Microplastic Identification via Holographic Imaging and Machine Learning
Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-02-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.201900153 |
| _version_ | 1828751017654616064 |
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| author | Vittorio Bianco Pasquale Memmolo Pierluigi Carcagnì Francesco Merola Melania Paturzo Cosimo Distante Pietro Ferraro |
| author_facet | Vittorio Bianco Pasquale Memmolo Pierluigi Carcagnì Francesco Merola Melania Paturzo Cosimo Distante Pietro Ferraro |
| author_sort | Vittorio Bianco |
| collection | DOAJ |
| description | Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials. |
| first_indexed | 2024-12-10T20:47:06Z |
| format | Article |
| id | doaj.art-5d24786db5ab4ba9830c5bb6f2102b19 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-12-10T20:47:06Z |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-5d24786db5ab4ba9830c5bb6f2102b192022-12-22T01:34:11ZengWileyAdvanced Intelligent Systems2640-45672020-02-0122n/an/a10.1002/aisy.201900153Microplastic Identification via Holographic Imaging and Machine LearningVittorio Bianco0Pasquale Memmolo1Pierluigi Carcagnì2Francesco Merola3Melania Paturzo4Cosimo Distante5Pietro Ferraro6Institute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Campi Flegrei 34 80078 Pozzuoli NA ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Campi Flegrei 34 80078 Pozzuoli NA ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Monteorni snc University Campus 73100 Lecce ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Campi Flegrei 34 80078 Pozzuoli NA ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Campi Flegrei 34 80078 Pozzuoli NA ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Monteorni snc University Campus 73100 Lecce ItalyInstitute of Applied Sciences and Intelligent Systems (ISASI) National Research Council (CNR) of Italy Via Campi Flegrei 34 80078 Pozzuoli NA ItalyMicroplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.https://doi.org/10.1002/aisy.201900153detectiondigital holographyenvironmental monitoringmachine learningmicroplastics |
| spellingShingle | Vittorio Bianco Pasquale Memmolo Pierluigi Carcagnì Francesco Merola Melania Paturzo Cosimo Distante Pietro Ferraro Microplastic Identification via Holographic Imaging and Machine Learning Advanced Intelligent Systems detection digital holography environmental monitoring machine learning microplastics |
| title | Microplastic Identification via Holographic Imaging and Machine Learning |
| title_full | Microplastic Identification via Holographic Imaging and Machine Learning |
| title_fullStr | Microplastic Identification via Holographic Imaging and Machine Learning |
| title_full_unstemmed | Microplastic Identification via Holographic Imaging and Machine Learning |
| title_short | Microplastic Identification via Holographic Imaging and Machine Learning |
| title_sort | microplastic identification via holographic imaging and machine learning |
| topic | detection digital holography environmental monitoring machine learning microplastics |
| url | https://doi.org/10.1002/aisy.201900153 |
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