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Rambler's Top100

Food processing Industry №6/2015

News of the Month

Results of the Food Processing Industry of Russia

AUTOMATION AND CONTROL TECHNOLOGIES AND BUSINESS PROCESSES

Blagoveshchenskii I.G. The use of computer vision systems to control online the quality of raw materials and finished food industry products

P. 9-13 Key words
automation of technological processes; the quality of finished products; control; food industry; computer vision systems

Abstract
The article shows the importance for the food industry automation control online quality raw material, finished products, identification of marriage. Based-WAN the ability to solve this problem using computer vision systems (CPS). The article presents the problems solved by the computer vision system. Brief description of the goals of computer vision in various applications. Considering Rena structure of market demand and computer vision systems. Lists of IP-using RMS of various technologies and methods for image processing. The basic methods of image processing. Shows the importance of using RMS crisp digital image, image processing to highlight significant information in the image and the mathematical analysis of the obtained data to solve problems. Shows the layout of a typical CPS. Shows the composition of a typical computer vision system. Analyzed the principle of operation of RMS in determining the product of the marriage. The sequence performed by the system of action. The basic steps for image processing in computer vision system. In the article the conclusion about expediency of use in the food industry RMS to develop high-tech automated intelligent expert systems for quality control of food raw materials, semi-finished and finished products.

References
1. Blagoveshchenskaya M. M., Zlobin L. A. Informatsionnye tekhnologii sistem upravleniya tekhnologicheskimi protsessami [Information technologies of process control systems]. Moscow: VysshayashkolaPubl., 2005. 768p.
2. Blagoveshchenskaya M. M., Makarov V. V. [The identification aspect of the methodology of creating control systems of technological objects with nonstationary parameters]. Vestnik Voronezhskogo gosudarstvennogo universiteta inzhenernykh tekhnologii, 2014, no. 1, pp. 85-90. (In Russ.)
3. Blagoveshchenskaya M. M. Osnovy stabilizatsii protsessov prigotovleniya mnogokomponentnykh pishchevykh mass: monografiya [Bases of stabilization of preparation processes of multi-component food masses]. Moscow, "Frantera" Publ., 2009. 281p.
4. Kazarinov L. S., Shnaider D. A., Barbasova T. A. Avtomatizirovannye informatsionno-upravlyayushchie sistemy: ucheb. posobie [Automated informationalcontrol systems].Chelyabinsk: SUSUPubl., 2008. 320p.
5. Blagoveshchenskii I. G., Shaverin A. V., Blagoveshchenskaya M. M. [Automating control of organoleptic quality indicators of chocolate products] Mat-ly pervoi mezhdunarodnoi nauchno-prakticheskoi konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. the First intern. scientific-practical conference-exhibition "Planning and provision of staff training and retraining for the food industry and medicine"]. Moscow, MSUFP, 2012, pp. 209-212. (InRuss.)
6. Danilova M. A. et al. [Automated accounting system of bulk foodstuffs]. Khranenie i pererabotka sel'khozsyr'ya, 2012, no. 6, pp. 63-66. (In Russ.)
7. Blagoveshchenskaya M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing the bases of modeling and diagnostics of hydromechanical systems of food productions in their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauchno-metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the17thIntern. scientific method. conf. "High intelligent technologies and innovations in education and science"]. St. Petersburg, PolytechnicInstitutePubl., 2010, vol. 2, pp. 95-98.(InRuss.)
8. Blagoveshchenskaya M. M., Ivanov Ya. V. [Subtraction of images in MATLAB]. Sb. dokladov IV mezhdunarodnoi konferentsii-vystavki "Vysokoeffektivnye pishchevye tekhnologii, metody i sredstva dlya ikh realizatsii" [Proc. the 4th Intern. conference-exhibition "High-food technologies, methods and means for their implementation"]. Moscow, MSUFP Publ., 2006, part 2, pp. 130-132.(InRuss.)
9. Ivanov Ya. V., Blagoveshchenskaya M. M. [The use of digital video cameras in automatic controlsystems of technological processes of food productions]. Sb. mat-lov V yubileinoi shkoly-konferentsii s mezhdunarodnym uchastiem "Vysokoeffektivnye pishchevye tekhnologii, metody i sredstva dlya ikh realizatsii" [Proc. the 5thJubilee School-conf. with intern. participation "Highly food technologies, methods and means for their implementation"]. Moscow, MSUFP, 2007, pp. 347-349. (In Russ.)
10. Blagoveshchenskaya M. M., Ivanov Ya. V. [Mathematical modeling movement of food mass harness after orizontal molding]. Zhurnal nauchnykh publikatsii aspirantov i doktorantov, 2008, no. 6, pp. 164-166. (InRuss.)
11. Blagoveshchenskaya M. M., Ivanov Ya. V.[Using intelligent sensor in the automatic control system of technological processes]. Sb. dokladov X mezhdunarodnoi nauchno-prakticheskoi konf. "Avtomatizatsiya i informatsionnoe obespechenie proizvodstvennykh protsessov v sel'skom khozyaistve" [Proc. the 10th Intern. scientific and practical conf. "Automation and information support of production processes in agriculture"]. Moscow, All-Russ. Research Institute of Agricultural Mechanization Publ., 2008, part. 2, pp. 448-451. (InRuss.)
Authors
Blagoveshchenskii Ivan Germanovich, graduate
Moscow State University of Food Production,
125080, Moscow, Volokolamskoe shosse, 11.



Fomushkin V.I., Blagoveshchenskaij M.M., Nosenko S.M., Blagoveshchenskii I.G.The automated expert system for raw meat microbiological spoilage risks monitoring

P. 14-17 Key words
microbiological safety; spoilage; storage; risks; meat; unplanned temperaturevariability; predictive model; monitoring

Abstract
One of the mainreasons forthe microbiologicalspoilageof meatisnon-compliance withthe establishedtemperature conditions in the links ofthe cold chain, as well as unplannedtemperature variability cased byequipment malfunctionsor electrical networks failure, as well as with the human factor. This paper discusses thepossibilitiesof automationto control and predict the microbiologicalspoilagerisk for raw meatduring storage using the temperature as the main parameter for changing microbiological statusof raw meat. The analysis of the automated parameters has been performed. Among them: temperature and respecting time todifferent initialcontaminationof raw meat. The role of predictive mathematical model has been shown in the implementation and evaluation of the data got from monitoring meat storage conditions and in the identifying unacceptable risks and critical control points (CCP). Automationcontrolis carried by automatingoperations as part of the kinetic curveimage for microorganism population growth, processing the digitalandvisual information, mathematical calculations andconsistencyanalysis of the CCP measurements toestablishedcritical limits. A computerprogram (basicprinciples andstructure) has been implementedfor processing experimental dataandconstructingpredictedempirical models of bacterial contamination in order to manage the processes of microbiologicalspoilage. To determine thestart ofraw meatspoilage, predictive modelsare an alternative tothe numerousand costlymicrobiological researches. The effectivenessof computer system implementationis the errors reduction, as well as the benefitsin time andcostin securingraw meatduring storage.

