[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120059-en":3,"doc-seo-120059-105":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},120059,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Advancing food security - The role of machine learning in pathogen detection","Machine Learning (ML) has become a key advancement for detecting pathogens, especially within food safety. This review summarizes current progress and how ML supports real-time foodborne pathogen detection and risk assessment. By combining AI-biosensing with deep learning, ML can speed up identification, shorten detection time, and improve accuracy, as reported across multiple studies and use cases. Applications cover organisms such as Escherichia coli and Pseudomonas aeruginosa, while benefits include improved epidemic prevention, customer safety, and operational efficiency. Remaining obstacles include data quality, model interpretability, and regulatory compliance, requiring transparent models and rigorous validation; future directions integrate ML with IoT and blockchain for real-time monitoring, traceability, and transparency.","University of Birmingham  \nAdvancing food security  \nOnyeaka, Helen; Akinsemolu, Adenike; Miri, Taghi; Nnaji, Nnabueze Darlington; Emeka, Clinton ; Tamasiga, Phemelo; Pang, Gu; Al-Sharify, Zainab T.  \nDOI:  \n10.1016/j.afres.2024.100532  \nLicense:  \nCreative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nOnyeaka, H, Akinsemolu, A, Miri, T, Nnaji, ND, Emeka, C, Tamasiga, P, Pang, G & Al-Sharify, ZT 2024,'Advancing food security: The role of machine learning in pathogen detection', Applied Food Research, vol. 4, no. 2, 100532. [https://doi.org/10.1016/j.afres.2024.100532](https://doi.org/10.1016/j.afres.2024.100532)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 02. Aug. 2026  \nApplied Food Research 4 (2024) 100532  \nContents lists available at ScienceDirect  \nApplied Food Research  \njournal [homepage: www.elsevier.com/locate/afres](homepage: www.elsevier.com/locate/afres)  \n| Advancing food security: The role of machine learning in pathogen detection\u003Cbr>Helen Onyeaka a,*, Adenike Akinsemolua,b,c, Taghi Miria, Nnabueze Darlington Nnajia,d, Clinton Emekae, Phemelo Tamasigaf, Gu Pang g, Zainab Al-sharifya,h,i\u003Cbr>a School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK b The Green Institute, Akure Road, Ondo 351101, Ondo State, Nigeria\u003Cbr>c The Institute for Oil, Gas and Environment, Energy and Sustainable Development, Afe Babalola University, Ado Ekiti 360001, Ekiti State, Nigeria d Department of Microbiology, University of Nigeria, Nsukka, Nigeria\u003Cbr>e Department of Food Science, College of Food and Agriculture, United Arab Emirates University, Al Ain, United Arab Emirates f German Institute of Development and Sustainability, Bonn, Germany\u003Cbr>g Birmingham Business School, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK\u003Cbr>h Environmental Engineering, Mustansiriyah University, Baghdad, Iraqi Pharmacy Department, Al Hikma University College, Baghdad, Iraq |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Food safety Pathogen monitoring ML pathogen detection Predictive analytics AI health solutions |  | Machine Learning (ML) has emerged as an important advancement in pathogen detection, particularly in the field of food safety. This paper reviews current advances and the application of machine learning in real-time foodborne pathogen detection and risk assessment. ML accelerates pathogen ","cbCaipx6UYVfkJOh","https://ap.wps.com/l/cbCaipx6UYVfkJOh","pdf",2038574,1,14,"English","en",105,"# Introduction\n## Current burden of foodborne illness and need for systematic pathogen control\n## Traditional detection approaches and transition toward ML\n## Real-world ML applications for pathogen detection and prediction\n## Challenges: data quality, interpretability, and regulation\n## Future directions: IoT and blockchain for real-time management","[{\"question\":\"How does machine learning improve pathogen detection in food safety?\",\"answer\":\"Machine learning accelerates pathogen identification by leveraging AI-biosensing and deep learning models, which can reduce detection time and improve accuracy.\"},{\"question\":\"Which real-world applications and pathogens are discussed?\",\"answer\":\"The review examines applications including detection of Escherichia coli, Pseudomonas aeruginosa, and Magnaporthe oryzae, focusing on quick detection, disease prediction, and contamination source identification.\"},{\"question\":\"What challenges limit the adoption of ML in pathogen detection?\",\"answer\":\"Key challenges include data quality, model interpretability, and regulatory compliance, making transparent models and rigorous validation important.\"}]","Advancing food security - The role of machine learning in pathogen detection | PDF",1785727938,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advancing-food-security-the-role-of-machine-learning-in-pathogen-detection","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advancing-food-security-the-role-of-machine-learning-in-pathogen-detection/120059/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does machine learning improve pathogen detection in food safety?","Question",{"text":75,"@type":76},"Machine learning accelerates pathogen identification by leveraging AI-biosensing and deep learning models, which can reduce detection time and improve accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which real-world applications and pathogens are discussed?",{"text":80,"@type":76},"The review examines applications including detection of Escherichia coli, Pseudomonas aeruginosa, and Magnaporthe oryzae, focusing on quick detection, disease prediction, and contamination source identification.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges limit the adoption of ML in pathogen detection?",{"text":84,"@type":76},"Key challenges include data quality, model interpretability, and regulatory compliance, making transparent models and rigorous validation important.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]