A Generative-Transformer Framework for SORS-based Butter Adulteration Quantification

A Generative-Transformer Framework for SORS-based Butter Adulteration Quantification | 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 A Generative-Transformer Framework for SORS-based Butter Adulteration Quantification Zhenfang Liu, Jinlei Li, Zhen Bi, Jungang Lou, Qing Shen, Xiongtao Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9363961/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The widespread use of packaging in commercial food products introduces optical occlusion and spectral mixing that hinder non-destructive internal quality assessment. Therefore, we designed an integrated spatially offset Raman spectroscopy (SORS)-deep learning pipeline and validated its effectiveness using butter-adulteration quantification as a representative through-packaging application. First, adulterated mixtures (10–90% w/w margarine,10% increments), measured both unpackaged and under representative packaging, were acquired with a line-scan SORS system and the resulting scattering regions were subjected to signal-to-noise ratio optimization. Second, an Attention-Gated U-Net (AGUNet) was constructed to generate Raman spectra, aiming to strengthen sparse and low-intensity Raman data. Finally, the processed spectra were fed into a Multiscale Dilated Transformer network (MDTNet), which leverages the receptive-field expansion of dilated convolutions and incorporates a spectral-angle loss to enable accurate quantitative prediction of margarine content. Experiments across four representative packaging types demonstrate that the proposed AGUNet generator followed by the MDTNet regressor outperforms alternative generative strategies and a suite of machine and deep baselines in both spectral-reconstruction fidelity and concentration-estimation accuracy and robustness. Moreover, systematic evaluation of the model's performance under reduced experimental sample sizes demonstrates that synthetically generated spectra can effectively compensate for the loss of information caused by packaging-induced signal attenuation, enabling high predictive accuracy even with substantially fewer experimental samples. Overall, the integrated SORS-deep learning framework provides a practical and scalable route for rapid, non-destructive assessment of product integrity in diverse packaged food systems. Spatially Offset Raman Spectroscopy Through-Packaging Detection Butter Adulteration Deep Spectral Generation Multiscale Transformer Regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 09 Apr, 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. 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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-9363961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626557965,"identity":"7d46191a-e2ee-4712-accc-f3781f90063f","order_by":0,"name":"Zhenfang 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Therefore, we designed an integrated spatially offset Raman spectroscopy (SORS)-deep learning pipeline and validated its effectiveness using butter-adulteration quantification as a representative through-packaging application. First, adulterated mixtures (10\u0026ndash;90% w/w margarine,10% increments), measured both unpackaged and under representative packaging, were acquired with a line-scan SORS system and the resulting scattering regions were subjected to signal-to-noise ratio optimization. Second, an Attention-Gated U-Net (AGUNet) was constructed to generate Raman spectra, aiming to strengthen sparse and low-intensity Raman data. Finally, the processed spectra were fed into a Multiscale Dilated Transformer network (MDTNet), which leverages the receptive-field expansion of dilated convolutions and incorporates a spectral-angle loss to enable accurate quantitative prediction of margarine content. Experiments across four representative packaging types demonstrate that the proposed AGUNet generator followed by the MDTNet regressor outperforms alternative generative strategies and a suite of machine and deep baselines in both spectral-reconstruction fidelity and concentration-estimation accuracy and robustness. Moreover, systematic evaluation of the model's performance under reduced experimental sample sizes demonstrates that synthetically generated spectra can effectively compensate for the loss of information caused by packaging-induced signal attenuation, enabling high predictive accuracy even with substantially fewer experimental samples. 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