References
1. Food Security Doctrine of the Russian Federation (approved by Presidential Decree of January 30, 2010 ¹ 120). (In Russ.)
2. Il'ina Z., Kondratenko S., Enchik S. Belorusskoe sel'skoe khozyaistvo, 2014, no. 8 (148). (In Russ.)
3. Krishtafovich V.I.,Zhebeleva I. A., Tolkunova N. N. Kholodil'naya obrabotka i sokhranyaemost' myasa i myasnykh produktov [Refrigeration processing and storageability of meat and meat products]. Ed. byV. I. Krishtafovich. Moscow, 2006. 172p.
4. Cheskotti O. [The role of the cold in the global food resources]. Informatsionnyi byulleten' Mezhdunarodnogo instituta kholoda of 09.09.2009.
5. Yuldashev R. S. [Features of refrigeration processing and storage of meat]. Myasnye tekhnologii, 2010, no. 5, pp. 42-45.(In Russ.)
6. Rogov I. A., Miklashevskii V. V., Danil'chuk T. N. [Prospects for usingthe frozen raw meat]. Myasnye tekhnologii, 2010, no. 5, pp. 38-41. (InRuss.)
7. Kazakov V. I. [Practical solutions for a perfect meat plant]. Myasnye tekhnologii, 2014, no. 6 (138). (In Russ.)
8. Shevchenko V. V. et al. Izmeritel'nye metody kontrolya pokazatelei kachestva i bezopasnosti produktov pitaniya. Ch. 2. Produkty zhivotnogo proiskhozhdeniya [Measuring methods of control of quality and safety of food. Part 2. Products of animal origin]. St. Petersburg, Troitskii most Publ., 2009. 200p.
9. Blekbern K. de V. Mikrobiologicheskaya porcha pishchevykh produktov [Microbial spoilage of foods]. St. Petersburg, Professiya Publ., 2008. 781 p.
10. Rubanov V. G., Filatov A. G. Intellektual'nye sistemy avtomaticheskogo upravleniya. Nechetkoe upravlenie v tekhnicheskikh sistemakh: ucheb. Posobie [Intellectual automatic control systems. Fuzzycontrolintechnicalsystems]. Belgorod, BSTUPubl., 2010.170 p.
11. Rubanov V. G. Teoriya avtomaticheskogo upravleniya (matematicheskie modeli, analiz i sintez lineinykh sistem): ucheb. posobie [Automatic control theory (mathematical models, analysis and synthesis of linear systems)]. Belgorod,BSTU Publ., 2009, part 1. 199p.
12. Selevtsov L. I., Selevtsov A. L. Avtomatizatsiya tekhnologicheskikh protsessov [Automation of technological processes]. Moscow, Akademiya Publ., 2014. 352 p.
13. Afonin A. M. et al. Teoreticheskie osnovy razrabotki i modelirovaniya sistem avtomatizatsii: ucheb. posobie [The theoretical bases of development and modeling automation systems]. Moscow, Forum Publ., 2011. 192p.
14. Ibraev A. M. et al. Kholodil'naya tekhnologiya pishchevoi promyshlennosti [Refrigerated technology of food industry]. Kazan', KSTU Publ., 2010. 125 p.
15. Mitin V. V. Issledovanie i razrabotka komp'yuternoi sistemy upravleniya mikrobiologicheskimi protsessami pri proizvodstve syrovyalenykh myasoproduktov: dis. kand. tekhn. nauk[Research and development of computer control system of microbiological processes in production of jerked meat products]. Moscow, MSUAB, 2002. 60 p.
16. Egorov G. A., Prokhorov N. L., Krasovskii V.E. Upravlyayushchie vychislitel'nye kompleksy dlya promyshlennoi avtomatizatsii: ucheb. posobie [Control computing complexes for industrial automation]. Eds. by N. L. Prokhorov, V. V. Syuzev. Moscow, Bauman MSTU, 2012. 372 p.
17. Efremov D. N. Issledovanie i razrabotka modelei, algoritmov i komp'yuternoi sistemy monitoringa i upravleniya proizvodstvom biologicheski bezopasnykh molochnykh produktov: dis. kand. tekhn. nauk [Research and development of models, algorithms and computer systems for monitoring and management of production of biologically safe dairy products: Cand. Diss. (Techn. Sci.)]. Moscow, MSUAB, 2002. 115 p.
Authors
Fomushkin Vladimir Igorevich, graduate,
Blagoveshchenskaij Margarita Mihailovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated sustems,
125080, Moscow, Volokolamskoeshosse, 11.
Nosenko Sergei Mikhailovich, doctor of technical Sciences, Professor,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1.
Blagoveshchenskii Ivan Germanovich,
doctor of technical Sciences, Professor,
Moscow state technical University. N. Uh. Bauman, department "The Theoretical Mechanics" named after professor N. E. Zhukovsky,
105005, Moscow, 2-nd Baumanskaya, 5, p. 1.



