Robust Non-Destructive Prediction of Jackfruit (Artocarpus heterophyllus cv. Tekam Yellow) Colour at Different Maturity Stages Using Visible–Near Infrared Spectroscopy: Influence of Rind and Flesh

Robust Non-Destructive Prediction of Jackfruit (Artocarpus heterophyllus cv. Tekam Yellow) Colour at Different Maturity Stages Using Visible–Near Infrared Spectroscopy: Influence of Rind and Flesh | 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 Robust Non-Destructive Prediction of Jackfruit (Artocarpus heterophyllus cv. Tekam Yellow) Colour at Different Maturity Stages Using Visible–Near Infrared Spectroscopy: Influence of Rind and Flesh Kok Jeng Lim, Phebe Ding, Nazmi Mat Nawi, Mashitah Jusoh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9106139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The Malaysian jackfruit ( Artocarpus heterophyllus ) industry is increasingly challenged by a physiological disorder known as jackfruit bronzing , attributed to Pantoea stewartii subsp. stewartii . This disorder manifests as a yellowish-orange to reddish discolouration of the pulp while leaving the rind visually unaffected, leading to substantial postharvest quality and economic losses. The Tekam Yellow cultivar, in particular, has demonstrated high susceptibility to this condition. This study aimed to evaluate the potential of visible near-infrared spectroscopy (Vis–NIRS) as a non-destructive analytical tool for the early detection of internal bronzing through the estimation of rind and flesh colour parameters (L*, a*, b*, C*, ΔE, and h°). Spectral data were collected from the rind surface of intact jackfruit samples at 10, 12, and 14 weeks after anthesis (WAA) across the 500–950 nm wavelength range. Partial Least Squares Regression (PLSR) models were developed to establish the relationship between spectral reflectance and reference colour metrics. Various spectral pre-processing techniques—including Savitzky–Golay smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC)—were applied to enhance signal quality and minimise scattering effects. The optimised models demonstrated high predictive accuracy, with coefficients of determination for calibration ( R c²) and prediction ( R p²) reaching up to 0.98. Correspondingly, root mean square errors of calibration (RMSEC) and prediction (RMSEP) were as low as 0.29. Overall, calibration performance remained consistently strong across traits and maturities ( R c² ≥ 0.62), indicating that the model structures effectively captured the spectral–colour relationships within the calibration dataset. In contrast, the predictive performance of independent validation models varied substantially between normal and bronzing conditions. Under normal conditions, several models achieved excellent predictive accuracy—for instance, rind colour L* at 10 WAA ( R p² = 0.95, RPD = 16.19) and flesh colour L* at 10 WAA ( R p² = 0.98, RPD = 10.88). Conversely, models developed under bronzing conditions frequently exhibited lower predictive coefficients ( R p²) and residual predictive deviation (RPD) values (< 1.0), suggesting greater spectral heterogeneity and reduced stability in diseased tissues.These findings imply that while overfitting was not evident during calibration, the spectral–chemical mapping was less robust under pathological or variable maturity conditions, thereby reducing external validity. Complementary destructive reference measurements were conducted and analysed using analysis of variance (ANOVA) followed by Fisher’s Protected Least Significant Difference (FPLSD) test at p ≤ 0.05 to confirm the significance of observed differences. Collectively, the results demonstrate that Vis–NIRS applied through the rind offers a promising non-invasive approach for the rapid and accurate detection of internal bronzing in Tekam Yellow jackfruit, facilitating improved quality monitoring and early disease detection at both harvest and postharvest stages. This work highlights the potential of Vis–NIRS as a practical, high-throughput phenotyping and quality assurance tool to support sustainable value-chain management in the Malaysian jackfruit industry. jackfruit robust predicting Pantoea stewartii visible near infrared spectroscopy (Vis-NIRS) intact fruit bronzing colour metrics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 12 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. 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-9106139","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608059613,"identity":"e687441e-91b6-4904-8d8c-8abcd0a975db","order_by":0,"name":"Kok Jeng Lim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACPmYGZgaGA2xAJvMBICEhQ1ALG0ILWwJICw9hLQxgLSAmjwGYJKyFnTvZ4MMZPjlz/jWfX92oseBhYD98dAN+h/FuTpxxg83YcsbbbdY5x4AO40lLu0FIy2GeD2yJG26c3WacwwbUIsFjRpSW+g03zjwzzvlHpJZknhtsCQbne5gf57YRqcVwxhk2ww032MyYc/skeNgI+YWf/+xmiQ/HjskbnD/8+HPOtzo5fvbDx/BqgYJjwEhMYJMA20uEchCoAdp3gPkDkapHwSgYBaNghAEA7FJFjuEE/qYAAAAASUVORK5CYII=","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Kok","middleName":"Jeng","lastName":"Lim","suffix":""},{"id":608059614,"identity":"a37e2781-9000-4a05-b5a3-ac3f1637e71c","order_by":1,"name":"Phebe Ding","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Phebe","middleName":"","lastName":"Ding","suffix":""},{"id":608059615,"identity":"c622b23e-ae2f-4aa2-9358-9bbb9c5cfea7","order_by":2,"name":"Nazmi Mat Nawi","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nazmi","middleName":"Mat","lastName":"Nawi","suffix":""},{"id":608059616,"identity":"f903eb98-3314-451c-86f0-1e88f2b570d4","order_by":3,"name":"Mashitah Jusoh","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mashitah","middleName":"","lastName":"Jusoh","suffix":""}],"badges":[],"createdAt":"2026-03-12 14:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9106139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9106139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105924825,"identity":"e14463a6-5ea0-4822-99b2-54b4c86132ac","added_by":"auto","created_at":"2026-04-01 13:12:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8625783,"visible":true,"origin":"","legend":"","description":"","filename":"VisNIRPredictionofJackfruitColourAppliedFoodScience.