Prediction of Thermodynamic Properties of Sacred Pepper (Piper auritum) Using Machine Learning

Prediction of Thermodynamic Properties of Sacred Pepper (Piper auritum) Using Machine Learning | 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 Prediction of Thermodynamic Properties of Sacred Pepper (Piper auritum) Using Machine Learning Cesar Cesar Pérez-Alonso, José Alvarez-Ramirez, Reyna Natividad, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8546355/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 This study compares the application of three Machine Learning (ML) models—Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machines (SVM)—for predicting thermodynamic properties (enthalpy change, ΔH; entropy change, ΔS; and Gibbs free energy change, ΔG) of Piper auritum . The models were developed using experimental data correlating these properties with moisture content and temperature. Each model’s architecture was optimized: ANNs with a three-layer feedforward structure trained with the Levenberg-Marquardt algorithm, RF with property-specific tree counts, and SVM with a Radial Basis Function kernel. Model performance was evaluated using k-fold cross-validation and metrics including R², MAE, MSE, and RMSE. Results show that all three ML approaches effectively capture nonlinear relationships, but ANNs outperformed the others, achieving high R² values (e.g., overall R² > 0.999 for ΔH and ΔS). SVM provided intermediate performance, while RF was the least accurate. ANNs are the most powerful and reliable tool for this regression task, offering a highly accurate computational method to predict thermodynamic behavior and potentially reduce experimental characterization needs. Artificial neural network Sacred pepper Machine learning Random forest Support vector machine Thermodynamic properties Full Text Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.tiff TableS1.docx TableS2.docx Supportingmaterialsv2.docx 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-8546355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571636156,"identity":"b90e34a6-1bf7-43f7-badf-f0de8267ef57","order_by":0,"name":"Cesar Cesar Pérez-Alonso","email":"","orcid":"","institution":"Universidad Autónoma del Estado de México","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"Cesar","lastName":"Pérez-Alonso","suffix":""},{"id":571636159,"identity":"e88e62fb-8c88-403e-aed3-431f09f20ce1","order_by":1,"name":"José Alvarez-Ramirez","email":"","orcid":"","institution":"Universidad Autónoma 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