Eyes That Never Blink: How AI Is Transforming Food Inspection and Safety

There is a common saying about food safety and quality: “Many eyes are better than two.” This saying refers to a single quality assurance technician performing line inspections, as opposed to the entire line of workers keeping a trained eye on food safety and quality.

But what if that number of eyes could be 1,000, 10,000, or “eyes that never blink?” Recent advances in digital optics that incorporate robust AI algorithms are achieving exactly that.

AI has been a firestorm, sweeping through almost every aspect of our day-to-day, and the same can be said for food manufacturing. AI may be the new tool that will allow almost any product to finally achieve 100% inspection, reducing defects and failures at the consumer level. So too, we are seeing incredible advancements in AI-driven food manufacturing, which, when fully developed and capitalized, will most certainly reset the bar for what consumers consider a “high-quality” product.

Advancements are infiltrating many aspects of daily life for food safety and quality professionals. This is leading to some very exciting initiatives to prevent deviations from specification and enhance manufacturers’ ability to ward off potential food safety problems.

There are many advancements hitting the marketplace, but some worth mentioning are in the following areas.

Vision Systems

Some of the most common quality complaints include missing labels, misaligned labels, missing or illegible code dates, the wrong label for the product, seal failures, and other packaging defects. New optical cameras with AI technology can automatically detect and remove these types of defects well before the consumer purchases them. Many systems use multipoint inspection, like the one pictured below, and can view a package from a 360° perspective. When defined parameters are exceeded, the product is automatically removed from the line. 

These systems can even detect various foreign materials, such as hard and soft plastics, wood, and other materials, based on their optical signatures.

Metal Detection and X-Ray Systems

Metal detectors are common in most food operations where knives and blades are used for protein harvest or when grinding protein-based products. They are used as a food safety measure for foreign materials that can cause harm when ingested. X-ray systems are often used for harder types of foreign materials, such as bones, cartilage, glass, and metal. In the past, this equipment was only as useful as the number of false positives it would produce during a production shift.

Now, with advances in AI algorithms, these smart detectors can discern multiple types of materials in pieces, much like the technology used to screen luggage at the airport does, in multiple layers or overlapping placement. This ensures foreign material is detected accurately and distinguishes between various types and the number of foreign objects in a product.  This will allow for early detection and root cause prevention

Hyperspectral Technology

This is a fascinating new field that will likely revolutionize food safety. These systems utilize spectroscopic measurements and ultra-sensitive cameras to detect images at the pixel level, across very narrow wavelength ranges within spectral bands, providing a 3D view of the test sample. It’s like incorporating a microscope and a microbiologist at the same time. This type of testing is very rapid and does not use any chemicals, making it very environmentally friendly. The system is literally looking for live organisms in a sample and can even quantify them, so it is very accurate.

Predictive Microbial Risk Modeling

AI systems analyze historical data, such as environmental monitoring results, sanitation logs, and process data, to predict where microbial contamination is most likely to occur in a facility. These predictive systems help companies prevent contamination rather than simply responding to it. By being predictive rather than reactive, food safety and sanitation professionals can reduce sanitation failures, line downtime, and prevent recalls. These models could also be used to validate environmental testing programs and food safety plans.

One additional integration for predictive modeling and food safety inspection is that it monitors employees' adherence to good manufacturing practices. AI is now being used to ensure employees do not use utensils that fall on the floor, use soiled equipment, or forget to wash their hands when working with food.

Enhanced Grading and Sorting Systems

By using optical cameras integrated with smart AI, machines can now be taught to recognize correct shapes and colors, and previously subjective quality grades can be measured objectively. If you have a baked kibble product, for example, how dark is too dark?

As quality professionals, we used to create picture-based scales to train employees on color and shape, but these tools were only as good as the original picture-taker and the printer you had to display the grading. Now, camera technology has gotten so good that a color scale and a given shape template can be programmed to ensure each piece of treat or kibble is within specification. This uniformity will lead to higher yields and fewer packaging issues, as the product itself will be more consistent.

Process Control Monitoring for Food Safety and Processing Parameters

Examples include thermal process schedule reviews, critical control point (CCP) and process control point (PCP) monitoring, and temperature and weight monitoring. One limitation of inspection is that you may only be able to perform it at a limited frequency, say, once per hour.

Almost anything that can be continuously measured can now be enhanced with AI logic to provide continuous, real-time monitoring of all your critical or process control points. This provides tons of data, and as root causes are identified and logged for a deviation or failure, your AI system will begin to predict useful information, such as, “Your average temperature deviation is every 41 days and has been assigned to a sensor failure. You should add it to the preventive maintenance list to change or check every 40 days.” Real-time monitoring also increases release time and does not need a formal pre-shipment review.

Unblinking Eyes: The Future of Food Safety

The creativity and uses for AI in food safety and quality systems are only now being realized. The market for equipment and use is only in its infancy. As a food safety and quality professional, I am excited to see how more advanced technology, like these systems that never get fatigued and have “eyes that never blink,” is implemented.

If you share my enthusiasm or have a compelling use case from your own operation, please share it in the comments. The future of food safety will be written not just by the technology itself, but by the professionals bold enough to implement it.

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About the Author

August Konie has been a Food Safety, Quality and Regulatory Professional for over 30 years. He was worked in many sectors of the food industry including fisheries, beverages, poultry, pork and pet food, under both FDA and USDA regulatory oversight. As an active committee member in various trade organization for food and pet food organizations, he was successful of implementing new regulatory guidance. He has worked with various teams across Asian, Europe, North and South American on various food safety, quality and import/export concerns. He currently serves as the Principal of BSM Assurance overseeing FSQAR activities at BSM Partners.