Petrjakov A.N., Blagoveshhenskaja M.M., Nosenko A.S., Blagoveshchenskii I.G. The use of genetic algorithms and particle swarm optimization methods in the production of animal feed

P. 18-21 Key words
mixed feed; particle swarm optimization; real-coded genetic algorithm; linear programming; cost optimization

Abstract
In this study, the best mixed feed was prepared by using the algorithm of particle swarm optimization (PSO) and by taking into account the breeding type and method of the poultries and various farm animals (cattle, sheep, rabbit), their needs, ages, and feeding costs and optimization them all. Algorithms were operated ten times for the same animal species, and certain conclusions were drawn from the recorded results. The most outstanding finding of the study was that particle swarm optimization behaved rather steadily in the solution of nonlinear problems. It was determined that genetic coded algorithm could not behave in a sufficiently steady way, and it stuck to local minimums. When the results were examined, it was observed that low-cost feed mixes having fewer penalties were available in linear programming in the solution of linear feed mix problems. According to results, particle swarm optimization produces more rapid and steady results having low penalty rates but its cost is slightly higher than real-coded genetic algorithm. Primary objective to be taken into consideration during the preparation of feed mixes is low penalty rate. Particle swarm optimization was accepted to produce better results than real-coded genetic algorithm as cost was a secondary factor to observe. However, it is observed that real-coded genetic algorithm and particle swarm optimization produce better results in the solution of nonlinear feed mix problems.

References
1. Sahman M.A., Altun A.A. Cost optimization of mixed feeds with the particle swarm optimization method. Neural Computing and Applications, 2011, no. 22,pp. 383-390.
2. Baran M.S. et al. Determination of the feeding values of feedstuffs and mixed feeds used in the Southeastern Anatolia region of Turkey. Turkish Journal of Veterinary and Animal Sciences, 2008, no. 32 (6),pp. 449-455.
3. Oishi K., KumagaiH., Hirooka H. Application of the modified feed formulation to optimize economic and environmental criteria in beef cattle fattening systems with food byproducts. Animal Feed Science and Technology, 2011, no. 165, pp. 38-50.
4. Kerrigan G.L., Norback J.P. Linear programming in the allocation of milk resources for cheese making. Journal of Dairy Science, 1986, no. 69 (5), pp. 1432-1440.
5. Munford A.G. The use of iterative linear programming in practical applications of animal diet formulation. Mathematics and Computers in Simulation, 1996, no. 42 (2-3), pp. 255-261.
6. Sahman M.A. et al. Cost optimization of feed mixed by genetic algorithms. Advances in Engineering Software, 2009, no. 40, pp. 965-974.
7. Coskun B., I'nal F., I'nalS. Ration programs. Available at: http://veteri-ner.selcuk.edu.tr/bolum/hbesleme. 2007.
Authors
Petrjakov Aleksandr Nikolaevich, postgraduate,
Blagoveshhenskaja Margarita Mihajlovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated systems,
125080, Moscow, Volokolamskoeshosse, 11.
Nosenko Sergei Mikhailovich, Candidate Economical Sciences, Professor,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N.Uh. Bauman, department "The Theoretical Mechanics" named after professor N.E. Zhukovsky.
105005, Moscow, 2-nd Baumanskaya, 5, p. 1.



Blagoveshchenskii I.G., Blagoveshchenskaya M.M., Nosenko S.M., Nosenko A.S. The selection of informative variables in the problem of structural-parametric fashion of stimulation of the process of cooking fondant syrup

P. 22-25 Key words
structural-parametric modeling; process; object management; simulation model; fondant syrup

Abstract
The article shows that an important step in the production of cream candies is the process of cooking fondant syrup. The scheme of preparation of sugar fondant, as well as the basic operations of cooking fondant syrup dosage sugar syrup, molasses, milk and other ingredients, mixing and boiling of mixture prescription. A description of the main parameters affecting the process of cooking fondant syrup. Described their relationship to each other. To identify all parameters and conditions that influence the process of cooking fondant syrup, was designed structural - parametric model of the process of cooking fondant syrup. In the work produced structural - parametric modeling of the process of cooking fondant syrup, which is boiled down to the construction of matrices of relationships between grouped by state parameters and goals of the individual functional blocks of the system similarly to the parametric adjacency matrix. For this purpose, the original data were generated in the form of an array of random observations. Further, as a result of casual observations was made a set of statistical data and generated the table of correlations. Correlation, in turn, subjected to the test of significance by student's criterion, the resulting transformed matrix of correlation coefficients. The main task was to find comparable characteristics of relations between the state parameters of the technological system, and then build a situational model of the system state with the algorithmic procedures of identification and forecasting. Was defined statistical model of the process of cooking fondant syrup by the method of Protodyakonov. The coefficients of the equations were calculated using the program Method. According to the formula coefficients have been calculated multiple linear regression regression and composed the matrix of relationships that has been converted into a matrix comparable dimensionless characteristics of relations. As a result of subsequent mathematical permutations and calculations in article obtained optimal criterion for the quality of the process of cooking fondant syrup. In this work it is concluded that the proposed method of structural - parametric modeling allows you to determine the most informative parameters in the problem of structural - parametric modeling of the process of cooking fondant syrup and find the best criterion of its quality.

References
1. Blagoveshchenskaya M. M., Zlobin L. A. Informatsionnye tekhnologii system upravleniya tekhnologicheskimi protsessami. Ucheb. dlya vuzov [Information technologies of process control systems]. Moscow, Vysshaya shkola Publ., 2010. 768p.
2. Blagoveshchenskaya M. M. Osnovy stabilizatsii protsessa prigotovleniya mnogokomponentnykh pishchevykh mass [Bases of stabilization of cooking process multi-component food masses]. Moscow, Frantera Publ., 2009. 281 p.
3. Blagoveshchenskaya M. M., Makarov V. V. [The identification aspect in methodology of creating control systems of the technological objects with time-varying parameters]. Vestnik Voronezhskogo gosudarstvennogo universiteta inzhenernykh tekhnologii, 2014, no. 1, pp. 85-90. (In Russ.)
4. Zamyatina O. M. Modelirovanie system. Ucheb. posobie [Modeling systems]. Tomsk, TPU Publ., 2009. 204p.
5. Kuprienko N. V., Ponomareva O. A., Tikhonov D. V. Statistika. Metody analiza raspredelenii. Vyborochnoe nablyudenie. S ispol'zovaniem STATISTICA. Ucheb. posobie [Methods of analysis of distributions. Selective observation. Using STATISTICA]. St. Petersburg, Politechnical University Publ., 2009. 138p.
6. Blagoveshchenskaya M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing bases of modeling and diagnostics hydromechanical systems of food productions according to their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauch. metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the 17th Intern. scientific method. conf. "High intelligent technologies and innovations in education and science"]. St. Petersburg, Politechnical University Publ., 2010, vol. 2, pp. 95-98. (In Russ.)
7. Blagoveshchenskaya M. M., Apanasenko S. I., Blagoveshchenskii I. G. [Virtual sensors based on neural network algorithms for determining the quality of the food masses]. Khranenie i pererabotka sel'khozsyr'ya, 2012, no. 9, pp. 44-45. (In Russ.)
8. Ivashkin Yu. A. Sistemnyi analiz i issledovanie operatsii v prikladnoi biotekhnologii. Ucheb. posobie [System analysis and operations research in applied biotechnology]. Moscow, MSUAB Publ., 2005. 196p.
9. Ivashkin Yu. A., Nazoikin N. V. Strukturno-parametricheskie i agentno-orientirovannye tekhnologii. Laboratornyipraktikum [Structural-parametric and agent-oriented technologies. Laboratory workshop].? Moscow, MSUAB Publ., 2010. 134 p.
10. Ivashkin Yu. A. Agentnye tekhnologii i mul'tiagentnoe modelirovanie sistem. Ucheb. posobie [Agent technologies and multiagent systems modeling]. Moscow, MIPT Publ., 2013. 268 p.
Authors
Blagoveshchenskii Ivan Germanovich, graduate
Blagoveshchenskaya Margarita Mikhailovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated sustems
125080, Moscow, Volokolamskoeshosse, 11.
Nosenko Sergei Mikhailovich, doctor of technical Sciences, Professor,
Nosenko Aleksei Sergeevich, candidate.Econ.Sciences
Management Company "United Confectioners"
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1. This email address is being protected from spambots. You need JavaScript enabled to view it.



