Soft sensor modeling for the fermentation process of Pichia pastoris based on the transfer modeling of substructural domain

Soft sensor modeling for the fermentation process of Pichia pastoris based on the transfer modeling of substructural domain | 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 Soft sensor modeling for the fermentation process of Pichia pastoris based on the transfer modeling of substructural domain Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shanwei Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4976105/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Aiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model, this paper proposes a soft sensor modeling method based on the transfer modeling framework of substructural domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructural domains, and adaptively weight the substructural domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices W s and W t that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructural domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model. Taking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method. Substructural domain Transfer learning Soft sensor Pichia pastoris Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 26 Aug, 2024 Submission checks completed at journal 26 Aug, 2024 First submitted to journal 26 Aug, 2024 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. 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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-4976105","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345270764,"identity":"973287f5-cae4-4fa7-8ce1-09dc4048fcca","order_by":0,"name":"Bo Wang","email":"","orcid":"","institution":"Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu 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