Novel application of machine learning models to predict secondary metabolites in Dracocephalum moldavica L

Novel application of machine learning models to predict secondary metabolites in Dracocephalum moldavica L | 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 Novel application of machine learning models to predict secondary metabolites in Dracocephalum moldavica L sharareh najafian, Shahnaz Fathi, Sajad Heidari, Roya Movlodzadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8198840/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 aimed to predict the levels of key secondary metabolites-carotenoids, the amino acid proline, and soluble sugars-in Dracocephalum moldavica under salinity stress using artificial neural networks (ANNs). Two ANN models, namely the Radial Basis Function (RBF) and Multilayer Perceptron (MLP), were employed. The models used 13 morphological and physiological traits, including leaf length, leaf width, number of leaves, number of internodes, crown diameter, stem length, internode length, number of lateral stems, lateral stem length, root length, shoot fresh weight, shoot dry weight, and relative water content, as inputs. Model performance was evaluated using a 5-fold cross-validation approach to ensure robust and reliable predictions. The RBF model consistently outperformed the MLP model, demonstrating a superior ability to capture nonlinear relationships between morphological traits and biochemical responses. While the MLP model provided reasonable estimates, it was less accurate for all target metabolites. Within the scope of the current dataset and greenhouse conditions, the RBF model proved to be a reliable tool for predicting secondary metabolites. This approach offers a rapid and cost-effective method to estimate key biochemical compounds, although predictions should be interpreted with caution. Future studies using independent external datasets are encouraged to further assess the model’s generalizability. Biochemical Cleaner production Ecological sustainability Secondary metabolites Dracocephalum moldavica Machine learning Proline accumulation 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. 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Two ANN models, namely the Radial Basis Function (RBF) and Multilayer Perceptron (MLP), were employed.\u003c/p\u003e\u003cp\u003eThe models used 13 morphological and physiological traits, including leaf length, leaf width, number of leaves, number of internodes, crown diameter, stem length, internode length, number of lateral stems, lateral stem length, root length, shoot fresh weight, shoot dry weight, and relative water content, as inputs. Model performance was evaluated using a 5-fold cross-validation approach to ensure robust and reliable predictions.\u003c/p\u003e\u003cp\u003eThe RBF model consistently outperformed the MLP model, demonstrating a superior ability to capture nonlinear relationships between morphological traits and biochemical responses. 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