Foreign material contamination is a serious risk for food manufacturers and ingredient suppliers. Foreign materials that find their way into food products will cause consumer complaints and damage brand reputation at best, and be a choking hazard at worst, also potentially causing injuries to the teeth or the digestive tract.

Manufacturers can minimize this risk by preventing contamination from within the plant, for example with regular equipment maintenance, or by removing foreign materials that are inadvertently collected from the field when produce is harvested. Despite preventive measures, however, foreign materials can contaminate food products. That’s where inspection technology comes in.

In this article we’ll give an overview of the main foreign contaminants, how X-rays and metal detectors—the two most important foreign material detection technologies— work, their strengths and challenges, and the current trends in foreign material contamination.

Types of Foreign Contaminants Found in Food Manufacturing

Foreign materials can be divided into two broad categories: metallic and non-metallic. Metallic foreign contaminants are the ones most commonly found in food and other consumer products. Examples are shards, nuts, screws, beads or bolts from production equipment, buckshot or needles (especially in meat), hair pins, wires, or fragments of metal blades. It’s important to prevent and detect contamination with metal objects as soon as it happens, because not only are they a hazard for consumers, but they can also damage equipment, causing lengthy unplanned downtime.

Contamination with non-metal objects is less common but it’s on the rise. Common, non-metal contaminants are bones in processed meat, rubber from equipment and utensils, glass from product containers, or wood from pallets and utensils. Non-metal contaminants are not detectable by metal detectors, and some of them can be challenging to find with food X-ray equipment.

Principles of Metal Detection in Food Inspection Systems

In a metal detector for a food production line, products pass through a magnetic field generated by transmitter coils. A metal object inside a product will disrupt the signal. This disruption is identified by the receiver coils and analyzed. Products above the reject threshold are flagged as positives and rejected.

The performance of a metal detector can be influenced by several factors, first of all, the type of metal. There are two properties of metal objects that cause great disturbances to the magnetic field, making them easier to detect: conductivity and the ability to become magnetized when exposed to a magnetic field.

Ferrous metals, such as iron and carbon steel are easier to detect because they have both strong magnetic properties and moderate to high electrical conductivity. Non- ferrous metals don’t have strong magnetic properties, so their detectability depends on how conductive they are. For example, silver, copper and aluminum have high conductivity and are easier to detect than stainless steel, which has low conductivity. To detect both types of metals and minimize false positives, dual-frequency food metal detectors use two bands simultaneously: lower frequencies for ferrous metals, and higher frequencies for non-ferrous metals.

There are other factors that can affect performance in metal detectors.

Contaminant orientation: the signal level generated by a contaminant is proportional to the surface area as it enters the field and to its surface area overall. A thin rectangular-shaped piece of metal will generate a much stronger distortion if it’s oriented vertically. A similar object with the same orientation but smaller surface will cause a smaller signal.

Product properties: the presence of iron (in meat), salt, or temperature variations can create disturbances and false positives. This is known as the product effect. It can be minimized by calibrating the frequencies of the metal detector based on the inspected product and making sure that size, shape, orientation and temperature remain constant.

Tunnel aperture: in general, having coils closer to each other improves detection, although highly reactive products sometimes perform better with oversize apertures.

Metal detectors for the food industry protect from all metal contaminants except stainless steel, which has almost no magnetic properties. To be detected, it needs a high frequency, which can affect performance on other metals. Metal detectors are very effective against aluminum too, although they might give false positives with metalized film or foil packaging.

How X-Ray Inspection Systems Detect Contaminants in Packaged Food Products

In an X-ray machine for the food industry, a tube on one side of the tunnel generates a short-wavelength X-ray beam, which is detected by a sensor at the opposing side.

As the product passes through the tunnel, it absorbs part of the beam depending on its density (atoms per unit volume), while a processor converts that difference in energy into a grayscale image. A non-contaminated product, (e.g. a loaf of bread) will generate an image with a uniform color. As most contaminants have a higher density than products (for example, metals are eight times as dense as meat) they will absorb more energy and appear as darker spots in X-ray images.

