Computer vision-based analysis of foods: a non-destructive colour measurement tool to monitor quality and safety.

Computer vision-based image analysis has been widely used in food industry to monitor food quality. It allows low-cost and non-contact measurements of colour to be performed. In this paper, two computer vision-based image analysis approaches are discussed to extract mean colour or featured colour information from the digital images of foods. These types of information may be of particular importance as colour indicates certain chemical changes or physical properties in foods. As exemplified here, the mean CIE a* value or browning ratio determined by means of computer vision-based image analysis algorithms can be correlated with acrylamide content of potato chips or cookies. Or, porosity index as an important physical property of breadcrumb can be calculated easily. In this respect, computer vision-based image analysis provides a useful tool for automatic inspection of food products in a manufacturing line, and it can be actively involved in the decision-making process where rapid quality/safety evaluation is needed.

[1]  Di Wu,et al.  Colour measurements by computer vision for food quality control – A review , 2013 .

[2]  Jorge Chanona-Pérez,et al.  Evaluation of Image Analysis Tools for Characterization of Sweet Bread Crumb Structure , 2012, Food and Bioprocess Technology.

[3]  Carmen Socaciu,et al.  Instruments to Analyze Food Colors , 2008 .

[4]  V. Fogliano,et al.  Development of functional bread containing nanoencapsulated omega-3 fatty acids , 2011 .

[5]  Burçe Ataç Mogol,et al.  ORIGINAL ARTICLE: Computer vision-based image analysis for rapid detection of acrylamide in heated foods: Rapid detection of acrylamide , 2010 .

[6]  Francis Butler,et al.  A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis , 2006 .

[7]  Kit L. Yam,et al.  A simple digital imaging method for measuring and analyzing color of food surfaces , 2004 .

[8]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[9]  Franco Pedreschi,et al.  Color changes and acrylamide formation in fried potato slices , 2005 .

[10]  O. Paquet-Durand,et al.  Monitoring baking processes of bread rolls by digital image analysis , 2012 .

[11]  V. Gökmen,et al.  Evaluation of the Maillard reaction in potato crisps by acrylamide, antioxidant capacity and color , 2009 .

[12]  E. Arendt,et al.  Influence of Additives and Mixing Time on Crumb Grain Characteristics of Wheat Bread , 2000 .

[13]  Petr Dejmek,et al.  A Low Cost Video Technique for Colour Measurement of Potato Chips , 1999 .

[14]  D. Rogers,et al.  Development of an objective crumb-grain measurement , 1995 .

[15]  Toby P. Breckon,et al.  Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab , 2011 .

[16]  Ronnier Luo,et al.  A digital imaging method for measuring banana ripeness , 2013 .

[17]  V. Gökmen,et al.  Investigating the correlation between acrylamide content and browning ratio of model cookies , 2008 .

[18]  Kit L. Yam,et al.  A versatile and inexpensive technique for measuring color of foods , 2000 .

[19]  Vural Gökmen,et al.  A Non-Contact Computer Vision Based Analysis of Color in Foods , 2007 .

[20]  Martin G. Scanlon,et al.  Prediction of Bread Crumb Density by Digital Image Analysis , 1999 .

[21]  Sundaram Gunasekaran,et al.  Computer vision technology for food quality assurance , 1996 .