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9106139/v1_covered_427100ad-1ec6-4bd6-93cf-65ab4889ecff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Non-Destructive Prediction of Jackfruit (Artocarpus heterophyllus cv. Tekam Yellow) Colour at Different Maturity Stages Using Visible–Near Infrared Spectroscopy: Influence of Rind and Flesh","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"applied-fruit-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Applied Fruit Science","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"jackfruit, robust predicting, Pantoea stewartii, visible near infrared spectroscopy (Vis-NIRS), intact, fruit bronzing, colour metrics","lastPublishedDoi":"10.21203/rs.3.rs-9106139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9106139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Malaysian jackfruit (\u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e) industry is increasingly challenged by a physiological disorder known as \u003cem\u003ejackfruit bronzing\u003c/em\u003e, attributed to \u003cem\u003ePantoea stewartii\u003c/em\u003e subsp. \u003cem\u003estewartii\u003c/em\u003e. This disorder manifests as a yellowish-orange to reddish discolouration of the pulp while leaving the rind visually unaffected, leading to substantial postharvest quality and economic losses. The \u003cem\u003eTekam Yellow\u003c/em\u003e cultivar, in particular, has demonstrated high susceptibility to this condition. This study aimed to evaluate the potential of visible near-infrared spectroscopy (Vis\u0026ndash;NIRS) as a non-destructive analytical tool for the early detection of internal bronzing through the estimation of rind and flesh colour parameters (L*, a*, b*, C*, ΔE, and h\u0026deg;). Spectral data were collected from the rind surface of intact jackfruit samples at 10, 12, and 14 weeks after anthesis (WAA) across the 500\u0026ndash;950 nm wavelength range. Partial Least Squares Regression (PLSR) models were developed to establish the relationship between spectral reflectance and reference colour metrics. Various spectral pre-processing techniques\u0026mdash;including Savitzky\u0026ndash;Golay smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC)\u0026mdash;were applied to enhance signal quality and minimise scattering effects. The optimised models demonstrated high predictive accuracy, with coefficients of determination for calibration (\u003cem\u003eR\u003c/em\u003ec\u0026sup2;) and prediction (\u003cem\u003eR\u003c/em\u003ep\u0026sup2;) reaching up to 0.98. Correspondingly, root mean square errors of calibration (RMSEC) and prediction (RMSEP) were as low as 0.29. Overall, calibration performance remained consistently strong across traits and maturities (\u003cem\u003eR\u003c/em\u003ec\u0026sup2; \u0026ge; 0.62), indicating that the model structures effectively captured the spectral\u0026ndash;colour relationships within the calibration dataset. In contrast, the predictive performance of independent validation models varied substantially between normal and bronzing conditions. Under normal conditions, several models achieved excellent predictive accuracy\u0026mdash;for instance, rind colour L* at 10 WAA (\u003cem\u003eR\u003c/em\u003ep\u0026sup2; = 0.95, RPD\u0026thinsp;=\u0026thinsp;16.19) and flesh colour L* at 10 WAA (\u003cem\u003eR\u003c/em\u003ep\u0026sup2; = 0.98, RPD\u0026thinsp;=\u0026thinsp;10.88). Conversely, models developed under bronzing conditions frequently exhibited lower predictive coefficients (\u003cem\u003eR\u003c/em\u003ep\u0026sup2;) and residual predictive deviation (RPD) values (\u0026lt;\u0026thinsp;1.0), suggesting greater spectral heterogeneity and reduced stability in diseased tissues.These findings imply that while overfitting was not evident during calibration, the spectral\u0026ndash;chemical mapping was less robust under pathological or variable maturity conditions, thereby reducing external validity. Complementary destructive reference measurements were conducted and analysed using analysis of variance (ANOVA) followed by Fisher\u0026rsquo;s Protected Least Significant Difference (FPLSD) test at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 to confirm the significance of observed differences. Collectively, the results demonstrate that Vis\u0026ndash;NIRS applied through the rind offers a promising non-invasive approach for the rapid and accurate detection of internal bronzing in \u003cem\u003eTekam Yellow\u003c/em\u003e jackfruit, facilitating improved quality monitoring and early disease detection at both harvest and postharvest stages. This work highlights the potential of Vis\u0026ndash;NIRS as a practical, high-throughput phenotyping and quality assurance tool to support sustainable value-chain management in the Malaysian jackfruit industry.\u003c/p\u003e","manuscriptTitle":"Robust Non-Destructive Prediction of Jackfruit (Artocarpus heterophyllus cv. 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