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JNFS 2026, 11(2): 166-168 |
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Marzban A, Emami P, Dowlati M. Artificial Intelligence in Food Safety: Applications and Opportunities. JNFS 2026; 11 (2) :166-168
URL: http://jnfs.ssu.ac.ir/article-1-1359-en.html
URL: http://jnfs.ssu.ac.ir/article-1-1359-en.html
Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
Keywords: Artificial intelligence, food quality assurance, sensory evaluation, predictive analytics, sustainability
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1 Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 2 Department of Emergency Medical Sciences, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran; 3 Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
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Artificial Intelligence in Food Safety: Applications and Opportunities
Ameneh Marzban; PhD1, Payam Emami; MSc2 & Mohsen Dowlati; PhD*3
Ameneh Marzban; PhD1, Payam Emami; MSc2 & Mohsen Dowlati; PhD*3
1 Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 2 Department of Emergency Medical Sciences, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran; 3 Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
| ARTICLE INFO | ||
| EDITORIAL ARTICLE | *Corresponding author mohsendowlati.69@gmail.com Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran. Postal code: 1449614535 Tel: +982188797301 |
|
| Article history: Received: 4 Jun 2025 Revised: 25 Oct 2025 Accepted: 21 Dec 2025 |
The global food industry faces increasing pressure to ensure high standards of safety, sustainability, and consumer satisfaction (Marzban and Sadeghi-Nodoushan, 2025). As expectations grow, traditional food quality assurance methods struggle to keep pace with the complexity and scale of modern production (Marzban et al., 2023). Artificial intelligence (AI) emerged as a revolutionary force, reshaping the way food quality is monitored, assessed, and controlled (Marzban et al., 2023). By integrating AI-driven technologies, manufacturers and regulatory bodies can enhance efficiency, minimize risks, and implement predictive solutions that were once considered unattainable (Emami and Marzban, 2023).
In practical terms, AI is now actively used in various stages of food production from raw material inspection to final packaging (Marzban and Sadeghi-Nodoushan, 2025). For example, AI algorithms are deployed to detect contaminants such as metal fragments or microbial traces in real time, using hyperspectral imaging and biosensors (Addanki et al., 2022). These systems not only improve detection accuracy but also reduce the time required for lab-based testing. In food logistics, AI helps optimize cold chain management by predicting temperature fluctuations and adjusting storage conditions to prevent spoilage. Moreover, AI-powered robotics are increasingly used in food sorting and grading, ensuring consistency and reducing waste (Chhetri, 2024).
AI plays a crucial role in transforming food quality assurance through real-time monitoring, predictive analytics, and automated assessment (Erkinjon and Feruz, 2024). AI-powered sensors track production variables with precision, detecting inconsistencies in texture, color, and composition as products move through processing lines (Kumar et al., 2021). This ensures uniform quality while significantly reducing human error. Predictive analytics, driven by machine learning algorithms, analyze vast datasets to anticipate potential quality issues before they arise, enabling proactive intervention. Furthermore, computer vision applications in AI facilitate automated inspections, identifying packaging defects, signs of spoilage, and inaccuracies in portion sizes, streamlining efficiency while upholding rigorous standards (Sahni et al., 2021).
Importantly, these AI applications directly contribute to food safety by enabling early detection of hazards, enforcing hygiene protocols, and ensuring traceability (Sharma et al., 2021). For instance, AI systems integrated with block chain technology allow for real-time tracking of food origin and handling conditions, which is critical during contamination outbreaks. In meat processing plants, AI tools monitor microbial growth and chemical residues, helping regulatory bodies enforce safety standards more effectively (Zatsu et al., 2024).
The impact of AI-driven food quality improvements extends beyond the factory floor. Advanced sensory evaluation methods leverage AI to analyze taste, aroma, and texture, refining product development to match consumer preferences with scientific precision (Chhetri, 2024). AI-enhanced nanotechnology allows food properties to be assessed at the molecular level, with embedded nanoparticles monitoring chemical changes during storage, preventing early spoilage, and extending shelf life. Additionally, AI assists in developing standardized metrics for organic food certification, ensuring consistency in environmental impact assessments, production processes, and nutritional profiling (Addanki et al., 2022).
Furthermore, AI contributes to public health by supporting early warning systems for foodborne illnesses (Kumar et al., 2021). Machine learning models analyze epidemiological data and consumer complaints to identify patterns that may indicate contamination risks. These insights enable faster response times and targeted recalls, reducing the spread of illness and protecting vulnerable populations (Marzban and Sadeghi-Nodoushan, 2025).
