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Computer vision‐based analysis of foods: A non‐destructive colour measurement tool to monitor quality and safety
Author(s) -
Mogol Burçe Ataç,
Gökmen Vural
Publication year - 2014
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.6500
Subject(s) - artificial intelligence , digital image analysis , computer science , computer vision , browning , digital image , quality (philosophy) , process (computing) , image processing , machine vision , pattern recognition (psychology) , image (mathematics) , food science , chemistry , philosophy , epistemology , operating system
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. © 2013 Society of Chemical Industry