Search

Computer vision has emerged as a useful alternative to manual expert grading of meat in recent years. Manual grading by experts has a number of essential flaws that can be effectively mitigated with computer vision technology. Computer vision technology is a simple and affordable alternative that, while requiring some expertise, is not excessively technologically demanding. Computer vision technology has shown key attributes of flexibility and ease of compatibility allowing a wide range of meat quality assessment challenges to be successfully tackled. The simplest approaches involving visible light imaging and explicit statistical modelling have proven adequate on the vast majority of occasions. On the other hand, for difficult tasks, more expensive non-visible wavelength imaging and implicit statistical modelling are required. In addition, fully automatic image segmentation still remains a difficult problem in many instances, although image processing is becoming more powerful particularly as computationally demanding texture characterisation algorithms become more viable as computation speeds increase. Finally training and testing a computer vision system will require considerable groundwork as a substantial amount of image and independent meat quality data will be required for good model calibration and validation. [Copyright &y& Elsevier]

With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables. Computer vision has been successfully adopted for the quality analysis of meat and fish, pizza, cheese, and bread. Likewise grain quality and characteristics have been examined by this technique. This paper presents the significant elements of a computer vision system and emphasises the important aspects of the image processing technique coupled with a review of the most recent developments throughout the food industry. [Copyright &y& Elsevier]

A computer vision system was used to evaluate the melting property of cheese. The method is suitable for both cheddar and mozzarella cheese. The effect of cooking time (0–20 min) and temperature (70–200°C) on the melting degree and melting rate of cheddar and mozzarella cheese was investigated and the melting property of both cheeses were compared. Both cheddar and mozzarella cheese melted rapidly at the initial stage of cooking and melting slowed down in about 3–4 min. Maximum melting degree and melting rate occurred at about 130–160°C. Extended cooking and cooking at high temperature may cause reduced melting. Cheddar cheese melted more rapidly and considerably than mozzarella cheese at all cooking temperatures. [Copyright &y& Elsevier]

Abstract: As a consequence of market competition, the production and manufacture of cheese products are at the stage of innovative dynamics. Pasteurized cheese with vegetable ingredients is one of the new products that may be added to sandwiches, salads, sauce, toast and pizza. Since vegetable ingredients can improve the nutritive value and flavour of cheese, it will probably become more and more popular in the future. Such new products require new techniques for monitoring and evaluating their quality in order to satisfy the increased awareness and expectations of consumers. Computer vision methods have been used increasingly in the food industry for inspection and evaluation purposes as they provide a rapid, economical, consistent and objective assessment. The aim of this study was to develop a computer vision method for inspecting two major quality attributes of pasteurized cheese, i.e. the distribution and amount of ingredients. An image pre-processing algorithm was first developed to delete the border area of cheese. Next a three-step method for ingredient extraction was developed, comprising colour quantification, ingredient location, and mask operation. Finally, the distribution and amount of each ingredient was calculated automatically. Two kinds of pasteurized cheese were evaluated using the above method, i.e. (a) cheese with garlic and parsley and (b) cheese with a mixture of vegetables composed mainly of pepper and parsley. It was found that the distribution and amount of ingredients in the first set of samples were determined within an accuracy of over 88%, compared with the results of a sensory method. As for the second set of samples, accuracies of over 81% and over 71% were achieved for measuring the distribution and amount of ingredients, respectively. [Copyright &y& Elsevier]