Despite its transformative potential, AI implementation in food quality assurance presents both opportunities and challenges (Sahni et al., 2021). Automation significantly enhances efficiency, reducing labor-intensive tasks, accelerating inspection timelines, and minimizing human errors. Sustainability efforts benefit as AI optimizes resource allocation, reduces waste, and supports
eco-friendly production models (Sharma et al., 2021). Transparent AI-driven quality assurance mechanisms reinforce consumer trust, offering verifiable insights into food safety and provenance (Zatsu et al., 2024).
However, challenges persist in widespread adoption. AI systems require substantial investment in infrastructure and expertise, posing financial barriers for smaller food producers (Chhetri, 2024). Ethical concerns arise regarding data privacy and algorithmic biases, requiring strict oversight in AI application policies. Additionally, successful integration demands close collaboration between food scientists, regulatory bodies, and AI developers to refine accuracy and maintain compliance with industry standards (Erkinjon and Feruz, 2024).
The intersection of artificial intelligence and food quality assurance represents a paradigm shift in safeguarding consumer well-being (Sahni et al., 2021). By harnessing AI’s capabilities, the industry can establish more adaptive, data-driven systems that elevate food safety, sustainability, and efficiency to unprecedented levels (Sharma et al., 2021). As technology continues to advance, strategic adoption and ethical considerations will determine how AI reshapes the future of food quality assurance in the years to come (Zatsu et al., 2024).
In conclusion, the practical applications of AI in food industries are vast and growing. From contamination detection and predictive maintenance to personalized nutrition and regulatory compliance, AI is not only enhancing food safety but also redefining operational standards. Future research should focus on scalable solutions for small producers and ethical frameworks to ensure responsible AI deployment across the food sector.
References
Addanki M, Patra P & Kandra P 2022. Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied food research. 2 (2): 100126.
Chhetri KB 2024. Applications of artificial intelligence and machine learning in food quality control and safety assessment. Food engineering reviews. 16 (1): 1-21.
Emami P & Marzban A 2023. The synergy of artificial intelligence (AI) and geographic information systems (GIS) for enhanced disaster management: opportunities and challenges. Disaster medicine and public health preparedness. 17: e507.
Erkinjon R & Feruz K 2024. The role of artificial intelligence in enhancing food safety and quality control. Western European journal of modern experiments and scientific methods. 2 (5): 233-236.
Kumar I, Rawat J, Mohd N & Husain S 2021. Opportunities of artificial intelligence and machine learning in the food industry. Journal of food quality. 2021 (1): 4535567.
Marzban A, Dowlati M & Sadeghi-Nodoushan F 2023. The Effects of Climate Change on Food Security. Journal of nutrition and food security. 8 (3): 340-342.
Marzban A & Sadeghi-Nodoushan F 2025. The role of artificial intelligence in food supply chain management during natural disasters. Journal of nutrition and food security. 10 (3): 338-340.
Sahni V, Srivastava S & Khan R 2021. Modelling techniques to improve the quality of food using artificial intelligence. Journal of food quality. 2021 (1): 2140010.
Sharma S, Gahlawat VK, Rahul K, Mor RS & Malik M 2021. Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics. 5 (4): 66.
Zatsu V, et al. 2024. Revolutionizing the food industry: The transformative power of artificial intelligence-a review. Food chemistry. 24 (X): 101867.
In practical terms, AI is now actively used in various stages of food production from raw material inspection to final packaging (Marzban and Sadeghi-Nodoushan, 2025). For example, AI algorithms are deployed to detect contaminants such as metal fragments or microbial traces in real time, using hyperspectral imaging and biosensors (Addanki et al., 2022). These systems not only improve detection accuracy but also reduce the time required for lab-based testing. In food logistics, AI helps optimize cold chain management by predicting temperature fluctuations and adjusting storage conditions to prevent spoilage. Moreover, AI-powered robotics are increasingly used in food sorting and grading, ensuring consistency and reducing waste (Chhetri, 2024).
AI plays a crucial role in transforming food quality assurance through real-time monitoring, predictive analytics, and automated assessment (Erkinjon and Feruz, 2024). AI-powered sensors track production variables with precision, detecting inconsistencies in texture, color, and composition as products move through processing lines (Kumar et al., 2021). This ensures uniform quality while significantly reducing human error. Predictive analytics, driven by machine learning algorithms, analyze vast datasets to anticipate potential quality issues before they arise, enabling proactive intervention. Furthermore, computer vision applications in AI facilitate automated inspections, identifying packaging defects, signs of spoilage, and inaccuracies in portion sizes, streamlining efficiency while upholding rigorous standards (Sahni et al., 2021).
Importantly, these AI applications directly contribute to food safety by enabling early detection of hazards, enforcing hygiene protocols, and ensuring traceability (Sharma et al., 2021). For instance, AI systems integrated with block chain technology allow for real-time tracking of food origin and handling conditions, which is critical during contamination outbreaks. In meat processing plants, AI tools monitor microbial growth and chemical residues, helping regulatory bodies enforce safety standards more effectively (Zatsu et al., 2024).
