{
  "abstract": "Small Island Developing States (SIDS) face unique challenges for microplastics (MPs) monitoring due to limited infrastructure and resources. In this context, we propose and test innovative approaches toward a standardized, low-cost methodology for quantifying MPs in SIDS. We evaluate the giant clam T. maxima as a bio-integrator, combining Nile red (NR) fluorescence staining with automated machine-learning detection. We optimized a digestion protocol using KOH and HNO3 for T. maxima viscera, and developed a DAPI-guided multi-spectra composite imaging approach based on triband fluorescence (DAPI, FITC, TRITC), to enhance polymer detection while reducing blooming artifacts. A semi-automated annotation pipeline using Labkit interactive segmentation with CLIP/UMAP clustering efficiently generated training data from 6711 fluorescence images. A U-Net model was trained on composite images to segment fluorescent particles. The workflow was applied to giant clams from three French Polynesian islands (Makemo, Hao, Tubuai), and NR-based estimates were validated against µFTIR spectroscopy. The model achieved F1-scores of 0.741 for giant clam samples and 0.657 for controls, comparable to human annotation (F1 = 0.680). MPs were detected across all islands, with highest concentrations in gills (16.9–52.7 particles·g−1 wet weight) compared to viscera (2.5–11.0 particles·g−1 ww). µFTIR validation revealed that NR overestimates MP counts (µFTIR: 0.80 ± 0.16 particles·g−1 ww at Tubuai), primarily due to false positives from proteins, cellulose, and stearates. In Tubuai, polyamide (28.9%), PVC (12.6%), and polystyrene (10.7%) were the dominant polymers, suggesting contributions from fishing gear, agriculture, and household waste. While NR-based quantification overestimates absolute MP counts, the automated pipeline demonstrates potential for high-throughput image processing, reproducible sample analysis, and methodological standardization. This workflow represents a first step toward scalable, low-cost approaches for MPs monitoring in insular systems, highlighting areas for further calibration and optimization. Future work should refine fluorescence thresholds and expand validation across species and locations.",
  "abstract_shown": true,
  "affiliations": [
    "Université de Polynésie Française, UMR SECOPOL, Tahiti, Polynésie Française",
    "AI Freelancer Papeete, Tahiti, Polynésie Française",
    "Aix-Marseille Université, LCE, Aix-en-Provence, France",
    "Université de Bordeaux, CNRS, UMR EPOC 5805, Pessac, France",
    "Institut de Recherche pour le Développement, UMR SECOPOL 241, Tahiti, Polynésie Française"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Irène Godéré"
    },
    {
      "affs": [
        1
      ],
      "name": "Taiamiti Edmunds"
    },
    {
      "affs": [
        2
      ],
      "name": "Nabila Gaertner-Mazouni"
    },
    {
      "affs": [
        3
      ],
      "name": "Fiona Gimenez"
    },
    {
      "affs": [
        4
      ],
      "name": "Pascal Wong-Wah-Chung"
    },
    {
      "name": "Stéphanie Lebarillier"
    },
    {
      "name": "Magalie Baudrimont"
    },
    {
      "name": "Chloé Pupier"
    },
    {
      "name": "Nicolas Maihota"
    },
    {
      "name": "Jean-Claude Gaertner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2247p4nb4812r.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357014",
  "fulltext_url": null,
  "id": "2247p4nb4812r",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357014",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2310|Microplastics and Plastic Pollution"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Animals/Invertebrates/Molluscs/Bivalves"
    ],
    [
      "plos",
      "/Biology and life sciences/Zoology/Animals/Invertebrates/Molluscs/Bivalves"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Earth sciences/Geomorphology/Topography/Landforms/Islands"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Polymer chemistry/Macromolecules/Polymers"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Polymer chemistry/Macromolecules/Polymers/Polyvinyl chloride"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Polymer chemistry/Polymers"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Polymer chemistry/Polymers/Polyvinyl chloride"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Plastics"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Polymers"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Polymers/Polyvinyl chloride"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Composite images"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Fluorescence imaging"
    ]
  ],
  "openalex": "W7213435273",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2247p4nb4812r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0357014",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357014"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:polymer chemistry",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Exploring Nile Red and machine learning for microplastics detection in Tridacna maxima",
  "title_text": "Exploring Nile Red and machine learning for microplastics detection in Tridacna maxima",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/2247p4nb4812r",
  "venue": "PLOS One",
  "versions": []
}