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Ensuring the quality and safety of olive-derived products is crucial for consumer health, market integrity, and cultural heritage preservation. Traditional destructive testing methods, though reliable, are labor-intensive, time-consuming, and damage samples, limiting their utility for real-time monitoring. Recently, nondestructive testing (NDT) techniques such as spectroscopy, hyperspectral imaging, ultrasound, and electronic nose/tongue systems have emerged as rapid, non-invasive alternatives capable of assessing parameters like ripeness, contamination, and chemical composition efficiently. The integration of artificial intelligence (AI) and big data analytics (BDA) with these NDT approaches significantly enhances their accuracy, sensitivity, and predictive capabilities, enabling real-time supply chain traceability and quality control. Additionally, computational pharmacology models the interactions and therapeutic effects of bioactive compounds in olive products, supporting holistic evaluations of safety and health benefits. Despite promising progress, challenges such as data heterogeneity, lack of standardized protocols, high instrumentation costs, and regulatory barriers hinder widespread industry adoption. The concept of digital twins offers transformative potential by simulating production processes and quality trajectories, facilitating proactive management and process optimization. This review synthesizes recent advancements (2013–2026) at the intersection of NDT, AI, BDA, digital twins, and computational pharmacology, emphasizing their synergistic integration to revolutionize olive product assessment. It provides a comprehensive overview of these innovative technologies, evaluates their current status, identifies existing challenges, and proposes future pathways toward sustainable, transparent, and scientifically validated practices that enhance product safety, authenticity, and health-promoting properties within the olive industry.
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Almoselhy et al. (2026) studied this question.
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