A Deep Learning Model for Real-time Detection and Prediction of Moisture Content in the White Tea Withering Process Based on Near-Infrared Spectroscopy

A Deep Learning Model for Real-time Detection and Prediction of Moisture Content in the White Tea Withering Process Based on Near-Infrared Spectroscopy | 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 Deep Learning Model for Real-time Detection and Prediction of Moisture Content in the White Tea Withering Process Based on Near-Infrared Spectroscopy wei tao, Bin Chen, Xinkun Yang, Bo Guo, Lingxin Chi, Yihan Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9125852/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract This study proposes a novel deep learning model, named STA-BiGRU-XGBoost, for predicting moisture content during the white tea withering process using near-infrared spectroscopy. The model integrates spatiotemporal attention mechanisms, bidirectional gated recurrent units (BiGRU), and the XGBoost algorithm to address challenges such as extended withering durations, environmental variability, and temporal variations in spectral data. The Maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select critical variables, including hot air velocity, air duct temperature, and spectral absorbance. A spatial attention mechanism enhances feature relevance, while BiGRU captures long-term temporal dependencies. Temporal attention further adjusts the weights of key time steps. XGBoost is incorporated to improve model robustness against noise. Experimental results using a production-line dataset demonstrate that the STA-BiGRU-XGBoost model achieves superior prediction performance, with RMSE = 0.0920, MAE = 0.0772, and R² = 0.9806, significantly outperforming traditional models. Furthermore, the model’s interpretability is validated through attention weight visualization, highlighting key features associated with moisture evaporation dynamics. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing white tea withering real-time moisture content detection and prediction near-infrared spectroscopy deep learning prediction model spatiotemporal attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers invited by journal 28 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 14 Mar, 2026 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. 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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-9125852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614352512,"identity":"9b680213-e101-458d-897b-5c99b3d0e5e1","order_by":0,"name":"wei tao","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"tao","suffix":""},{"id":614352513,"identity":"d1720ba7-9cb6-43d2-8147-f50f0e5e823d","order_by":1,"name":"Bin 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The model integrates spatiotemporal attention mechanisms, bidirectional gated recurrent units (BiGRU), and the XGBoost algorithm to address challenges such as extended withering durations, environmental variability, and temporal variations in spectral data. The Maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select critical variables, including hot air velocity, air duct temperature, and spectral absorbance. A spatial attention mechanism enhances feature relevance, while BiGRU captures long-term temporal dependencies. Temporal attention further adjusts the weights of key time steps. XGBoost is incorporated to improve model robustness against noise. Experimental results using a production-line dataset demonstrate that the STA-BiGRU-XGBoost model achieves superior prediction performance, with RMSE\u0026thinsp;=\u0026thinsp;0.0920, MAE\u0026thinsp;=\u0026thinsp;0.0772, and R\u0026sup2; = 0.9806, significantly outperforming traditional models. 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