Advances in nondestructive techniques for quality and safety assessment of olive products using AI and big data analytics: A systematic review

Advances in nondestructive techniques for quality and safety assessment of olive products using AI and big data analytics: A systematic review | 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 Systematic Review Advances in nondestructive techniques for quality and safety assessment of olive products using AI and big data analytics: A systematic review Rania I.M. Almoselhy, Nedyalko Katrandzhiev, Meghit Boumediene Khaled, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9612429/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 Background Ensuring quality and safety of olive products is critical for consumer health, market value, and cultural heritage. Traditional destructive techniques are limited by their time-consuming and sample damage. Nondestructive techniques (NDT) allow real-time and non-invasive evaluation of authenticity, contamination, ripeness, and chemical composition. Artificial intelligence (AI) and big data analytics (BDA) integration enhances data interpretation, decision-making, and predictive modeling fostering transformative impact on olive industry. Aims This systematic review aimed to comprehensively evaluate advances in NDT for quality and safety assessment of olive products, emphasizing the role of AI and BDA and highlighting future prospects for industrial applications. Methods Following PRISMA guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar, from 2010 to 2026 involving spectroscopic, imaging, sensor-based methods, integrated with AI and BDA. Results NDT like near-infrared and Raman spectroscopy, ultrasound, hyperspectral and X-ray imaging, and electronic nose/tongue enable rapid and accurate assessment of chemical composition, contamination, and ripeness. When coupled with AI algorithms, they significantly improve detection sensitivity, classification accuracy, and real-time monitoring. BDA facilitate comprehensive data management, predictive modeling, and supply chain traceability. Computational pharmacology contributes to understanding complex chemical interactions and bioactive compound behavior in olive products, enhancing safety and quality assessments. Challenges include data heterogeneity, lack of standardization, high instrumentation costs, interpretability of AI models, and regulatory hurdles. Conclusion Integrating NDT with AI, BDA, and computational pharmacology offers a sustainable, efficient industrial pathway, with future research needed for standardization, portable device development, multi-sensor data fusion, and regulatory validation. artificial intelligence big data analytics computational pharmacology hyperspectral imaging nondestructive technology olive products quality and safety spectroscopy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 May, 2026 Editor assigned by journal 07 May, 2026 Submission checks completed at journal 07 May, 2026 First submitted to journal 04 May, 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-9612429","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":635506125,"identity":"74cc14ee-c8b3-40ae-8706-ae05dabef2df","order_by":0,"name":"Rania I.M. 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Traditional destructive techniques are limited by their time-consuming and sample damage. Nondestructive techniques (NDT) allow real-time and non-invasive evaluation of authenticity, contamination, ripeness, and chemical composition. Artificial intelligence (AI) and big data analytics (BDA) integration enhances data interpretation, decision-making, and predictive modeling fostering transformative impact on olive industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review aimed to comprehensively evaluate advances in NDT for quality and safety assessment of olive products, emphasizing the role of AI and BDA and highlighting future prospects for industrial applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing PRISMA guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar, from 2010 to 2026 involving spectroscopic, imaging, sensor-based methods, integrated with AI and BDA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNDT like near-infrared and Raman spectroscopy, ultrasound, hyperspectral and X-ray imaging, and electronic nose/tongue enable rapid and accurate assessment of chemical composition, contamination, and ripeness. When coupled with AI algorithms, they significantly improve detection sensitivity, classification accuracy, and real-time monitoring. BDA facilitate comprehensive data management, predictive modeling, and supply chain traceability. Computational pharmacology contributes to understanding complex chemical interactions and bioactive compound behavior in olive products, enhancing safety and quality assessments. 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