Inspection Systems - Don't Settle for a Single Point of View

Food Engineering's Wayne Labs explores why food processors shouldn’t rely on vision inspection alone, and how AI and machine learning are expanding what inspection systems can detect alongside X-ray, NIR/hyperspectral and metal detection.

Courtney Crichlow, Project Manager at KPM Analytics, contributed key insights to the article, explaining that traditional inspection criteria were shaped by what human quality teams could realistically spot-check, and that much of today’s equipment is still built to those historical standards rather than to reduce food waste, improve quality and prevent foreign material incidents. She also addresses a common misconception that AI vision models deliver value right out of the box, noting that AI-based systems need substantially larger initial data sets to learn product variability and the specific characteristics of the foreign materials being targeted. The article also features KPM Analytics imagery showing how a well-trained AI/ML system finds low-density foreign materials, such as plastic in chicken and a cotton glove in beef trim, that X-ray and metal detectors often miss.

Read the article from Food Engineering