Serpa cheese: technological, biochemical and microbiological characterisation of a PDO ewe's milk cheese coagulated with Cynara cardunculus L

Portugal has a strong tradition of cheesemaking from raw ewe's milk; most of these cheeses are still made on a traditional farmhouse scale. Their production is protected by Protected Designation of Origin (PDO) but the specific biochemical aspects of the majority still need to be characterised. Two different cheesemaking procedures, traditional and semi-industrial, were compared technologically, biochemically and microbiologically. It was observed that, despite the highly significant difference between artisanal and semi-industrial cheeses (P < 0.001), both products were within the limits of national regulations for most parameters except maturation temperature, humidity and the value for the maturation index. Although the present study was not fully representative of the region, the results obtained suggest that the specific regulations for Serpa cheese should be revised and that other parameters, such as moisture and salt-in-moisture content, which are very much dependent on the cheesemaking process, should be included in order to characterise better this traditional cheese.

A concept of approximated densities for efficient nonlinear estimation

This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.

Motion estimation through approximated densities

Many techniques are currently used for motion estimation. In the block-based approaches the most common procedure applied is the block-matching based on various algorithms. To refine the motion estimates resulting from the full search or any coarse search algorithm, one can find few applications of Kalman filtering, mainly in the intraframe scheme. The Kalman filtering technique applicability for block-based motion estimation is rather limited due to discontinuities in the dynamic behaviour of the motion vectors. Therefore, we propose an application of the concept of the filtering by approximated densities (FAD). The FAD, originally introduced to alleviate limitations due to conventional Kalman modelling, is applied to interframe block-motion estimation. This application uses a simple form of FAD involving statistical characteristics of multi-modal distributions up to second order.