Volume 6 • Issue 2 • 1000421 J Food Process Technol ISSN: 2157-7110 JFPT, an open access journal Open Access Research Article Birmpa et al., J Food Process Technol 2015, 6:2 DOI: 10.4172/2157-7110.1000421 *Corresonding author: Apostolos Vantarakis, Environmental Microbiology Unit, Department of Public Health, Medical School, University of Patras, GR26500 Patras, Greece, E-mail: avanta@upatras.gr Received December 23, 2014; Accepted January 30, 2014; Published February 06, 2015 Citation: Birmpa A, Vantarakis A, Anninou A, Bellou M, Kokkinos P, et al. (2015) A User-Friendly Theoretical Mathematical Model for the Prediction of Food Safety in a Food Production Chain. J Food Process Technol 6: 421. doi:10.4172/2157- 7110.1000421 Copyright: © 2015 Birmpa A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Minimally processed vegetables have recently undergone a signifcant increase in consumer demand because of their healthy image and convenience of use. Some of these products can be contaminated by microbial pathogens, as some have been implicated in an increasing number of outbreaks of foodborne illnesses. The aim of the present paper was the development of a Decision Support System (DSS) using the theory of Fuzzy Cognitive Maps (FCMs), in order to diagnose the importance of critical control points (concepts) for the food safety and hygiene during the production of salad vegetables (lettuce). The methodology described, extracts the knowledge from experts with different scientifc background and exploits their experience on the process of lettuce production. The results of this study show that software tools like Food Science Support Systems using theories of FCMs can be explored and problems that can arise during the food production chain can be prevented if the importance of some critical control points during the food production is previously indicated. A User-Friendly Theoretical Mathematical Model for the Prediction of Food Safety in a Food Production Chain Angeliki Birmpa 1 , Apostolos Vantarakis 1 *, Antigoni Anninou 2 , Maria Bellou 1 , Petros Kokkinos 1 and Peter Groumpos 2 1 Environmental Microbiology Unit, Department of Public Health, Medical School, University of Patras, GR26500, Rio, Patras, Greece 2 Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, GR26500, Rio, Patras, Greece Keywords: Decision support system experts; Food safety; Fuzzy cognitive maps; Lettuce production chain; Sofware tool Introduction Today’s society is characterized by an increasing health consciousness and growing interest in the role of food for maintaining and improving human well-being and consumer health [1,2]. Vegetables and fruits are fully recognized for their benefits towards healthy living [3], thanks to their protective function against cancer and other chronic degenerative diseases [4,5]. At present Europe is the largest importer and exporter of food products in the world, the safety of which is managed by the EU food policy, developed to support the consumer’s confdence in food safety [6]. However, food-borne outbreaks happen quite ofen. Te most common outbreaks caused by food-borne pathogens of fresh produce are mainly Cryptosporidium parvum, Listeria monocytogenes, Escherichia coli O157:H7, Salmonella spp., Clostridium botulinum, Shigella spp., Cyclospora spp., and Noroviruses [7,8]. Tese pathogens can cause infection and illness by the fecal-oral route of food contamination [9,10]. In 2006, E. coli O157:H7 infected bagged spinach causing three deaths and 102 hospitalizations [11]. In 2008, Alaska experienced a Campylobacter outbreak from peas contaminated by crane droppings, causing 99 illnesses [12]. In 2010 and 2011, Salmonella infected alfalfa sprouts, causing 125 illnesses (CDC, 2011). Escherichia coli outbreaks with lettuce occurred in 2010 and 2011 (CDC, 2010; CDC, 2011). Surprisingly, most pathogens do not originate from the food they contaminate but are transported to produce through other means, including household cross contamination [13]. Quality assurance and control measures must be in place in diferent stages of the fresh produce chain (from farm to fork). Te lettuce/leafy greens industry recognizes that once lettuce/ leafy greens are contaminated, removing or inactivate pathogens is difcult. Terefore, prevention of microbial contamination at all steps from production to distribution is strongly favored over treatments to eliminate contamination afer it has occurred. Tus, lettuce/leafy greens food safety programs should pay special attention to controlling, reducing and eliminating potential fecal contamination. Lettuce/leafy greens may be harvested mechanically or by hand and are almost always consumed uncooked or raw. In addition, lettuces are characterized by high water activity levels, which make them more vulnerable to cross- contamination. In a processing operation, the basic principles of Good Manufacture Practices (GMPs), HACCP, sanitation and documented operating procedures are commonly employed to ensure production of the safest products possible. Te complexity of food systems and the large number of “critical points” in food production chain impose the necessity of the development of mathematical models for the prediction of food safety as well as the prevention of contamination. New practices, predictive models, methods and valuable tools have been emerging as complements to decisions taken in Food Science problems. Several types of models are used ranging from qualitative (e.g., tree structure) to quantitative (e.g., microbial growth models) [14]. Prototype dynamic models which describe the growth and inactivation of a microbial population as a function of time and temperature have already been presented by Baranyi et al. and Van Impe et al. [15,16]. Predictive models (PM) can be produced within a few days, which can be augmented or modifed ensuring that PM becomes a timely tool for product development [17]. A computerized food design support system already exists for the simulation of developing and optimizing food products [18]. On the other hand, it is difcult to develop a classical automated approach due in part to: (1) many dimensions that must be taken into account in parallel, and (2) non-linearity and coupling between the variables involved in the system. Meanwhile, Decision Support Systems (DSS) in the feld of Food Science require fexibility, autonomy, intelligence, reliability but above all should be trusted by people related to Food Science. To fulfl all these Journal of Food Processing & Technology J o u r n a l o f F o o d P r o c e s s i n g & T e c h n o l o g y ISSN: 2157-7110