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TRUVACE RECORD VERSION
record: TRV-2026-0547
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-24T00:40:13.952181Z
status: published
lens: g_space
sector: lifestyle
headline: Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review
dek: Abstract Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, pre…
gain_title: AI-driven real-time contamination detection, automated defect detection, and resource-efficient agriculture techniques improved food safety, quality consistency, and security by reducing public health risks and optimizing shelf-life and supply chains.
problem_title: (none)
trace_subject: (none)
gain_reading: AI-driven real-time contamination detection, automated defect detection, and resource-efficient agriculture techniques improved food safety, quality consistency, and security by reducing public health risks and optimizing shelf-life and supply chains.
gain_evidence: AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring | reducing public health risks | It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance
problem_reading: (none)
problem_evidence: (none)
quick_read: A comprehensive review published January 11, 2025 synthesized literature from 1990 to 2024 on AI in food systems, finding that machine learning, deep learning, computer vision and NLP are used for real-time contamination detection, predictive risk modeling, compliance monitoring, defect detection, shelf-life prediction, yield forecasting, and supply chain optimization, often integrated with processing techniques like high-pressure processing, UV treatment, pulsed electric fields, cold plasma, and irradiation.

These capabilities matter because they directly affect public health risks, consistency of nutritious food, and availability and accessibility of food resources, while integration with IoT, blockchain and sensors could improve transparency and efficiency; uncertainty remains around data limitations, model generalizability, and ethical concerns that the review flags as barriers to reliable deployment.
limitation: Review identifies persistent barriers including data limitations, model generalizability, and ethical concerns that constrain deployment, plus research gaps in transparency and decision-making.
tag: Evidence-backed gain
key_points: Review synthesized literature from 1990 to 2024 on machine learning, deep learning, natural language processing, and computer vision in food systems. | Integration with high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation to ensure microbial safety and extend shelf life. | Combination with Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control.
rundown: The review covers AI methods including machine learning, deep learning, natural language processing, and computer vision applied across the food supply chain, drawing on literature from 1990 to 2024.

It details integration with advanced processing methods such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, and with IoT, blockchain, and AI-powered sensors for predictive analytics and automated quality control.
sources:
- peer_reviewed | Discover Applied Sciences | https://doi.org/10.1007/s42452-025-06472-w | 2025-01-11
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