Canada Spends Logo

Explore our Budget 2026 analysis

Grant: Applied Research and Development Grants - Level 1

Algonquin College of Applied Arts and Technology

$25,000.00

Development of a platform for precise color identification

FY 2011-12

Summary
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.
Program
Applied Research and Development Grants - Level 1
Location
Ontario, Canada
Recipient Type
Other
Type
Grant
Source
NSERC's Awards Data

As published

Application id
424108-2011
Name nom
BrundelRe, Riccardo
Department département
Head Office
Organization id
2324
Institution établissement
Algonquin College of Applied Arts and Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2011
Competition year année de concours
2011
Award amount
25000
Program id
CARD1
Program nane en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
515
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Instrumentation technology
Area of application fr
Technologie de l'instrumentation
Research subject code
2603
Research subject group en
Robotics
Research subject group fr
Robotique
Research subject en
Computer vision
Research subject fr
Vision artificielle
Partie
1
Nb partie
1
Application title
Development of a platform for precise color identification
Keyword
NSERC
Application summary
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.