{"paper_id":"0a657c61-2b29-4ef6-9d7c-689e2d5be64e","body_text":"A Novel AI-Based Detection Framework for Reused Cooking Oils: Advancing Food Safety and Cancer Prevention in Restaurant Foods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel AI-Based Detection Framework for Reused Cooking Oils: Advancing Food Safety and Cancer Prevention in Restaurant Foods Aras Masood Ismael, Shadia Fars Ahmad, Alex Akinbi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Graphical Abstract Abstract Cooking oil use increases the risk of cancer by producing carcinogenic polycyclic aromatic hydrocarbons (PAHs) and aldehydes, especially in developing countries. The detection techniques used today are time-consuming, reliant on laboratories, and incompatible with real-time monitoring. Here, we demonstrate how real-time cooking oil reuse detection is made possible with remarkable accuracy using hybrid deep learning architectures that combine ResNet50/VGG16 with Extreme Learning Machines with Local Receptive Fields (ELM-LRF). We examined 5,000 food samples made with oils that had been reused 0–3 times using multi-modal sensor fusion that included chemical gas sensors, visual imaging, and near-infrared spectroscopy. The ResNet50+ELM-LRF model successfully identified hazardous compound concentrations (PAHs >50 μg/kg, aldehydes >100 μg/kg) with a processing time of 2.3 seconds per sample, while the VGG16+ELM-LRF model showed 95.4% accuracy. An estimated 2.4 million diet-related cancer cases could be avoided each year in developing nations thanks to this portable CNN+ELM-LRF approach, which offers a revolutionary digital health solution for oil reuse detection in resource-constrained environments. Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Drug discovery Earth and environmental sciences/Environmental sciences Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors food safety deep learning public health oil reuse detection cancer prevention ResNet50 VGG16 and ELM-LRF Full Text Additional Declarations No competing interests reported. Supplementary Files aisystemarchitecture.png oildegradationchemistry.png graphicalabstract.png methodologyflowchart.png globalimpactmap.png modelperformancecomparison.png methodologyimplementationcode2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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