Microbiological quality and safety of minimally processed fruits in the marketplace of southern Portugal

The availability of fresh-cut fruit (FCF) in the marketplace has been increasing in Portugal, although reports of its microbial quality are not known. Due to the growing concerns of these commodities over their microbial safety, the objectives of this work were to study the microbiological quality and prevalence of Salmonella and Listeria monocytogenes on fresh-cut fruits sold in southern Portugal. A study to examine the changes in pH and microbial counts, before and after the expiration dates, was also made. A total of 160 samples was purchased in the local grocery stores between September 2011 and August 2014, before their sell-by date. These samples were assayed for aerobic mesophilic (AM) and psychrotrophic (AP) microorganisms, yeasts and molds (YM), lactic-acid bacteria (LAB), coliforms (TC), Escherichia coli and coagulase positive staphylococci as well as L. monocytogenes and Salmonella. The microbiological counts ranged from 3.0-9.2 lg cfu/g (AM); 2.2–10.7 lg cfu/g (AP); 2.3–10.4 lg cfu/g (YM); 1.9–9.0 lg cfu/g (LAB) and less than 1–9.1 lg cfu/g (TC). The melons and watermelon presented the highest levels of the microbial quality parameters studied. However, no E. coli, staphylococci, Salmonella and L. monocytogenes were detected in any of the samples. After the sell-by date, an increase of the AM, AP, LAB and YM values was observed in all fruits. Conversely, the differences found in TC counts before and after the best-before date had no statistical significance. A decrease in pH was observed in all fruits except pineapple whose pH slightly increased after 14 days of storage. The results highlight the importance of preventing contamination and cross contamination, selecting adequate decontamination technologies and maintaining a strict temperature control during processing, distribution and selling of FCF.

Intelligent predictive control for thermal comfort and energy savings in public buildings

This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.