Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Harde, Neha; B, Tulasi Title A name given to the resource Deep learning based classification of microplastic in edible food using optical microscopy images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;478; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s44163-026-01246-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44163-026-01246-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040515934?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040515934?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Harde N., CHRIST (Deemed to be University), Karnataka, Bangalore, India; B T., CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource Microplastics (MPs), a prevalent pollution in food, water, and ecosystems around the world, have become a serious environmental and health concern. The traditional detection and classification techniques are labor-intensive by nature and do not support extensive, large-scale monitoring. The main emphasis of this study is to generate a novel image dataset via a simple extraction method that will be useful for classification applications in high-consumption edible food by integrating with the deep-learning model. This study compares the efficacy of several Deep learning (DL) architectures, including MobileNetV2, ResNet101V2, ResNet50V2, InceptionV3, EfficientNetB0, and a baseline Convolutional Neural Network (CNN) in classification into three groups: threads, beads, and fragments. The best performance was recorded by MobileNetV2, ResNet101V2, and ResNet50 V2, all with 98 percent test accuracy and weighted F1-scores of 0.986 and 0.983, respectively, which is a strong and consistent MPs classification. The outcome indicates that the DL models, especially ResNet101V2 and MobileNetV2, outperform the baseline CNN in terms of classification accuracy (98%). The present study provides strong, scalable opportunities for Artificial Intelligence (AI) based solutions for the assessment and reduction of MPs contamination globally in edible food. The Author(s) 2026. Subject The topic of the resource Automated detection; Deep learning; Food contamination; Image classification; MP Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 27310809; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online