Max-INtensity Untargeted Transformation (MINUT) for Direct Chemometric Modeling of High-Resolution Mass Spectrometry Data

Max-INtensity Untargeted Transformation (MINUT) for Direct Chemometric Modeling of High-Resolution Mass Spectrometry Data | 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 Max-INtensity Untargeted Transformation (MINUT) for Direct Chemometric Modeling of High-Resolution Mass Spectrometry Data Christophe CORDELLA, Valentin MEO, Benedicte GAURIAT, Jean-Francois GARNIER, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8049758/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Conventional untargeted classification methods in chromatography-coupled high-resolution mass spectrometry (HRMS) rely on preprocessing steps that distort the data, introduce information loss, compromise data integrity, and limit chemical interpretability. To overcome these limitations, we developed MINUT (Max-INtensity Untargeted Transformation), a novel framework for processing chromatography-coupled HRMS data that uses a two-dimensional maximum intensity binning approach. This method preserves the full resolution of both mass-to-charge ratio (m/z) and retention time, while remaining agnostic to assumptions about the data. As a result, MINUT enables direct chemometric modeling with minimal preprocessing and without compromising analytical precision. We validated MINUT and demonstrated its versatility across biological, clinical, and food authenticity datasets, including two public biomedical benchmarks, i.e. lung cancer and COVID-19 plasma samples. The method consistently outperformed conventional metabolomic pipelines in classification accuracy and interpretability. It successfully recovered known biomarkers such as sphingosine in the case of COVID-19 samples, while also revealing novel discriminative compounds. MINUT is a robust, scalable, and generalizable tool for HRMS-based classification and biomarker identification that lowers analytical costs, improves reproducibility, and enables interpretable HRMS classification across disciplines. Therefore, our results have major implications across wide range of disciplines, including clinical diagnostics, environmental sciences, food analysis, toxicology, and biotechnology. Biological sciences/Biological techniques Health sciences/Biomarkers Physical sciences/Chemistry Biological sciences/Computational biology and bioinformatics Chromatography-coupled high-resolution mass spectrometry Untargeted classification Biomarker discovery Chemometric modeling Full Text Additional Declarations No competing interests reported. Supplementary Files suplementary.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Editor assigned by journal 09 Nov, 2025 Submission checks completed at journal 09 Nov, 2025 First submitted to journal 06 Nov, 2025 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-8049758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":541102416,"identity":"1885c921-0b5c-472a-be70-33c1a50a9246","order_by":0,"name":"Christophe 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