The impact of AI-driven food quality improvements extends beyond the factory floor. Advanced sensory evaluation methods leverage AI to analyze taste, aroma, and texture, refining product development to match consumer preferences with scientific precision (Chhetri, 2024). AI-enhanced nanotechnology allows food properties to be assessed at the molecular level, with embedded nanoparticles monitoring chemical changes during storage, preventing early spoilage, and extending shelf life. Additionally, AI assists in developing standardized metrics for organic food certification, ensuring consistency in environmental impact assessments, production processes, and nutritional profiling (Addanki et al., 2022).
Furthermore, AI contributes to public health by supporting early warning systems for foodborne illnesses (Kumar et al., 2021). Machine learning models analyze epidemiological data and consumer complaints to identify patterns that may indicate contamination risks. These insights enable faster response times and targeted recalls, reducing the spread of illness and protecting vulnerable populations (Marzban and Sadeghi-Nodoushan, 2025).
Despite its transformative potential, AI implementation in food quality assurance presents both opportunities and challenges (Sahni et al., 2021). Automation significantly enhances efficiency, reducing labor-intensive tasks, accelerating inspection timelines, and minimizing human errors. Sustainability efforts benefit as AI optimizes resource allocation, reduces waste, and supports
eco-friendly production models (Sharma et al., 2021). Transparent AI-driven quality assurance mechanisms reinforce consumer trust, offering verifiable insights into food safety and provenance (Zatsu et al., 2024).
However, challenges persist in widespread adoption. AI systems require substantial investment in infrastructure and expertise, posing financial barriers for smaller food producers (Chhetri, 2024). Ethical concerns arise regarding data privacy and algorithmic biases, requiring strict oversight in AI application policies. Additionally, successful integration demands close collaboration between food scientists, regulatory bodies, and AI developers to refine accuracy and maintain compliance with industry standards (Erkinjon and Feruz, 2024).
The intersection of artificial intelligence and food quality assurance represents a paradigm shift in safeguarding consumer well-being (Sahni et al., 2021). By harnessing AI’s capabilities, the industry can establish more adaptive, data-driven systems that elevate food safety, sustainability, and efficiency to unprecedented levels (Sharma et al., 2021). As technology continues to advance, strategic adoption and ethical considerations will determine how AI reshapes the future of food quality assurance in the years to come (Zatsu et al., 2024).
In conclusion, the practical applications of AI in food industries are vast and growing. From contamination detection and predictive maintenance to personalized nutrition and regulatory compliance, AI is not only enhancing food safety but also redefining operational standards. Future research should focus on scalable solutions for small producers and ethical frameworks to ensure responsible AI deployment across the food sector.
References
Addanki M, Patra P & Kandra P 2022. Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied food research. 2 (2): 100126.
Chhetri KB 2024. Applications of artificial intelligence and machine learning in food quality control and safety assessment. Food engineering reviews. 16 (1): 1-21.
Emami P & Marzban A 2023. The synergy of artificial intelligence (AI) and geographic information systems (GIS) for enhanced disaster management: opportunities and challenges. Disaster medicine and public health preparedness. 17: e507.
Erkinjon R & Feruz K 2024. The role of artificial intelligence in enhancing food safety and quality control. Western European journal of modern experiments and scientific methods. 2 (5): 233-236.
Kumar I, Rawat J, Mohd N & Husain S 2021. Opportunities of artificial intelligence and machine learning in the food industry. Journal of food quality. 2021 (1): 4535567.
Marzban A, Dowlati M & Sadeghi-Nodoushan F 2023. The Effects of Climate Change on Food Security. Journal of nutrition and food security. 8 (3): 340-342.
Marzban A & Sadeghi-Nodoushan F 2025. The role of artificial intelligence in food supply chain management during natural disasters. Journal of nutrition and food security. 10 (3): 338-340.
Sahni V, Srivastava S & Khan R 2021. Modelling techniques to improve the quality of food using artificial intelligence. Journal of food quality. 2021 (1): 2140010.
Sharma S, Gahlawat VK, Rahul K, Mor RS & Malik M 2021. Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics. 5 (4): 66.
Zatsu V, et al. 2024. Revolutionizing the food industry: The transformative power of artificial intelligence-a review. Food chemistry. 24 (X): 101867.
Type of article: letter to the editor |
Subject:
public specific
Received: 2025/06/4 | Published: 2026/05/30 | ePublished: 2026/05/30
Received: 2025/06/4 | Published: 2026/05/30 | ePublished: 2026/05/30
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