The density difference between product and contaminant is not the only factor influencing detection. Another one is the thickness of the foreign object: a smaller metal fragment will be harder to detect than a larger one. Product thickness also plays a role: a foreign object will be harder to detect in thicker products than it would be in less thick ones.

Based on that, X-ray systems are very effective with high-density ferrous, non-ferrous, and stainless-steel metals. Mid-range density materials such as aluminum, glass, stones and calcified bones are also detectable. On top of that, X-rays are ideal for inspecting products in metalized film or foil packaging, which would generate false positives in metal detectors, and won't be affected by products’ temperature or salt content.

With low-density contaminants that have the same density as the base food product, such as wood, cartilage (which lacks calcium), bugs, common plastics, glass fragments in glass containers, detection performance will depend on the size of the foreign object and the inspected product.

Why Food Manufacturers Should Invest in Contaminant Detection Equipment

Adding foreign material detection to your production line provides three important benefits:

Minimizing risk. X-ray systems and metal detectors are essential for minimizing risk in any HACCP plan. They are a reliable safety net for preventing product recalls, which cost food manufacturers millions in lost products, production downtime, crisis management, legal fees, and brand reputation.

Complying with government regulations. There is no ‘safe’ size for foreign contaminants. Even the smallest fragment in a food product might be considered as a potential cause of traumatic injury by the FDA and subject to a product recall.

Improve Quality Assurance. Investing in foreign object detection will give you the opportunity to validate the efficacy of your quality and safety programs, reduce product loss, and minimize production downtime.

How to Select the Right Contaminant Detection Solution: A 5-Step Framework

1) Identify your target contaminants. Conduct a risk assessment and identify the most likely foreign contaminants (e.g. metal, non-metal, glass, bones, etc.) that might enter your production line.

2) Assess your product properties. The right detection solution will also depend on the product’s properties: size, density, shape, line speed, temperature, processing seps and type of packaging.

3) Determine which inspection technology to use. Wherever possible, we recommend using both the metal detectors and X-ray machines on a production line. There might be instances where a metal detector will find matter that an X-ray system cannot. Examples are clusters of metal particles that are typically too small for an X-ray machine to locate, and thinner material, including aluminum foil.

4) Find the best line placement. Identify the best point in the production line to place the detection equipment. Typically, that would be at the filling step, after primary packaging, or after secondary packaging.

5) Add a rejection system. A detection solution wouldn't be complete without a rejector that removes faulty products from the production line. The type of mechanism will depend on the product. For example: a flipper rejector for boxes and thick bags or a dropout rejector for thin bags.

Evolving Trends and Advanced Technologies in Foreign Material Detection

Although the basic principles of metal detection and X-rays were invented decades ago, inspection equipment has continued to evolve. The improvement of algorithms and hardware components have significantly increased detection sensitivity and reduced false positives. One of the latest developments in X-ray inspection are dual energy machines, which use two different X-ray energies to detect materials of varying densities.

Two global trends in particular are pushing manufacturers of inspection technology like Anritsu to constantly evolve and improve.

Automation. While automation in food processing allows manufacturers to streamline production and compensate for labor shortages, more equipment and a smaller labor force means higher probability of machine components falling into food during processing and fewer visual checks to spot them. Using effective detection systems has become crucial to keeping that risk under control.

Globalization. In a supply chain where food products and ingredients are processed and shipped across the world, international brands are increasingly designing foreign object detection solutions as part of a global strategy, rather than on a facility basis.

Along with X-ray systems and metal detectors, there are other emerging technologies that food manufacturers can use to ensure foreign contaminant-free products.

1. Hyperspectral imaging allows for precise identification of foreign objects based on their specific light absorption and reflection properties.

2. Vision systems inspect food products and detect surface-level contaminants and defects by using cameras and image processing software.

3. Near-Infrared (NIR) uses near-infrared light to analyze the composition of products and distinguish between organic (food) and inorganic (foreign contaminant) materials.

4. Ultrasonic systems detect foreign objects within food by looking at how sound waves travel through different materials.

5. Thermal imaging uses infrared cameras to detect foreign materials based on their thermal properties.