Kostin A.M., Yablokov A.E., Blagoveshchenskii I.G., Nosenko S.M. Distributed automated system for intelligent monitoring of the equipment of grain processing enterprises

P. 26-30 Key words
technological equipment; diagnostics; monitoring; cloud technology; grain manufacture enterprises; intelligent automated systems

Abstract
In article aspects of the use of artificial intelligence technologies as a method of pattern recognition in the tasks of monitoring decision-making and technical diagnostics equipment grain processing enterprises. It is shown that the most perspective direction of development of monitoring systems is the use of intelligent methods of analysis of source diagnostic information. These methods include expert systems based on neural network data analysis methods. The use of intelligent systems for monitoring and technical diagnostics helps to increase efficiency and reliability of technological equipment. For the successful application of these technologies is necessary to have a large amount of statistical data on many parameters of the machines in various operating modes and technical conditions. There are development and theoretical researches of the intelligent monitoring system in Moscow State University of Food Production (MGUPP). There are practical researches to collect information characterizing various technical state of the researched machines on grain processing enterprises (feed and milling) in the Moscow region. The proposed system for monitoring and gathering information having distributed nature and constructed with the use of "cloud" technologies. Thus, the controlled equipment is equipped with a stationary system tolerable of collection of telemetry data (vibration, temperature, instantaneous values of current to motor circuit, the noise emitted by the machine during its operation). Further information is transmitted to a remote server through GSM technology for its storage and mathematical treatment. Currently designed a prototype monitoring system that continuously collect information in real time with functioning equipment. This approach allows us to accumulate the necessary statistics on the equipment to create a powerful knowledge base and the creation of neural networks. The results of research is the development of software and hardware of a stationary system of collection and processing of diagnostic information about the researched equipment. The block diagram of the automated monitoring of technological machines is shown. The procedure of collecting and analyzing diagnostic information using neural network data analysis tools program Statistica has been developed.

References
1. GlebovL. A. et al. Tekhnologicheskoe oborudovanie i potochnye linii predpriyatii po pererabotke zerna: uchebnik [Technological equipment and production lines of grain processing factories]. Parts 1, 3 ed.byL. A. Glebov. Part 2 ed. by A. B. Demskii. Moscow, DeLiprint, 2010. 696p.
2. Blagoveshchenskaya M. M., Zlobin L. A. Informatsionnye tekhnologii sistem upravleniya tekhnologicheskimi protsessami [Information technologies of process control systems]. Moscow, VysshayashkolaPubl., 2005. 768p.
3. Blagoveshchenskaya, M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing the bases of modeling and diagnostics hydromechanical systems of food productions according to their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauch. metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the 17thInternational scientific-method. conf. "High intelligent technologies and innovations in education and science"]. St. Petersburg, Politechnical University Publ., 2010, vol. 2, pp. 95-98. (In Russ.)
4. Tugengol'd A. K. et al. Intellektual'nye sistemy v upravlenii proizvodstvennymi i tekhnologicheskimi protsessami [Intelligent systems in management of production and technological processes]. DSTU Publ., 2010. 179p.
5. Blagoveshchenskaya M. M. et al. [The use of intelligent technologies for control of the cheesequality]. Vestnik VGUIT, 2014, no. 2, pp. 83-89. (InRuss.)
6. Kulakov S. M., Trofimov V. B. Intellektual'nye sistemy upravleniya tekhnologicheskimi ob"ektami: teoriya I praktika [Intelligent control systems of technological objects: theory and practice]. Federal agency for education, Siberian state industrial university Publ., 2009. 223p.
7. Galushkin A. I. Neironnye seti. Osnovy teorii [Neural networks. Fundamentals of the theory]. Moscow, HLTPubl., 2012. 496 p.
8. Shaverin A. V., Blagoveshchenskaya M. M., Blagoveshchenskii I. G. [Control automation of organoleptic qualitie indicators of chocolate products] Mat-ly pervoj mezhdunarodnoj nauch. prakt.konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny". [Proc.the first international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP Publ., 2012, pp. 209-212. (InRuss.)
9. Blagoveshchenskaya M. M., Apanasenko S. I., Blagoveshchenskii I. G. [Virtual sensors based on neural network algorithms for determining quality of the food masses] Khranenie i pererabotka sel'khozsyr'ya, 2012, no. 9, pp. 44-45.(In Russ.)
10. Blagoveshchenskaya M. M., Blagoveshchenskii I. G., Nosenko S. M. [The automatic control system of forming bundles of candy]. Pishchevaya promyshlennost', 2013, no. 5, pp. 46-49. (In Russ.)
11. Blagoveshchenskaya M. M. et al. [The automatic control system of forming bundles of candy]. Khranenie I pererabotka sel'khozsyr'ya, 2013, no. 7, pp. 39-40. (InRuss.)
12. Blagoveshchenskii I. G., Apanasenko S. I., Blagoveshchenskaya M. M. [Creating virtual sensors based on a neural network for determining the main characteristics of the confectionery masses]. Konditerskoe I khlebopekarnoe proizvodstvo, 2014, no. 11-12, pp. 44-47. (In Russ.)
Authors
Kostin Alexander Mikhailovich, graduate,
Yablokov Alexander Evgenievich, candidate of technical Sciences, associate Professor
Moscow State University of Food Production, department of information technology and automated sustems,
125080, Moscow, Volokolamskoeshosse, 11.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N. Uh. Bauman, department "The Theoretical Mechanics" named after professor N. E. Zhukovsky,
105005, Moscow, 2-nd Baumanskaya, 5, p. 1
Nosenko Sergei Mikhailovich, doctor of technical Sciences, Professor,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1.



