Determination of microbial contamination sources for use in quality management of cheese industry: “Dil” cheese as an example

The microbiological quality, safety and shelf-life of cheeses depend on manufacture and handling in an environment that meets basic standards for hygiene and the management of hygiene in the process. In this research contamination sources of “Dil” cheese during production in a local dairy plant in Bursa, Turkey were determined. Eighteen different control points (raw milk, pasteurized milk, heated curd, molded cheese before kneading, kneaded cheese, brine solution for kneading, thermophilic culture, rennet, calcium chloride solution, brine solution for cheese, cheese vat, workers hands, production room air, production room floor, production room wall, packaging material and packaged cheese) have been examined for the enumeration of total aerobic mesophilic bacteria, Staphylococci, Enterobacteriaceae, Salmonella spp., Escherichia coli, lactic acid bacteria, Pseudomonas spp. and yeast-moulds. It was determined that viability of lactic acid bacteria in thermophilic culture was not in high numbers and some contaminations to “Dil” cheese were detected from the starter culture. Brine solutions and rennet were contaminated with Staphylococci. Yeast and moulds in production room air were the major sources of contamination. Pasteurization and kneading in hot brine solution can eliminate some of the microorganisms but that was not sufficient in the production of Dil cheese. Finish cheese should meet specific hygienic standards, with respect to regulations post-contaminations to the cheese must be inhibited and a HACCP plan should be established during production.     

0.1% Nepafenac reduces pain and increases patient comfort during cataract surgery

Purpose

To evaluates analgesic effectiveness of 0.1% nepafenac during cataract surgery.

A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method

Raman spectroscopy and the principal component analysis (PCA) were successfully applied to differentiate the origin of the meat and meat products based on their extracted fat samples in the present study. A total of 132 extracted fat samples that were obtained from different meat species (cattle, sheep, pig, fish, poultry, goat and buffalo) and their salami products were examined based on their Raman measurements. Then, the collected Raman data were analyzed with a four-stage PCA method. Seven meat species and their salami products were successfully differentiated from each other according to their origin. The results of this study showed that the Raman spectroscopy with a chemometric method can be used to detect the origin of the meat species in a very short analysis time (30 s) after fat extraction, without requiring complicated chromatographic, immunologic or genetic methods and instruments as well as trained personnel.     

Microbial desulphurization of lignites by a thermophilic bacterium

As a possible means of abatement of air pollution the use of thermophilic thiobacilli in lignite desulphurization has been investigated in lab-scale batch experiments and 50% organic sulphur 58% total sulphur removal has been achieved in 10% lignite slurries at 50 °C.     

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.