection | DOAJ |
| description | Microplastic (MP) contamination is a growing environmental concern with significant impacts on ecosystems, the economy, and potentially human health. However, accurately detecting and characterizing MPs in biological samples remains a challenge due to the complexity of biological matrices and analytical limitations. This study presents a novel, non-destructive visible near-infrared multispectral imaging (Vis-NIR-MSI) method combined with a supervised self-organizing map (SOM) to enable rapid qualitative and quantitative analysis of MPs in seafood. We specifically aimed to identify and differentiate four types of microplastics, namely PET, PE, PP, and PS, in the range 1–4 mm, present on the surface of minced shrimp and shrimp shell. For quantification, MPs were incorporated into minced shrimp surface at concentrations ranging from 0.04% to 1% <i>w</i>/<i>w</i>. The modified model achieved a high coefficient of determination (R<sup>2</sup> > 0.99) for PE and PP quantification. Unlike conventional techniques, this approach eliminates the need for pre-sorting or chemical processing, offering a cost-effective and efficient solution for large-scale monitoring of MPs in seafood, with potential applications in food safety and environmental protection. |
| format | Article |
| id | doaj-art-d8d366ab8a15407b877c3dae4a7b4d91 |
| institution | DOAJ |
| issn | 2227-9040 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj-art-d8d366ab8a15407b877c3dae4a7b4d912025-08-20T03:08:00ZengMDPI AGChemosensors2227-90402025-07-0113723710.3390/chemosensors13070237Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing MapSureerat Makmuang0Abderrahmane Aït-Kaddour1Université Clermont Auvergne, INRAE, VetAgroSup, UMRF, 63370 Lempdes, FranceUniversité Clermont Auvergne, INRAE, VetAgroSup, UMRF, 63370 Lempdes, FranceMicroplastic (MP) contamination is a growing environmental concern with significant impacts on ecosystems, the economy, and potentially human health. However, accurately detecting and characterizing MPs in biological samples remains a challenge due to the complexity of biological matrices and analytical limitations. This study presents a novel, non-destructive visible near-infrared multispectral imaging (Vis-NIR-MSI) method combined with a supervised self-organizing map (SOM) to enable rapid qualitative and quantitative analysis of MPs in seafood. We specifically aimed to identify and differentiate four types of microplastics, namely PET, PE, PP, and PS, in the range 1–4 mm, present on the surface of minced shrimp and shrimp shell. For quantification, MPs were incorporated into minced shrimp surface at concentrations ranging from 0.04% to 1% <i>w</i>/<i>w</i>. The modified model achieved a high coefficient of determination (R<sup>2</sup> > 0.99) for PE and PP quantification. Unlike conventional techniques, this approach eliminates the need for pre-sorting or chemical processing, offering a cost-effective and efficient solution for large-scale monitoring of MPs in seafood, with potential applications in food safety and environmental protection.https://www.mdpi.com/2227-9040/13/7/237shrimpmicroplasticsmultispectralSOMsqualitativequantitative |
| spellingShingle | Sureerat Makmuang Abderrahmane Aït-Kaddour Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map Chemosensors shrimp microplastics multispectral SOMs qualitative quantitative |
| title | Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map |
| title_full | Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map |
| title_fullStr | Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map |
| title_full_unstemmed | Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map |
| title_short | Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map |
| title_sort | quantitative and qualitative evaluation of microplastic contamination of shrimp using visible near infrared multispectral imaging technology combined with supervised self organizing map |
| topic | shrimp microplastics multispectral SOMs qualitative quantitative |
| url | https://www.mdpi.com/2227-9040/13/7/237 |
| work_keys_str_mv | AT sureeratmakmuang quantitativeandqualitativeevaluationofmicroplasticcontaminationofshrimpusingvisiblenearinfraredmultispectralimagingtechnologycombinedwithsupervisedselforganizingmap AT abderrahmaneaitkaddour quantitativeandqualitativeevaluationofmicroplasticcontaminationofshrimpusingvisiblenearinfraredmultispectralimagingtechnologycombinedwithsupervisedselforganizingmap |