Development of a platform for precise color identification

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Food/agriculture sector is the world's biggest industry comprising 10% of global GDP, or ~$5 trillion. Food inspection is a ultimillion $ growing industry especially with escalating concerns regarding the global food safety and quality testing. Color and appearance of products such as food have a deep influence on consumer preferences and choices and form a basis for his/her buying decision based on quality and safety of the product. At the same time, color perception varies among ndividuals and also depends on illumination. Consequently, there is a need for fast, continuous monitoring of food color during production while being able to objectively quantify its color in order to be able to reject in real-time the lower quality products. Sightline Process Control is in the business of manufacturing food inspection systems. The company has a strong market hare (>40% in USA alone) and have customers such as McDonald's. However in order to keep ahead of the market and dapt to evolving client needs the company must add additional capabilities to it's products e.g. to precisely determine food color. The present collaborative project between Algonquin College and Sightline Process Control Inc. will develop a atform/methodology enabling the company manufactured food inspection systems to determine the food color independent n specific system, illumination and other conditions. This will be achieved by "weaving in" color recognition algorithms/software developed by Algonquin College with the existing software for size/shape characterization in the Sightline food inspection ystems, running tests for stability, repeatability and consistency in field/industrial conditions and debugging the problems while acquiring statistical data on the platform performance. The developed platform will help the company to extend the apabilities of its food inspection systems into a new dimension on the road to implementation of state-of-art quality control systems.