Study on the Effect of Moisture Content on the Spectral Detection of Soluble Solids in Apricot (Prunus armeniaca ‘Diao Gan')

Study on the Effect of Moisture Content on the Spectral Detection of Soluble Solids in Apricot (Prunus armeniaca ‘Diao Gan') | 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 Study on the Effect of Moisture Content on the Spectral Detection of Soluble Solids in Apricot (Prunus armeniaca ‘Diao Gan') lei kang, Huaping Luo, Xueting Ma, Jinlong Yu, Hongyang Liu, Huaiyu Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8030114/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract To reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact of moisture content on the detection model effect of SSC in apricot. This study showed that as moisture content decreased, both the absolute values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, indicating a weakening of moisture characteristics. Furthermore, within the 450 nm-1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Under 8-hour apricot drying conditions, the model achieved optimal prediction accuracy with an R p of 0.9844, RMSEP of 0.5712, and RPD of 5.7989, meeting high-precision quantitative requirements. This study discovered the influence of different moisture intervals on the hyperspectral SSC prediction model of apricot fruits, laying a foundation for the subsequent research on moisture content correction spectra to eliminate the influence of moisture content on spectral detection of SSC content. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Materials science Physical sciences/Optics and photonics Hyperspectral Moisture Content Morphological Parameters Soluble Solids Content Quantitative Prediction Model Apricot Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Editor invited by journal 11 Nov, 2025 Submission checks completed at journal 09 Nov, 2025 First submitted to journal 09 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. 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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-8030114","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557305909,"identity":"55cc0d44-dd85-42be-881b-d770587c5346","order_by":0,"name":"lei kang","email":"","orcid":"","institution":"Tarim University","correspondingAuthor":false,"prefix":"","firstName":"lei","middleName":"","lastName":"kang","suffix":""},{"id":557305910,"identity":"d327af7c-e116-48fc-ac7b-8302352c94cc","order_by":1,"name":"Huaping 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Morphological Parameters, Soluble Solids Content, Quantitative Prediction Model, Apricot","lastPublishedDoi":"10.21203/rs.3.rs-8030114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8030114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact 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This study showed that as moisture content decreased, both the absolute values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, indicating a weakening of moisture characteristics. Furthermore, within the 450 nm-1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Under 8-hour apricot drying conditions, the model achieved optimal prediction accuracy with an R\u003csub\u003ep\u003c/sub\u003e of 0.9844, RMSEP of 0.5712, and RPD of 5.7989, meeting high-precision quantitative requirements. 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