Blagoveshchenskii I.G., Nosenko S.M., Nosenko A.S. Expert intelligent monitoring system of the molding process fondant candies using vision systems

P. 32-35 Key words
fondant candy; molding process; expert system; monitoring; machine vision system

Abstract
In this article the necessity of solving the problem of automation of quality control of fondant masses, the most massive and popular categories of chocolates. It is shown that the most important process in the production of these sweets is the molding process. The conducted research has focused on the development of intelligent integrated expert system for monitoring structural and mechanical properties of fondant masses after molding with the use of vision systems. An important area of application of digital images recorded by a digital camera in the visible range, an automatic control of the main indicators kachestva color and shape of manufactured products. The article formulates the tasks required for achieving the set goal. Also presented proposals to ensure wide use of digital video camera as a smart sensor. The technique of preparation of digital videography and methods experimental video molding process harnesses fondant mass in industrial conditions. Developed, presented in the paper, the block diagram of the control algorithm on the basis of which an intelligent system takes the decision to change the regulatory impacts. Presents a model of the system of automatic regulation of the process of molding fondant harness developed in Simulink.

References
1. Blagoveshchenskaya M. M., ZlobinL. A. Informatsionnye tekhnologii sistem upravleniya tekhnologicheskimi protsessami: ucheb. dlya vuzov [Information technologies of process control systems]. Moscow, Vysshaya shkola Publ., 2005. 768p.
2. Shaverin A. V., Blagoveshchenskaya M. M., Blagoveshchenskii I. G. [Control automation of organoleptic qualitie indicators of chocolate products]. Mat-ly pervoi mezhdunarodnoi nauchno-prakticheskoi konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. The first international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP Publ., 2012, pp. 209-212. (InRuss.)
3. Ivanov Ya. V., Blagoveshchenskaya M. M., Blagoveshchenskii I. G. [Automating the process of forming candy mass on the basis of mathematical and algorithmic support, using as a smart trans mitter digital camcorder (DVC)]. Mat-ly pervoi mezhdunarodnoi nauchno-prakticheskoi konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. the First international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP Publ., 2012,pp. 215-218. (In Russ.)
4. Blagoveshchenskaya M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing the bases of modeling and diagnostics hydromechanical systems of food productions according to their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauch. metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the 17th International scientific-method. conf. "High intelligent technologies and innovations in education and science]. St. Petersburg, Politechnical University, 2010, vol. 2, pp. 95-98. (In Russ.)
5. Danilova M. A. et al. [Automated accounting system of bulk food products]. Khranenie I pererabotka sel'khozsyr'ya, 2012, no. 6, pp. 63-66. (InRuss.)
6. Troitskii A. K., Blagoveshchenskii I. G. [The possibility of using images processing for quality control of confectionary]. Mat-ly mezhdunarodnoi nauch. prakt. konf. "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. International scientific-practical conference "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP, 2012, pp. 160-165. (InRuss.)
7. Troitskii A. K.,Blagoveshchenskii I. G. [Theoretical bases of using vision system in the automatic process control]. Mat-ly mezhdunarodnoi nauch. prakt. konf. "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. International scientific-practical conference "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP, 2012, pp. 165-172. (InRuss.)
8. Blagoveshchenskii I. G.,Troitskii A. K. [The use of Prewitt method in designing algorithms…]. Mat-ly pervoi mezhdunarodnoi nauch. prakt. konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. the First international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFPPubl., 2012, pp. 153-157. (InRuss.)
9. Blagoveshchenskii I. G., Troitskii A. K. [Formalization of source images to extract information for processing digital video frames using various methods]. Mat-ly pervoi mezhdunarodnoi nauch. prakt. konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. the First international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFPPubl., 2012, pp. 157-160. (In Russ.)
10. Blagoveshchenskii I. G.,Troitskii A. K. [The possibility of using image processing for quality control of confectionery]. Mat-ly pervoi mezhdunarodnoi nauch. prakt. konferentsii-vystavki "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. the First international scientific-practical conference-exhibition "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow,MSUFPPubl., 2012, pp. 160-165. (InRuss.)
11. Blagoveshchenskaya M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing the bases of modeling and diagnostics hydromechanical systems of food productions according to their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauch. metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the 17th International scientific-method. conf. "High intelligent technologies and innovations in education and science]. St. Petersburg, Politechnical University, 2010, vol. 2, pp. 95-98. (In Russ.)
12. Blagoveshchenskaya M. M., Blagoveshchenskii I. G., Nosenko S. M. [The automatic control system of forming bundles of candy]. Pishchevaya promyshlennost', 2013, no. 5, pp. 46-49. (In Russ.)
13. Blagoveshchenskaya M. M. et al. [The automatic control system of forming bundles of candy]. Khranenie i pererabotka sel'khozsyr'ya, 2013, no. 7, pp. 39-40. (In Russ.)
Authors
Blagoveshchenskii Ivan Germanovich, graduate,
Moscow State University of Food Production, department of information technology and automated sustems
125080, Moscow, Volokolamskoe shosse, 11.
Nosenko Sergei Mikhailovich, doctor of technical Sciences, Professor,
Nosenko Aleksei Sergeevich
Management Company "United Confectioners"
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1. This email address is being protected from spambots. You need JavaScript enabled to view it.



