Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms | 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 Research Article Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms Abdullahi G. Usman, Sagiru Mati, Sujay Raghavendra Naganna, Hanita Daud, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4663424/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 The need for qualitative determination of mycotoxins in food samples is of paramount importance in designing procedures for their prevention, as well as understanding their adverse effects on humans and animals. In this article, a machine learning technique, specifically support vector regression (SVR) hyphenated with two metaheuristic algorithms - Harris-hawks optimization (HHO) and Particle Swarm Optimization (PSO) models (i.e., SVR-HHO and SVR-PSO) was used to forecast the chromatographic behaviour of various classes of mycotoxins in food samples. Three different metrics were employed to anticipate the model's performance: mean square error (MSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency (NSE). The simulation results showed that the M3 input variable combination demonstrated higher performance accuracy than M1 and M2 with both SVR and its hybridized versions (SVR-HHO and SVR-PSO) during both the training and testing stages. In general, the hybridized model, based on the used assessment measures, SVR-HHO performed better in the training and testing phases than the other two data-driven techniques. Overall, the results show that employing chromatographic techniques, machine learning and metaheuristic approaches can both accurately predict the qualitative characteristics of mycotoxins in food samples. Mycotoxins Chromatographic technique Machine learning Harris-hawks optimization Particle swarm optimization Full Text Additional Declarations No competing interests reported. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4663424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331659953,"identity":"db7dc2bb-c362-4236-ace1-cd951e3aa09b","order_by":0,"name":"Abdullahi G. 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In this article, a machine learning technique, specifically support vector regression (SVR) hyphenated with two metaheuristic algorithms - Harris-hawks optimization (HHO) and Particle Swarm Optimization (PSO) models (i.e., SVR-HHO and SVR-PSO) was used to forecast the chromatographic behaviour of various classes of mycotoxins in food samples. Three different metrics were employed to anticipate the model's performance: mean square error (MSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency (NSE). The simulation results showed that the M3 input variable combination demonstrated higher performance accuracy than M1 and M2 with both SVR and its hybridized versions (SVR-HHO and SVR-PSO) during both the training and testing stages. In general, the hybridized model, based on the used assessment measures, SVR-HHO performed better in the training and testing phases than the other two data-driven techniques. 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