Pet food: quality and quality improvement

Today’s pet food industry is growing rapidly, with pet owners demanding high-quality diets for their pets. The primary role of diet is to provide enough nutrients to meet metabolic requirements, while giving the consumer a feeling of well-being. Diet nutrient composition and digestibility are of crucial importance for health and well being of animals. A recent strategy to improve the quality of food is the use of “nutraceuticals” or “Functional foods”. At the moment, probiotics and prebiotics are among the most studied and frequently used functional food compounds in pet foods. The present thesis reported results from three different studies. The first study aimed to develop a simple laboratory method to predict pet foods digestibility. The developed method was based on the two-step multi-enzymatic incubation assay described by Vervaeke et al. (1989), with some modification in order to better represent the digestive physiology of dogs. A trial was then conducted to compare in vivo digestibility of pet-foods and in vitro digestibility using the newly developed method. Correlation coefficients showed a close correlation between digestibility data of total dry matter and crude protein obtained with in vivo and in vitro methods (0.9976 and 0.9957, respectively). Ether extract presented a lower correlation coefficient, although close to 1 (0.9098). Based on the present results, the new method could be considered as an alternative system of evaluation of dog foods digestibility, reducing the need for using experimental animals in digestibility trials. The second parte of the study aimed to isolate from dog faeces a Lactobacillus strain capable of exert a probiotic effect on dog intestinal microflora. A L. animalis strain was isolated from the faeces of 17 adult healthy dogs..The isolated strain was first studied in vitro when it was added to a canine faecal inoculum (at a final concentration of 6 Log CFU/mL) that was incubated in anaerobic serum bottles and syringes which simulated the large intestine of dogs. Samples of fermentation fluid were collected at 0, 4, 8, and 24 hours for analysis (ammonia, SCFA, pH, lactobacilli, enterococci, coliforms, clostridia). Consequently, the L. animalis strain was fed to nine dogs having lactobacilli counts lower than 4.5 Log CFU per g of faeces. The study indicated that the L animalis strain was able to survive gastrointestinal passage and transitorily colonize the dog intestine. Both in vitro and in vivo results showed that the L. animalis strain positively influenced composition and metabolism of the intestinal microflora of dogs. The third trail investigated in vitro the effects of several non-digestible oligosaccharides (NDO) on dog intestinal microflora composition and metabolism. Substrates were fermented using a canine faecal inoculum that was incubated in anaerobic serum bottles and syringes. Substrates were added at the final concentration of 1g/L (inulin, FOS, pectin, lactitol, gluconic acid) or 4g/L (chicory). Samples of fermentation fluid were collected at 0, 6, and 24 hours for analysis (ammonia, SCFA, pH, lactobacilli, enterococci, coliforms). Gas production was measured throughout the 24 h of the study. Among the tested NDO lactitol showed the best prebiotic properties. In fact, it reduced coliforms and increased lactobacilli counts, enhanced microbial fermentation and promoted the production of SCFA while decreasing BCFA. All the substrates that were investigated showed one or more positive effects on dog faecal microflora metabolism or composition. Further studies (in particular in vivo studies with dogs) will be needed to confirm the prebiotic properties of lactitol and evaluate its optimal level of inclusion in the diet.

Ethical Perspectives on Big Data in Agri-food: Ownership and Governance for Safety

Big data are reshaping the way we interact with technology, thus fostering new applications to increase the safety-assessment of foods. An extraordinary amount of information is analysed using machine learning approaches aimed at detecting the existence or predicting the likelihood of future risks. Food business operators have to share the results of these analyses when applying to place on the market regulated products, whereas agri-food safety agencies (including the European Food Safety Authority) are exploring new avenues to increase the accuracy of their evaluations by processing Big data. Such an informational endowment brings with it opportunities and risks correlated to the extraction of meaningful inferences from data. However, conflicting interests and tensions among the involved entities - the industry, food safety agencies, and consumers - hinder the finding of shared methods to steer the processing of Big data in a sound, transparent and trustworthy way. A recent reform in the EU sectoral legislation, the lack of trust and the presence of a considerable number of stakeholders highlight the need of ethical contributions aimed at steering the development and the deployment of Big data applications. Moreover, Artificial Intelligence guidelines and charters published by European Union institutions and Member States have to be discussed in light of applied contexts, including the one at stake. This thesis aims to contribute to these goals by discussing what principles should be put forward when processing Big data in the context of agri-food safety-risk assessment. The research focuses on two interviewed topics - data ownership and data governance - by evaluating how the regulatory framework addresses the challenges raised by Big data analysis in these domains. The outcome of the project is a tentative Roadmap aimed to identify the principles to be observed when processing Big data in this domain and their possible implementations.