Color is one of the most important metrics of foodstuffs quality. It gives an indication of freshness, ingredient composition as well as about the presence or absence of falsification. Most often, the color is estimated visually, and thus, the evaluation is subjective. By automating the color analysis a wide application for this method could be found. The aim of this research is to study the principles of color analysis as applied to the task of evaluating the freshness of meat products using modern machine vision systems. From a scientific point of view, the color of meat depends on the proportion of myoglobin and its derivatives. It's the main pigment that characterizes the freshness of meat. Further color of meat can change due to oxidation of myoglobin during storage. Myoglobin exists in three forms. There are oxygenated form, oxidized form and form without oxygen. The meat color changes not only due to the conversion of one form into another. The content of amino acids and ammonia are another characteristics and constant signs of meat products spoilage. The paper presents the results of meat color computer simulation based on data on the content of various forms of myoglobin in different proportions. The spectral characteristic of the light source used to illuminate the meat sample is taken into account. Also the experimental studies were conducted using samples of beef. As a result the correlations between said biochemical indicators of the quality and color of the meat obtained with the help of machine vision system were found.
At the moment color sorting method is one of promising methods of mineral raw materials enrichment. This method is based on registration of color differences between images of analyzed objects. As is generally known the problem with delimitation of close color tints when sorting low-contrast minerals is one of the main disadvantages of color sorting method. It is can be related with wrong choice of a color model and incomplete image processing in machine vision system for realizing color sorting algorithm. Another problem is a necessity of image processing features reconfiguration when changing the type of analyzed minerals. This is due to the fact that optical properties of mineral samples vary from one mineral deposit to another. Therefore searching for values of image processing features is non-trivial task. And this task doesn't always have an acceptable solution. In addition there are no uniform guidelines for determining criteria of mineral samples separation. It is assumed that the process of image processing features reconfiguration had to be made by machine learning. But in practice it's carried out by adjusting the operating parameters which are satisfactory for one specific enrichment task. This approach usually leads to the fact that machine vision system unable to estimate rapidly the concentration rate of analyzed mineral ore by using color sorting method. This paper presents the results of research aimed at addressing mentioned shortcomings in image processing organization for machine vision systems which are used to color sorting of mineral samples. The principles of color analysis for low-contrast minerals by using machine vision systems are also studied. In addition, a special processing algorithm for color images of mineral samples is developed. Mentioned algorithm allows you to determine automatically the criteria of mineral samples separation based on an analysis of representative mineral samples. Experimental studies of the proposed algorithm were performed using samples of gold and copper-nickel ores. And obtained results confirmed its efficiency with respect to mineral objects. The research results will allow: expanding the use of the color sorting method in the field of mineral raw materials enrichment; facilitating the search for values of image processing features for machine vision systems which are used to the color analysis of minerals; reducing the time required for reconfiguration of image processing features when changing the type of analyzed minerals; realizing the process of rapid estimating the concentration rate of analyzed mineral ore by using color sorting method.
Due to the depletion of solid minerals ore reserves and the involvement in the production of the poor and refractory ores a process of continuous appreciation of minerals is going. In present time at the market of enrichment equipment are well represented optical sorters of various firms. All these sorters are essentially different from each other by parameters of productivity, classes of particles sizes for processed raw, nuances of decision algorithm, as well as by color model (RGB, YUV, HSB, etc.) chosen to describe the color of separating mineral samples. At the same time there is no dressability estimation method for mineral raw materials without direct semi-industrial test on the existing type of optical sorter, as well as there is no equipment realizing mentioned dressability estimation method. It should also be note the lack of criteria for choosing of one or another manufacturer (or type) of optical sorter. A direct consequence of this situation is the "opacity" of the color sorting method and the rejection of its potential customers. The proposed solution of mentioned problems is to develop the dressability estimation method, and to create an optical-electronic system for express analysis of mineral raw materials dressability by color sorting method. This paper has the description of structure organization and operating principles of experimental model optical-electronic system for express analysis of mineral raw material. Also in this work are represented comparison results of the proposed optical-electronic system and the real color sorter.