AI takes food safety beyond the human eye

Article By Keith Loria Published October 9, 2025
Article Source: https://www.meatpoultry.com/articles/32591-ai-takes-food-safety-beyond-the-human-eye

Artificial intelligence (AI) is transforming food safety for meat and poultry processors, helping them move from reactive inspections to proactive prevention. With everything from real-time monitoring of production lines to predictive analysis that flags potential contamination risks, AI tools are enabling faster, more accurate decision-making while streamlining compliance with strict regulatory standards.

Bryan Quoc Le, PhD, founder and principal of Mendocino Food Consulting in Mendocino, Calif., noted the food safety management system has already transitioned from merely inspecting the final product to controlling and preventing risks at all stages of the food chain farm to fork.

He explained spectroscopy, smart sensors and computer vision systems are now integrated into processing and analyzing contaminants in real time. These tools can identify adulteration, bruising, ripeness, infections and even subtle quality changes that are often invisible to the human eye.

“The limitations of manual detection and testing have also become increasingly evident,” he said. “But with the development and integration of AI technology in monitoring systems, limitations such as delayed detection, narrow sampling range and difficulty in on-site and real-time testing have been progressively addressed.”

For instance, automation has improved the pathogen identification processes, in which machine learning (ML) plays a vital role. ML employs AI-biosensing, prediction models and data visualization, which exceeds the traditional detection in terms of speed and accuracy of analyzing even larger datasets.

“One example is early outbreak signals can be detected using Natural Language Processing (NLP) models that analyze data from a wide range of sources, including scientific journals, regulatory reports and social media,” Le said. “Deep neural networks, meanwhile, can process genomic data to identify pathogens and determine their resistance patterns.”

Steve Burton, founder and chief executive officer of Icicle Technologies, whose clients include Fraser Valley Specialty Poultry, Country Prime Meats and Made Rite Meats, noted many meat and poultry processors face a significant labor shortage, making it increasingly difficult to staff critical quality control and food safety roles.

“This gap strains existing teams, increases the risk of human error, and limits the time QC staff can spend on proactive safety measures,” he said. “AI and automation offer a path forward — reducing manual workload, enabling faster decision-making, and allowing smaller teams to have a bigger operational impact.”

Luke Qian, PhD, who works in the department of food science at Cornell University, noted at present, the most widely used AI application is computer-vision inspection, which could enable facilities to scan products in real time — spotting defects, contaminants or anomalies better than manual checks do.

“This technology is beginning to deliver measurable improvements in defect detection and recall reduction, though successful deployment still depends heavily on careful calibration and integration within existing operations,” he said. “A few forward-looking operations (e.g., Tyson, Chick-fil-A) are beginning to experiment with predictive analytics to flag patterns that may precede contamination — for example, tracking environmental trends or production shifts. However, this is still on the experimental side, and its effectiveness in reducing recalls or incidents has yet to be broadly validated.”

Golan Haiem, founder and CEO of Los Angeles-based Destination Wagyu noted another area AI is making an impact is in environmental monitoring.

“Sensors in processing and cold storage facilities continuously feed data into AI models that can detect temperature or humidity deviations before they become safety risks,” he said. “This helps prevent issues like bacterial growth and ensures the beef remains safe and fresh throughout the supply chain.”

Data-Driven Approach

Some processors are even using AI to predict food safety risks and have shown strong potential in improving the existing traditional methods of analysis.

“It can analyze massive datasets and build predictive models out of the patterns it has identified for creating proactive and scalable risk prevention measures,” Le said. “However, its effectiveness still depends on the quality and availability of data.”

Joe Heinzelmann, vice president of food safety digital solutions for Lansing, Mich.-based Neogen Corp., noted automation of repetitive physical and documentation tasks is the first step in digitalization, allowing food safety professionals to focus on underserved areas and impactful projects.

“As these systems run, they generate structured, high-quality data that can be analyzed to spot trends, benchmark performance and guide decision-making, turning operational improvements into a truly data-driven food safety program,” he said.

While things are still in the early stages for AI classification and prediction, Heinzelmann noted technologies exist that can take general properties and processes for a food product and make hazard recommendations, and they are making progress in assessing risks.

“While classification and prediction still require significant human input, AI models and classification schemes are increasingly helping companies identify emerging risks from trend data,” he said. “By centralizing and standardizing data — as Neogen Analytics does for environmental monitoring — processors can clearly see where risks are not being managed and apply targeted interventions. This data-driven approach allows teams to prioritize actions that deliver the greatest impact on food safety outcomes.”

Regulatory Compliance

In large-scale meat and poultry operations, AI-driven systems support regulatory compliance and audits by analyzing images and environmental data, such as temperature and humidity, to help predict the shelf life of meat products. Some AI integrations also enhance traceability and fraud detection across the supply chain.

Some early AI-driven systems can even automate tasks and recommend policy-specific actions, supporting compliance execution and digitalization.

“When compliance activities are digitized, the resulting data creates a real-time, auditable trail,” Heinzelmann said. “This not only makes regulatory inspections more efficient but also enables proactive, data-driven analysis to identify patterns, address gaps before they become violations and strengthen long-term compliance performance.”

Overcoming Challenges

Although AI has greatly advanced food safety risk management, it still faces significant challenges such as data scarcity, high costs and regulatory hurdles.

“In some regions, the lack of sensors, stable power supply and internet access limits the collection of high-quality, consistent data,” Le said. “This scarcity makes AI training difficult, as long-term, standardized validation with real-world data is often absent, reducing the system’s pattern recognition and predictive accuracy.”

High costs for hardware and software also make AI adoption challenging for smaller companies and business operators. Another factor is that low stakeholder confidence regarding privacy and data protection remains a barrier to companies fully embracing these technologies, including meat and poultry processors.

“By establishing a consistent architecture, companies can integrate AI tools that deliver measurable ROI,” Heinzelmann said. “Whether starting with environmental monitoring or supplier management, the ultimate goal is building a scalable, data-driven foundation for food safety decision-making.”

The Future is Bright

The industry’s current data volume limits predictive power. But as more standardized, quality data is captured, predictive and automation systems are expected to fill the gap.

“Success will depend on applying AI in ways that deliver actionable insights from that data,” Heinzelmann said. “While we are not yet at the point of predicting the exact location of the next pathogen positive, we are moving toward a future where data-driven recommendations guide prevention, prioritization and measurable risk reduction.”

Qian said in the next three to five years, large companies — with robust digital infrastructure — are likely to integrate AI tools like vision inspection and predictive analytics into plant networks and enterprise data platforms, while smaller processors will focus on plug-and-play or vendor-hosted solutions like portable vision systems that improve detection accuracy and decision-making without requiring major IT investments.