AI in Food Safety
Food safety concerns, in conjunction with an overall health improvement trend, are increasing. This attention is thanks to a growing consumer emphasis on healthier eating, cleaner labels and a continued prevalence of food-related illness breakouts and food fraud.
Since 2010, produce-associated outbreaks have increased1, with the CDC estimating annual foodborne illness in around 48 million people, of which 128,000 were hospitalized and 3,000 died2. Domestic and farmed animals are also impacted.
A long-term study from the World Health Organization’s Foodborne Disease Epidemiology Reference Group prioritized 31 foodborne hazards3. These include microbes such as bacteria, viruses, parasites, and fungi, the top five being salmonella, norovirus, campylobacter spp., toxoplasma gondii, and e-coli4. As recently as September 6, 2024, 65 Americans across nine states were infected with an outbreak strain of Salmonella, with 24 hospitalized5.
In response, authorities are cracking down on consumer food safety elements, including pathogens, chemicals, and other unknown agents. For producers and manufacturers, the FDA’s January 2026 FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods will mean tighter traceability across supply chains6. With 2026 rapidly approaching, how can organizations safety test their produce and food products consistently and reliably?
Food Safety Testing in 2024
Food safety covers production and manufacture, handling, preparation, and storage, including food hygiene, additives, labeling, and pesticide residue practices. These efforts help prevent various types of food contamination, which may occur without changing the look, smell, texture or taste of the food.
Testing adheres to ISO 22000 standards. Food inspections include sample analysis for quality and contamination levels, production environments and food handling practices evaluation using qualitative and observational methods.
At the same time, with varying health accessibility and illness experienced with the presence of co-morbidities, food safety reporting is complex and multifaceted. Measurement challenges also include surveillance system disparities, diagnostic uncertainties, under-reporting, and reporting variations.
How can these error-prone elements be reduced or eliminated and food safety testing enhanced? One exciting solution is the emergence and development of digital food systems reliant on artificial intelligence (AI)-driven technologies.
These applications hold the promise of analytical tools to support improved and more efficient surveillance systems design, surveillance data interpretation with predictive analytics, and real-time monitoring for enhanced safety.
AI Applications in Food Safety
Applications such as natural language processing (NLP), machine learning (ML), and computer vision (CV) are generating significant buzz in food safety testing. How can they help?
Software such as NLP and ML grows and nurtures AI knowledge, memory, and communication skills. Computer vision enables computers to find and understand items in visuals such as lab slides, helping to automate and simplify food safety tasks. These include food safety risk monitoring, prediction, and supply chain optimization. They also incorporate public health system improvements such as early outbreak alerts, source attribution, and pathogen detection, identification, and characterization.
AI can aid producers and manufacturers in spotting weaknesses and risks and formulating and administering corrective measures. It could help accelerate the speed of intervention to reduce or prevent contamination and outbreaks. Early detection could also support superior food quality and assurances and, over time, help reduce food waste.
AI has dramatic potential to contribute to public health. However, the development of AI-powered technology in food safety lags, even in regions with sophisticated surveillance systems, such as the USA, the UK, and Europe.
Barriers include minimal collaboration and data sharing across research efforts, a lack of data standardization and strict privacy protection methods. Instead, a collaborative ecosystem will help drive innovation in AI food safety application and development. Meanwhile, you can help ensure ingredient purity by relying on safe, EU-certified flavor and odor extracts from Advanced Biotech.
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1 https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.02667/full
2 https://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html
3 https://en.wikipedia.org/wiki/Food_safety#cite_note-4
4 https://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html
5 https://www.fda.gov/food/outbreaks-foodborne-illness/outbreak-investigation-salmonella-eggs-sept-2024
6 https://info.markem-imaje.com/what-is-FSMA