[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125379-en":3,"doc-seo-125379-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125379,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Prediction on the Level of Toxicity in Fruits and Vegetables Based on PAHs Using Machine Learning","This study assesses toxicity levels in fruits and vegetables linked to polycyclic aromatic hydrocarbons (PAHs), focusing on regions where industrial and vehicular pollution leads to particulate deposition on plant surfaces. It compares conventional PAH measurement methods such as GC/MS and HPLC, which are accurate yet expensive and time-intensive, against toxicity inference constrained by EFSA limitations. Using artificial intelligence, the work evaluates toxicity based on 16 PAHs with datasets validated statistically, then applies machine learning classification to scale toxicity levels and confirm harmfulness. Results show classification accuracy above 90%, supporting food safety and public health with an interdisciplinary perspective on environmental contaminants.","|  | Nature Environment and Pollution Technology\u003Cbr>An International Quarterly Scientific Journal |  |  | p-ISSN: 0972-6268 (Print copies up to 2016)\u003Cbr>e-ISSN: 2395-3454 | Vol. 24 | No. 2 |  | Article ID\u003Cbr>D1690 |  | 2025 |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Original Research Paper |  |  |  [https://doi.org/10.46488/NEPT.2025.v24i02.D1690](https://doi.org/10.46488/NEPT.2025.v24i02.D1690) |  |  |  |  |  | Open Access Journal |  |  |\n\nPrediction on the Level of Toxicity in Fruits and Vegetables Based on PAHs Using Machine Learning  \nStaphney Texina1†, Sathees Kumar Nataraj2 , Alagammai Renganathan1 and Kavitha Vasantha2  \n1Department of Math and Science, University of Technology Bahrain, Salmabad, Bahrain 2Department of Mechatronics Engineering, University of Technology Bahrain, Salmabad, Bahrain †Corresponding author: Staphney Texina; [texinastaphney@gmail.com](texinastaphney@gmail.com)  \nAbbreviation: Nat. Env. & Poll. Technol.  \n[Website: www.neptjournal.com](Website: www.neptjournal.com)  \nReceived: 09-06-2024  \nRevised: 28-07-2024  \nAccepted: 09-08-2024  \nKey Words:  \nPolycyclic aromatic hydrocarbons Environmental contaminants Fruits and vegetables  \nMachine learning algorithm  \nCitation for the Paper:  \nTexina, S. , Nataraj, S. K. , Renganathan, A. and Vasantha, K. , 2025. Prediction on the level of toxicity in fruits and vegetables based on PAHs using machine learning. Nature Environment and Pollution Technology, 24(2), p. D1690 . [https://doi](https://doi). org/10.46488/NEPT.2025.v24i02.D1690  \nNote: From year 2025, the journal uses Article ID instead of page numbers in citation of the published articles.  \nCopyright: © 2025 by the authors  \nLicensee: Technoscience Publications This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/l](creativecommons.org/l)icenses/by/4 .0/) .  \nABSTRACT  \nThis study focuses on assessing the toxicity levels in fruits and vegetables based on the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in regions affected by industrial and vehicular pollution where the particulate matter deposits on the plant surfaces. Traditional methods, including Gas Chromatography/Mass Spectrometry (GC/MS) and HighPerformance Liquid Chromatography (HPLC), are used to measure PAH levels in fruits and vegetables, which are found to be valuable but expensive and time-consuming. However, the detection of toxicity relies on either expert knowledge or experimental analysis when compared with the limitations set by EFSA (European Food Safety Authority) . Therefore, in this study, artificial intelligence techniques have been employed to evaluate the toxicity levels based on 16 PAHs. The PAH concentrations in fruits and vegetables were collected from different articles corresponding to safe and unsafe datasets and then validated through statistical analysis. The validated dataset is classified using different machine learning algorithms. Based on the output from the neural network, the level of toxicity is also scaled and compared with the targeted outputs. The promising results of the classification of toxicity using artificial intelligence methods are substantiated by an experimental study and validated through statistical methods. From the results, it can be observed that the machine learning algorithm has given classification accuracy of more than 90% along with their degree of harmfulness. This research holds implications for food safety and public health, offering a novel approach to the interdisciplinary understanding of climate change by addressing the impact of environmental contaminants on the edibility of fruits and vegetables.  \nINTRODUCTION  \nIn recent years, there has been a growing concern about the impact of polycyclic aromatic hydrocarbons (PAH) on both environmental and public health (AbdelShafy & Mansour 201","cbCaifc3JxJUHfXl","https://ap.wps.com/l/cbCaifc3JxJUHfXl","pdf",1578867,1,18,"English","en",105,"# Abstract\n# Introduction\n## Background on PAHs and health concerns\n## Sources and exposure in environment\n## PAH contamination in fruits and vegetables","[{\"question\":\"What is the main goal of the study on fruits and vegetables?\",\"answer\":\"To predict and assess toxicity levels in fruits and vegetables based on the presence and concentrations of PAHs.\"},{\"question\":\"Why do the authors use machine learning instead of only traditional lab methods?\",\"answer\":\"Traditional methods like GC/MS and HPLC are accurate but expensive and time-consuming, while toxicity assessment may also rely on expert knowledge or experimental analysis under EFSA-related limitations.\"},{\"question\":\"How many PAHs and which modeling approach are used to evaluate toxicity?\",\"answer\":\"The study evaluates toxicity based on 16 PAHs and classifies toxicity using validated datasets with different machine learning algorithms, including neural-network-based outputs.\"}]","Prediction on the Level of Toxicity in Fruits and Vegetables Based on PAHs Using Machine Learning | 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is the main goal of the study on fruits and vegetables?","Question",{"text":75,"@type":76},"To predict and assess toxicity levels in fruits and vegetables based on the presence and concentrations of PAHs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do the authors use machine learning instead of only traditional lab methods?",{"text":80,"@type":76},"Traditional methods like GC/MS and HPLC are accurate but expensive and time-consuming, while toxicity assessment may also rely on expert knowledge or experimental analysis under EFSA-related limitations.",{"name":82,"@type":73,"acceptedAnswer":83},"How many PAHs and which modeling approach are used to evaluate toxicity?",{"text":84,"@type":76},"The study evaluates toxicity based on 16 PAHs and classifies toxicity using validated datasets with different machine learning algorithms, including neural-network-based 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