Nazoykin E.A., Ionov A.V., Nosenko A.S., Blagoveshchenskii I.G. The use ofmulti-agenttechnologyto forecast andidentification the learning process

P. 36-39 Key words
simulation; intelligent agents; multi-agent system; agent-orientedtechnology; social modeling; creation of knowledge

Abstract
The article reveals the use of technology multi-agent simulation modeling to predict and identify the processes that take place in high school at the time of transfer and accumulation of knowledge of the active elements. Enter a description of the block diagram in the form of system decomposition for further modeling. A mathematical description of the processes of knowledge creation and evaluation of the quality of education. Describes the use of means of expansion of multi-agent models by constructing an artificial neural network to improve the accuracy of calculations with the experiments with the model. The use of multi-agent simulation using mathematical models and artificial intelligence allows to reflect the status and dynamics of the process of accumulation and transfer of knowledge to the analysis and prediction of the quality of education. This article describes the methods and software implementation of agent-based simulation of interaction of the teacher and the student, taking into account the psycho-physiological, emotional and cognitive state of intelligent agents. Simulation results and their analysis.

References
1. Maklakov A.G. Professional'nyi psikhologicheskii otbor personala. Teoriya i praktika: uchebnik dlya vuzov [Professional psychological selection of staff. Theory and practice]. St. Petersburg, Piter Publ., 2008
2. Sviridov A.P. Statisticheskaya teoriya obucheniya [Statistical learning theory]. Moscow, RSSU Publ., 2009. 576 p.
3. Ivashkin Yu.A., Nazoikin E.A. [Multi-agent simulation of learning process]. Programmnye produkty i sistemy, 2011, no. 1, pp. 47-52. (In Russ.)
4. Nazoikin E.A. [Multi-agent simulation of the learning process and knowledge transfer]. Sistemy upravleniya i informatsionnye tekhnologii, 2011, no. 1.1 (43), pp. 159-162. (In Russ.)
5. Ivashkin Yu.A. Mul'tiagentnoe imitatsionnoe modelirovanie bol'shikh sistem: ucheb. posobie [Multi-agent simulation of large systems]. Moscow, MSUAB Publ., 2008. 238 p.
6. Shmidt B. Iskusstvo modelirovaniya i imitatsii. Vvedenie v universal'nuyu imitatsionnuyu sistemu Simplex3 [Art of modeling and simulation. The introduction of a universal simulation system Simplex3]. Eds. By Yu.A. Ivashkin, V.L. Konyukh. Ghent, 2003. 550p.
7. Blagoveshchenskaya M.M., Shepelev G.V. Innovatsionnye tekhnologii obrazovaniya v oblasti pishchevykh i kholodil'nykh proizvodstv [Innovative technologies of education in the field of food and refrigeration industries]. Seriya "Innovatsionnye tekhnologii obrazovaniya" [Ser. "Innovation technologies of education"]. St. Petersburg, "Intermediya" Publ., 2012, issue. 8. 421 p.
8. Blagoveshchenskaya M.M. et al. [Innovations in education based on technologies of interactive immersion]. Trudy XVII Vserossiiskoi nauchno-metodicheskoi konf."Telematika 2010" [Proc. the 17thAll-Russ. scientific and methodical conf. "Telematics 2010"]. 2010, vol. 2, pp. 338-340.(In Russ.)
9. Blagoveshchenskaya M.M., Davydenko T.M., Nikolaev N.S. [Innovative technologies of education]. Sb. Annotirovannykh otchetov po MKRITO za 2010 g. [Collection of annotated reports MKR ITO 2010]. Moscow, MIREA Publ., 2011. 143 p.
Authors
Nazoykin Evgeniy Anatolevich, candidate of technical Sciences, associate Professor
Ionov Andrei Viktorovich, associate Professor
Moscow State University of Food Production, department of information technology and automated sustems
125080, Moscow, Volokolamskoe shosse, 11.
Nosenko Aleksei Sergeevich, Candidate of Economic Sciences,
Management Company "United Confectioners"
115184, Moscow, Novokuznetskaya 2-nd per., 13/15, p. 1, This email address is being protected from spambots. You need JavaScript enabled to view it.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N.Uh. Bauman, department "The Theoretical Mechanics" named after professor N.E. Zhukovsky,
105005, Moscow, 2-nd Baumanskaya, 5, p. 1, This email address is being protected from spambots. You need JavaScript enabled to view it.



Bychkov I.A., Blagoveshchenskaya M.M., Nosenko A.S., Blagoveshchenskii I.G. The system of supplier selection processing enterprise in the face of uncertainty

P. 40-41 Key words
subsystem inference; expert system; linguistic variables; fuzzy sets theory; qualitative parameters

Abstract
In terms of abundance in the country the number of suppliers of inputs has increased dramatically. Processing plants that depend on suppliers, trying to find the best option, which will satisfy all customer requirements. In a qualitative and not expensive raw products manufactured by the company, will largely determine the economic performance of the enterprise such as revenue, earnings and profitability. Therefore, before these enterprises especially acute question of choosing the most suitable resource provider. Mainly for processing enterprises must take into account the following parameters: the quality of incoming raw materials, conditions for the supply of raw materials", "price of raw materials received, a contractual basis. For this purpose was developed and proposed methodology for supplier selection for incoming quality parameters with the use of linguistic variables, subsystems inference in fuzzy set theory. The basis of this methodology lies in the system together with expert estimates. Fuzzy and linguistic variables are used in natural language description of the various objects and phenomena in the formalization of processes and decision-making in examining hard situations. The proposed methodology allows the user to take an appropriate decision on the choice of the Executive Order required to set parameters, to avoid getting poor quality of raw materials. Also, using the proposed methodology, the company will be able to automate the stage of acceptance of raw materials, to focus on the production of products, new product development and sale of goods on the market, the development of optimal logistic component. Taken together, all steps to automate the business processes of the enterprise, as a result, the company will provide a structured, independent largely on human factors and other outside influences.

