Nondestructive quality and safety assessment of olive-derived products via synergistic integration of AI, big data analytics, digital twins, and computational pharmacology

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.

Authors

Institutions

Publication Details

Journal
Discover Agriculture
Published
2026-10-30
DOI
https://doi.org/10.1007/s44279-026-00795-x
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nondestructive quality and safety assessment of olive-derived products via synergistic integration of AI, big data analytics, digital twins, and computational pharmacology

Rania I.M. Almoselhy, Méghit Boumediène Khaled, Afreen Usmani, Mohd Aftab Siddiqui et al.
1 links
Discover Agriculture
Spectroscopy and Chemometric Analyses
article

Nondestructive quality and safety assessment of olive-derived products via synergistic integration of AI, big data analytics, digital twins, and computational pharmacology

Rania I.M. Almoselhy, Méghit Boumediène Khaled, Afreen Usmani, Mohd Aftab Siddiqui, Nedyalko Katrandzhiev, Imen Benchikh, Manisha Bihani, Sabu Thomas, Rajakumari Rajendran
article en

Abstract

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.

Discover AgricultureVol. 4(1)
Chulalongkorn University (TH), Mahatma Gandhi University (IN), Integral University (IN), University of Johannesburg (ZA), University of Food Technologies (BG), Université Djilali de Sidi Bel Abbès (DZ), Agricultural Research Center (EG), College of Engineering Trivandrum (IN), Christ University (IN)
Zero hunger
Openalex Percentile: Top 19%
Spectroscopy and Chemometric Analyses
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon