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Thermal Imaging Generates Predictive Data to Reduce Waste

Yoran Imaging’s Process Analytical Monitoring system measures residual heat from sealing applications to predict problems on the line before they occur, leading to lower costs and increased food safety.

Food safety inspection thermal imaging Yoran
Yoran Imaging’s smartphone-sized camera can be placed on the line to oversee food processing or packaging, and data generated by the camera’s thermal imaging is transferred wirelessly to this portable cabinet monitor, where end users can see real-time analytics.
Yoran Imaging

Inspection is a foundational safeguard in food processing and packaging to ensure products are free of contaminants before they’re sold to consumers. While X-ray and metal detection can reveal whether a package contains harmful materials, those technologies can’t determine whether each individual package on the line has been sealed correctly to maintain the integrity of the product and keep out potential pathogens.

That’s a gap that Israel-based Yoran Imaging plans to fill with its Process Analytical Monitoring (PAM) system. At the core of PAM’s capabilities is a proprietary thermal imaging inspection and data capture system. The thermal imaging technology, according to Eran Sinbar, co-founder and CEO at Yoran Imaging, was originally developed as night vision for the Israeli military. That technology is now transferred to the CPG manufacturing industry and can determine whether the seal on a food or beverage package has been applied properly by instantly analyzing residual heat from the sealing application.       

 “We understood there was a huge opportunity [in CPG] since night vision is based on visualizing thermal radiation,” says Sinbar. “When heat sealing is applied to a package, it generates a heat pattern that is not visible to a [traditional] vision inspection system. But [PAM] can reveal it and can show the pattern so we can learn and understand the process.”


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The thermal imaging data from PAM’s smartphone-sized camera overseeing the line is transferred wirelessly to a portable cabinet monitor, where an end user can see where an alignment might need to be made to a sealing machine if the data shows any problems. Sinbar says PAM’s built-in AI can also help manufacturers predict failure before it occurs on the line, potentially saving enormous amounts of material and resources in the process.

“[PAM] can predict and prevent failure and this is one of our huge advantages,” Sinbar says. “You don't even have to reach a point where you have failure. This is why we can dramatically reduce waste and customer complaints and enable manufacturers to work at a very high capacity.”

Another goal for Yoran Imaging with PAM is to eliminate manual sampling methods from CPG manufacturing, which Sinbar says is an antiquated strategy. “Manual sampling is somebody pulling a product randomly off the line and expecting the condition of that one package to be representative of everything in that batch. For example, if it’s a potato chip bag, they put it in a bucket of water to see if air bubbles appear, which would mean it’s not sealed properly. I would say that’s primitive,” Sinbar says, adding that manual sampling also doesn’t generate practical data to apply to an operation like PAM does.

Humidity detection

Because PAM’s wireless camera is small enough to be mounted anywhere on the line, it can also be moved upstream into food processing, Sinbar says. In particular, PAM’s thermal imaging technology can detect and measure humidity and moisture in a product prior to being packaged.

“Let’s say potato chips have been cooked and are moving into being packed and sealed in pouches. [PAM] can monitor those chips to ensure they’re the right humidity level,” Sinbar says. “If they’re too warm, they will lose their crispness in the bag, and any excess moisture from that heat could potentially create bacteria in the bag too.” Nestlé in Switzerland is one of Yoran Imaging’s customers that purchased PAM to monitor the humidity and moisture levels of its products, he adds.

Eran Sinbar Yoran Imaging‘We assume that future production lines will be touchless, zero contact, and zero waste, so new equipment that comes with our system inside would be a closed-loop digital system,’ says Eran Sinbar, co-founder and CEO at Yoran Imaging.Yoran Imaging

Sustainable packaging is another area where PAM can offer advantages for manufacturers. “A sustainable milk carton, for example, is smaller today to save material and align with zero waste initiatives,” Sinbar says. “The carton is also thinner because it might have monolayer instead of multilayer materials. When you put that together, it becomes a challenge if you aren’t monitoring your sealing process because there’s less room for error.”

The reduced use of preservatives in food is also an opportunity to use PAM as a safeguard against packaging defects. “Some processors don’t add preservatives to meat or cheese anymore,” he notes. “Instead, they put inert gas in the packaging to keep the food fresh. If that inert gas leaks through a faulty heat seal, the food can spoil without preservatives. PAM can monitor and catch this before it happens.”

In addition to Nestlé, other notable customers of Yoran Imaging include Tetra Pak and Colgate, and many major snack and coffee companies in Israel. Sinbar says PAM has the most installations in Israel and is just getting started in Europe and the U.S., with its eye on expanding further into both of those markets this year.  

Additional plans for Yoran Imaging include working with OEMs to integrate PAM as a component inside processing and packaging machines. “We assume that future production lines will be touchless, zero contact, and zero waste, so new equipment that comes with our system inside would be a closed-loop digital system,” Sinbar explains. “We’re also looking at how to bring data from other equipment like checkweighers, X-rays, and metal detectors, and combine it all under our [data] umbrella. There may be small SMEs out there that might like us to handle all their digital controls.”

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