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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.

Back to Basics: Understanding Conveyors for Food Processing
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Back to Basics: Understanding Conveyors for Food Processing