References
1. Ptuskin A.S. Nechetkie modeli i metody v menedzhmente [Fuzzy models and methods in management]. Moscow, Bauman MSTU, 2008.
2. Yager R.R. Nechetkie mnozhestva i teoriya vozmozhnostei. Poslednie dostizheniya [Fuzzy sets and possibilities theory. Latest achievements]. 1986. 409 p.
3. Yakh"eva G.E. Nechetkie mnozhestva i neironnye seti [Fuzzy sets and neural networks]. 2006. 316 p.
4. Khaptakhaeva N.B., Dambaeva S.V., Ayusheshva N.N. Vvedenie v teoriyu nechetkikh mnozhestv [Introduction to the theory of fuzzy sets]. 2004. 69 p.
5. Zade L.A. Ponyatie lingvisticheskoi peremennoi i ego primenenie k prinyatiyu priblizhennykh reshenii [The concept of linguistic variable and its application to the adoption of approximate solutions]. 1976. 165 p.
Authors
Bychkov Ivan Aleksandrovich, graduate,
Blagoveshchenskaya Margarita Mikhailovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated sustems
125080, Moscow, Volokolamskoe shosse, 11. This email address is being protected from spambots. You need JavaScript enabled to view it.
Nosenko Aleksei Sergeevich, Candidate of Economic Sciences,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1. This email address is being protected from spambots. You need JavaScript enabled to view it.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N.Uh. Bauman, department "The Theoretical Mechanics" named after professor N.E. Zhukovsky,
105005, Moscow, 2-nd Baumanskaya, 5, p. 1



Nosenko A.S., Bychkov I.A., Blagoveshchenskaya M., Blagoveshchenskii I.G. Automatic system of formation of the product range based on the theory of fuzzy sets

P. 42-44 Key words
subsystem inference; expert system; fuzzy sets theory

Abstract
In response to growing consumer demands in the country at the moment there is a tendency to reduce the role of standard products, replacing them with products designed for specific groups of consumers. The country is in practice to maintain an assortment of such a production plan, which provides for the same release for each product. Same preferences of consumers in this approach is not recognized. The theory of fuzzy sets enables scheme solving problems in which judgment or evaluation play an important role in the evaluation of uncertainty. The intention of this theory was to build a functional correspondence between fuzzy linguistic descriptions and special functions, expressing a degree of membership values of measured parameters mentioned vague descriptions. L. Zadeh fuzzy sets defined as a tool for constructing theories of options. The technique of forming the range of products for companies based on the theory of fuzzy sets. Finding perspective nomenclature and structure of production ensures the formation of a trade enterprise core, which will be sold on the market with minimum risk, will ensure stable functioning of the enterprise and achieve its strategic goals. Also, using the proposed methodology, the company will be able to automate the phase of the sale of goods on the market, focus on the manufacture of products, new product development, development of optimal logistic component. Taken together, all steps to automate the business processes of the enterprise, as a result, the company will provide a structured, independent largely on human factors and other outside influences.

References
1. Blagoveshchenskaya M. M., Zlobin L. A. Informatsionnye tekhnologii sistem upravleniya tekhnologicheskimi protsessami: ucheb. dlya vuzov [Information technologies of process control systems]. Moscow, Vysshaya shkola Publ., 2005. 768 p.
2. Ptuskin A.S. Nechetkie modeli i metody v menedzhmente [Fuzzy models and methods in management]. Moscow, Bauman MSTU, 2008.
3. Yager R.R. Nechetkie mnozhestva i teoriya vozmozhnostei. Poslednie dostizheniya [Fuzzy sets and possibilities theory. Latest achievements]. Moscow, Radio i svyaz' Publ., 1986, pp. 136-143.
4. Yakh"eva G.E. Nechetkie mnozhestva i neironnye seti [Fuzzy sets and neural networks]. Moscow, BINOM. Laboratoriya znanii Publ., 2006, pp. 71-79.
5. Zade L.A. Ponyatie lingvisticheskoi peremennoi i ego primenenie k prinyatiyu priblizhennykh reshenii [The concept of linguistic variable and its application to the adoption of approximate solutions]. Moscow, Mir Publ., 1976.
6. Khaptakhaeva N.B., Dambaeva S.V., Ayusheshva N.N. Vvedenie v teoriyu nechetkikh mnozhestv [Introduction to the theory of fuzzy sets]. Ulan-Ude, ESSTU Publ., 2004.
7. Danilova M. A. et al. [Automated accounting system of bulk food products]. Khranenie i pererabotka sel'khozsyr'ya, 2012, no. 6, pp. 63-66. (In Russ.)
8. Troitskii A. K., Blagoveshchenskii I. G. [The possibility of using images processing for quality control of confectionary]. Mat-ly mezhdunarodnoi nauch. prakt. konf. "Planirovanie i obespechenie podgotovki i perepodgotovki kadrov dlya otraslei pishchevoi promyshlennosti i meditsiny" [Proc. International scientific-practical conference "Planning and providing staff training and retraining for the food industry and medicine"]. Moscow, MSUFP, 2012, pp. 160-165. (In Russ.)
9. Blagoveshchenskaya M. M., Sulimov V. D., Shkapov P. M. [The methodology for developing the bases of modeling and diagnostics hydromechanical systems of food productions according to their dynamic characteristics]. Mat-ly XVII mezhdunarodnoi nauch. metod. konf. "Vysokie intellektual'nye tekhnologii i innovatsii v obrazovanii i nauke" [Proc. the 17th International scientific-method. conf. "High intelligent technologies and innovations in education and science]. St. Petersburg, Politechnical University, 2010, vol. 2, pp. 95-98. (In Russ.)
10. Chernorutskii I.G. Metody optimizatsii. Komp'yuternye tekhnologii [Optimization techniques. Computer technologies].St. Petersburg, BHV-Peterburg Publ., 2011. 384 p.
Authors
Nosenko Aleksei Sergeevich, Candidate of Economic Sciences,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1. This email address is being protected from spambots. You need JavaScript enabled to view it.
Bychkov Ivan Aleksandrovich, graduate,
Blagoveshchenskaya Margarita Mikhailovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated sustems
125080, Moscow, Volokolamskoe shosse, This email address is being protected from spambots. You need JavaScript enabled to view it.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N.Uh. Bauman,,department "The Theoretical Mechanics" named after professor N.E. Zhukovsky,
105005, Moscow, 2?nd Baumanskaya, 5, p. 1. This email address is being protected from spambots. You need JavaScript enabled to view it.



