Preventing foreign object contamination is a growing priority for food processors. According to USDA, it accounted for more than 75% of the total volume of food recalled by the Food Safety and Inspection Service in 2019. Contamination isn’t a novel issue, however, and many processors are looking for new and innovative solutions to help automate detection and increase the likelihood of finding foreign materials. One reason for this trend is that materials such as plastics and rubber are showing up with greater frequency, and these materials are often missed by metal detectors and X-rays.
Improved detection of foreign contaminants will help reduce food waste as well as lower costs and the risk of recalls. Many processors look to identify contaminants early so they can address an issue quickly and minimize the impact on production.
The good news is that detection technology is evolving quickly. Vision-based systems are a good example of growing innovation in the processing sector. But what exactly do we mean when we talk about vision systems for food processing?
While X-rays and metal detectors are commonplace in processing, vision systems are relatively new. “Vision system” is an umbrella term for a number of different systems with widely varying capabilities and characteristics. In this article, we will compare different vision systems in terms of how they function, their specific attributes, and how they may benefit food processors.
The Science of Seeing
To understand the differences among types of vision systems, it’s useful to remember how light works—the science behind how we see things.
Our eyes are only able to see three color bands: red, green, and blue, otherwise known as the visible spectrum. However, light is actually made up of thousands of different wavelengths. Each wavelength behaves differently and interacts differently with various materials. We can use these diverse wavelengths of light, both inside and outside of the visible spectrum, to gather information about different materials or objects.
When it comes to comparing vision systems, there are three main differences to consider:
- The number of light bands, i.e., the number of colors that a system is able to see (otherwise known as wavelengths);
- The spectral resolution—the higher the resolution, the smaller the ‘gaps’ between each color or wavelength; and
- The amount of information a vision system is able to see per pixel of an image. A pixel is the smallest unit of information that makes up a picture.
Together, these three characteristics define the level of detail a vision system is able to consider, the ability of that system to detect a variety of different materials, and how “trainable” a system is, i.e., whether it can learn from the information it’s gathering.
The Art of Looking
The original vision systems are our eyes. Human inspectors are frequently brought in or added when there has been a contamination event. Studies from other industries have shown, however, that after just 15 minutes on an inspection task, human performance drops dramatically. After 30 minutes on a task, the probability of detection falls by more than 50% on average, meaning that inspectors have a one in two chance of missing the materials they’ve been hired to find.
This can be due to multiple factors, including line speed, levels of training or experience, fatigue or illness, and even external factors such as background noise or lighting conditions. Studies in other industries have shown that simply adding more inspectors does not necessarily increase detection rates.
Automation of repetitive tasks—such as inspection—delivers better and more consistent outcomes. It also frees up valuable staff for more important and, often, safer tasks that require human expertise.
Camera-Based Inspection Systems
Camera-based systems are the most well-understood type of vision system. Cameras have been around for more than a century, and most of us carry one in our pocket at all times. Camera-based inspection systems are the closest in performance to the human eye, which means that they will only see objects within the three colors of the visible spectrum. Their advantage over human inspectors can be greater consistency; they don’t get tired or lose concentration. However, cameras are not effective in detecting contaminants when there is little contrast between the object being inspected and the material they are looking for—for example, white plastic on a fatty piece of chicken or on ground pork trim.