Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision

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.