Petrjakov A.N., Blagoveshhenskaja M.M., Nosenko A.S. Blagoveshchenskii I.G. The use of optimization algorithms in solving of feed ration feedingproblems

P. 46-48 Key words
mixed feed; premix; feeding optimization; method; algorithm, software

Abstract
The paper deals with the optimization of the animal feed ration problems and its solving by means of optimization algorithms. The objects of the survey are the following: to produce the mathematical problem determination of the linear programming, to lead the construction of the objective function, to choose the problem solving procedure, and to work out software for the method realization. The expectations of the survey are to find a functional and un expansive solution in the field of calculation processes which provides the calculation of the balanced nutrition in animal diet with the given specification. Input data, the received recipe and the software-based realization were performed with the use of the Microsoft Excel mathematical software, and the processing was produced with the use of the built-in algorithmical language VBA (Visual Basic for Application). As a result, the optimization in the form of total deviations decrease of nutrients content in the animals ration which allows getting the better balanced ration.

References
1. Kiktev N.A. [Formulation and solution of the problem of optimizing the diet feeding of animals]. Tekhnologicheskii audit i rezervy proizvodstva, 2013, no. 6/2 (14), pp. 8-11. (In Russ.)
2. Babkov G.A. Metodika agrarno-ekonomicheskikh issledovanii [Methods of agro-economic researches ]. Kishinev, Shtil' Publ.,1995. 238p.
3. Badevits Z. Matematicheskaya optimizatsiya v sel'skom khozyaistve [Mathematical optimization in agriculture]. Ed. By R. G. Kravchenko. Moscow, Kolos Publ., 2002. 549 p.
4. Gataulin A.M., Kharitonova L. M., Gavrilov G. V. Ekonomiko-matematicheskie metody v planirovanii sel'skokhozyaistvennogo proizvodstva [Economic-mathematical methods in planning of agricultural production]. Moscow, Kolos Publ., 1996. 224p.
5. Broesch J. D. Practical Programmable Circuits: A Guide to PLDs, State Machines, and Microcontrollers. Waltham, Academic Press, 1991. 286 p.
6. Zak D. Programming with Visual Basic 6.0. Boston, Course Technology, 2001. 935 p.
7. Hawhee H., Moore T., Martins F. Programming Languages - Visual BASIC. Pietermaritzburg, Riders Publishing, 1999. 1202 ð.
8. Gorban, A.N. et al. Principal Graphs and Manifolds. Handbook of Research on Machine Learning Applications and Trends. Algorithms, Methods, and Techniques. Hershey, IGI Global, 2009, pp. 28-59.
9. Arthur D., Vassilvitvitskii S. How slow is the k-means Method. 22en ACM Symposium on Computational Geometry. Sedona, 2006.
Authors
Petrjakov Aleksandr Nikolaevich, postgraduate
Moscow State University of Food Production, department of information technology and automated systems
125080, Moscow, Volokolamskoe shosse, 11,
Blagoveshhenskaja Margarita Mihajlovna, doctor of technical Sciences, Professor,
Moscow State University of Food Production, department of information technology and automated systems,
125080, Moscow, Volokolamskoe shosse, 11.
Nosenko Sergei Candidate of technical Sciences,
Management Company "United Confectioners",
115184, Moscow, Novokuznetskaya 2 nd per., 13/15, p. 1.
Blagoveshchenskii Ivan Germanovich, doctor of technical Sciences, Professor,
Moscow state technical University. N. Uh. Bauman, department "The Theoretical Mechanics" named after professor N. E. Zhukovsky.
105005, Moscow, 2-nd Baumanskaya, 5, p. 1



ECONOMICS AND MANAGEMENT

Grigoryeva I.V. How to avoid problems when choosing a supplier for the production of flour

QUALITY AND SAFETY

Matison V.A., Arutyunova N.I., Goryacheva E.D.Application of Descriptor-Profile Method to Assess the Food Quality

P. 52-54 Key words
descriptor; quantification of the weighting of descriptors; assessors training; organoleptic tests; profile analysis; reference substances and products

Abstract
Organoleptic tests in the food industry are becoming more and more important due to the increased competition in the grocery market. Price / performance ratio is in most cases determined by solving the consumer purchase. The use of modern methods of sensory evaluation allows the manufacturer to improve the quality of products and processes as well as the sensory tests are used in the evaluation of raw materials and semi-finished and finished products. Reasonable application of sensory techniques improves efficiency and effectiveness of processes. Quantitative descriptive and profile method allows to solve many problems of food products in the field of sensory analysis. Reasonable application of sensory techniques improves efficiency and effectiveness of processes. Quantitative descriptive and profile method allows to solve many problems of food products in the field of sensory analysis. Sensory evaluation by means of descriptive and profile analysis is carried out in several stages: preparation of test selection and selection of descriptors, estimate the intensity parameter descriptor identifying the final list of descriptors, test and build the profile of the product. The reliability of the results obtained, allowing to build the profile of the product is greatly influenced by the factor of accuracy of the estimate testers intensity descriptors. It largely depends on the ability of the test to extract the complex sensations incentives that reflect the analyzed descriptor and comparing through sensory memory with sensations obtained in training with reference substances and products. Due to the importance of similarity assessment testers same intensities at different sessions of the descriptors need to pay special attention to the organization of trainings. Quantitative descriptor-profile analysis is used in various areas of sensory analysis, in particular for a comprehensive assessment of the quality of food and the development of new customer relationship-oriented products and rebranding existing products.

References
1. Kantere V.M., Matison V.A., Fomenko M.A. Sensornyi analiz produktov pitaniya: Monografiya [Sensory analysis of food products]. Moscow, RAAS Publ., 2003. 400 p.
2. Matison V.A., Edelev D.A., Kantere V.M. Organolepticheskii analiz produktov pitaniya: Uchebnik [The sensory test of food products]. Moscow, RSAU-MTAA Publ., 2010. 294p.
Authors
Matison Valeriy Arvidovich, Doctor of Technical Science, Professor,
Arutyunova Natalya Igorevna, Candidate of Technical Sciene,
Moscow State University of Food Production,
11, Volokolamskoye Shosse, Moscow, 125080, This email address is being protected from spambots. You need JavaScript enabled to view it.
Goryacheva Elena Davydovna, Candidate of Technical Science, Docent,
Russian State University of Physical Culture, Sport, Youth and Tourism,
4, Sirenevy Bulvar, Moscow, This email address is being protected from spambots. You need JavaScript enabled to view it.



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