Prediction of glucose content in zinc gluconate production: based on artificial intelligence and machine learning

Prediction of glucose content in zinc gluconate production: based on artificial intelligence and machine learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 August 2025 V1 Latest version Share on Prediction of glucose content in zinc gluconate production: based on artificial intelligence and machine learning Authors : Dianzheng Zhuang 0009-0000-2328-9423 [email protected] , Yulong Wu , Anding Yang , Fei Xue , Yaohuan Tang , Xinyu Zhang , Kai Wang , and Xuejun Li Authors Info & Affiliations https://doi.org/10.22541/au.175428366.60452938/v1 168 views 95 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurately predicting the change in glucose content during the production of zinc gluconate by is crucial for monitoring the reaction process. The presence of a large amount of glucose oxidase in the reaction solution can affect the accuracy of the accuracy of the enzyme electrode method. This study used a neural network algorithm to predict glucose concentrations during zinc gluconate production. Data from online sensors on the reaction vessel were processed to remove outliers, interpolate missing values, and reduce noise. Using mutual information theory, key features highly correlated with glucose concentration were identified. Comparative analysis confirmed that the Long Short-Term Memory (LSTM) model with a sliding window technique outperformed others, achieving a goodness-of-fit of 0.994. This study accurately predicted the glucose concentration during the production of zinc gluconate by the bienzymatic method, which provided valuable guidance for research and industrial production. Supplementary Material File (manuscript.pdf) Download 954.03 KB Information & Authors Information Version history V1 Version 1 04 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords mutual information theory neural network prediction of glucose content sliding window Authors Affiliations Dianzheng Zhuang 0009-0000-2328-9423 [email protected] Shenyang University of Technology View all articles by this author Yulong Wu Shenyang University of Technology View all articles by this author Anding Yang Shenyang University of Technology View all articles by this author Fei Xue Shenyang University of Technology View all articles by this author Yaohuan Tang Shenyang University of Technology View all articles by this author Xinyu Zhang Shenyang University of Technology View all articles by this author Kai Wang Liaoyang Fuqiang Biotechnology Co Ltd View all articles by this author Xuejun Li Shenyang University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 168 views 95 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dianzheng Zhuang, Yulong Wu, Anding Yang, et al. Prediction of glucose content in zinc gluconate production: based on artificial intelligence and machine learning. Authorea . 04 August 2025. DOI: https://doi.org/10.22541/au.175428366.60452938/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175428366.60452938/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffe6dc50ace09d6',t:'MTc3OTQ4MDI1NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();