
Regal Packaging Services Is Your Total Inspection Service Provider
July 24, 2026
IS THERE ANY USE FOR AI IN METAL DETECTION?
Masayoshi Son (founder and CEO of SoftBank Group) once said, “I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.”
There are, of course, a plethora of opinions about the value and uses of AI, but the fact remains, nonetheless, that Artificial Intelligence is altering the way we do almost everything, and that includes metal detection in the food industry. There is a move from conventional systems to techniques capable of adapting, absorbing and translating information to appreciably reduce false rejects and enhance detection sensitivity for smaller contaminants. AI enriches the ability to detect metal particles in demanding, high-conductivity products (like cheese or meat) and through metallic packaging. For businesses pursuing quality assurance at a higher level, the latest in AI technology delivers a host of innovations.
Key advancements and applications of AI in food metal detection include:
-
Enhanced Metal Detection Capabilities
- Reduced “Product Effect”: Traditional detectors have long been the backbone of food safety but can sometimes struggle with “product effect” – where wet, salty, or conductive food appears as a metal contaminant. AI learns to differentiate between the signal created by the product (analyzing product “fingerprints” so to speak) and actual metal, reducing false positives.
- Smaller Contaminant Detection: AI algorithms are capable of identifying smaller, subtler metal fragments than conventional equipment, increasing sensitivity by utilizing multiple frequencies simultaneously.
- Metallic Packaging Detection: AI enables reliable inspection even when food is packaged in aluminum or metallized film, which previously caused significant interference.
- Automating and Enhancing Audit Checks: Hourly or other scheduled verification checks are an integral part of food safety compliance, but they can be tedious and subject to human error. AI-driven systems automate these tests, guiding operators step-by-step and logging results automatically. This streamlines the process and guarantees more accurate, consistent record keeping for regulatory audits.
-
Operational Efficiencies
- Fewer False Positives/Rejects: By correctly recognizing contaminants, AI limits the unwanted rejection of safe food, reducing waste and saving time and money.
- Real-time Adaptation: AI-powered systems continuously learn and fine-tune the process, adjusting to new product variations (e.g., changes in dryness or temperature) without needing constant labor-intensive re-calibration.
- Predictive Maintenance: AI proctors equipment health in real-time, examining and analyzing data such as signal strength and energy usage to forecast potential breakdowns before they happen, thus circumventing unplanned downtime.
- Operational Proficiency: “To assist … food processors, Fortress is already using its own proprietary data software package, Contact 4.0, across its metal detection, X-ray and checkweighing technologies,” said Eric Garr, regional sales manager, Fortress Technology. “This enables processors to review, collect data and securely oversee the performance of multiple Fortress metal detectors, checkweighers or combination inspection machines connected on the same network.”
-
Key Technologies and AI Systems
- Machine Learning (ML) Models: These are trained on massive datasets of images or signal patterns (with and without contaminants) to learn what “good” product looks like, allowing the system to reveal and flag nonconformities.
- Vision AI: In addition to magnetic detection, Vision AI utilizes cameras to recognize surface-level contaminants like hard plastic, wood, or glass that metal detectors would otherwise miss. Cutting-edge image analysis driven by deep learning can identify these non-metallic contaminants that traditional systems might miss. By dissecting X-ray images at a granular level, AI can discover irregularities that would otherwise slip through the cracks. Each AI model is an collection of many images of appropriate and objectionable products and substances.
- Smart Checkpoints: Modern systems (e.g., from Fortress Technology rep Regal Packaging Services) feature automated testing (like HALO) that verifies system sensitivity without requiring a human to insert test pieces.
- Microwave Technology: This is typically used to detect low density foreign materials like wood, soft plastics or fruit stone fragments in homogeneous liquid and semi-liquid products like sauces and purees. Antennas are situated around the product, so the microwaves infiltrate and inspect the entire product. Variations in the electric field reveal contaminants, and when united with Artificial Intelligence (AI) algorithms, these technologies can identify which type of contaminant is present using 3D reconstruction. Microwave is less energy intensive and does not heat the product, which helps preserve finished product integrity.
-
Benefits for Food Manufacturers
- Improved Safety: Enhanced detection of ferrous, non-ferrous, and stainless-steel contaminants protects consumers.
- Reduced Recalls: Increased accuracy reduces the likelihood of contaminated products reaching the market.
- Compliance: AI systems help meet stringent HACCP, IFS, BRC, and FDA standards.
While AI is a powerful tool, experts note that it is not a complete replacement for human oversight but rather a tool to augment it, requiring high-quality training data to be effective. Humans will always need to oversee the training, competency and outcomes of AI assisted metal detection. And humans, of course, will always bear the responsibility for the results.
Material taken, in part, from:
https://oxmaint.com/industries/fmcg/ai-foreign-object-detection-food-manufacturing
https://www.checkfirst.ai/blog/ai-workflow-automation-for-certification-leaders
https://fortresstechnology.com/future-proofing-food-production-with-all-in-one-inspection/















































