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 Conference Paper Faculty Publications- Conference Papers 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 Mohanani, Jayesh; Tulasi, B.; Divya, V.R. Title A name given to the resource Forecasting Global Microplastic Exposure from Processed Foods: Data-Driven Forecasts and Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 3rd International Conference on Emerging Computation and Information Technologies, ICECIT 2025 - Book of Abstracts; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECIT67774.2025.11451087" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECIT67774.2025.11451087</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036375089?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036375089?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 Mohanani J., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Tulasi B., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Divya V.R., CHRIST (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India Description An account of the resource Microplastics are one of the major contaminants of processed foods at a global scale and they contain high risks for human health. Even though the public understanding of the issue has become wider, the knowledge of individual levels of exposure is still very much limited together with the practical tools which can estimate microplastic ingestion. This study proposes a complete data pipeline and a machine learning framework for predicting microplastic contamination and estimating personalised exposure to microplastics depending on country, specific consumption patterns and contamination trends of a long, term nature. The dataset consisted of approximately 18 food groups across 109 countries. So far the data has been through a very thorough preprocessing stage, exploratory analysis, and feature engineering was undertaken, which among other things, included microplastic load aggregation, the addition of lagged variables, and mixing serving sizes information. Random Forest and XGBoost regressors models were trained to predict the levels of contamination from 2019 to 2030. Polynomial Regression delivered the highest accuracy on the training data of R2= 0.9897. While XGBoost gave the best generalization result of R2 = 0.9469 and was therefore chosen as a final forecasting model. The consumption of microplastics through the global food chains is predicted to keep increasing. The originality of this study is in the combination of the long, term contamination data with the selective food, category modelling that allows to generate a reliable framework for the forecasting of the individual intake and to provide to the policy makers EBP (Evidence, Based Policy) advice. 2025 IEEE. Subject The topic of the resource Food Contamination; Machine Learning; Microplastics; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156865-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online