Analysis of the risk management information for the hazards in meat and meat products

Abstract

The world is confronted with the challenge to feed an estimated 9 billion population of the earth by 2050. The safety of food is a critical health, social and economic issue. According to World Health Organization (WHO) food-borne diseases kill an estimated 2.2 million people annually. Food-borne diseases, caused by pathogens, natural toxins, and chemical contaminants, remain a global public health challenge, since new threats are continuously emerging while others are being controlled. Also, in many countries, the consumption of food prepared outside the home increases the exposure to the risks posed by poor hygiene in food service. The purpose of this study was to analyze and correlate national data concerning various descriptive variables within the RASFF system for the period between 1980 till the 25th of September 2019. By analyzing these data trends were revealed and what this study also attempted was to suggest ideas for the scientific community that could be adopted in new early warning systems for emerging food borne hazards. The methodology of the research work included analysis of the RASFF data and analysis of case studies on hypothetical or actual scenarios. The related data has been collected from the RASFF portal and was estimated to be a few thousands of notifications. Furthermore, data mining techniques have been applied in order to identify frequent cases and association rules among them. Specific details like the subject of notification or the distribution have been analyzed with text analysis software (boolean logic) in order to identify frequent keywords for hazards, food products, causes and countries. Some particular case studies have been elaborated individually such as the adulteration of bovine meat with horse meat, outbreaks of E. coli O157, Salmonella spp or other pathogens, the bovine spongiform encephalopathy and others. Test analysis with qPCR was carried out in order to identify the essential detection of horse meat in some prompt mixture that have been prepared for the horse meat adulteration. Association trees between the datasets may point out possible or least possible scenarios that may be used for drills of risk management. The results of the study, based on the reports of the RASFF system identified that the vast majority of initial notifications concerned food in all groups of research area, focuses more on meat and meat products with the most common type of notification in both categories is that of alert.