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Alternative food preservation technologies include substitutes to heating methods that may have benefits that include reduction of energy consumption. High-pressure processing (HPP), membrane filtration (MF), pulsed electric fields (PEF), and ultraviolet radiation (UV) are examples of alternative preservation technologies of growing commercial interest. As unit operations these technologies operate in 4 modes of energy transfer: momentum, heat, electromagnetic, or photon transfer. The objectives of this review were: (1) to examine the fundamentals of energy requirements of 4 alternative food processing technologies such as HPP, MF, PEF, UV, and conventional high-temperature short-time (HTST) processing, (2) to establish a basis for comparison of energy consumption between or within technologies, and (3) to evaluate specific energy requirements for the 5 technologies to achieve required safety performance in apple juice. Three levels of energy evaluation for each technology including internal energy, applied energy, and consumed energy were reviewed. The comparison of the specific energy for the 5 technologies was based on information published in scientific papers where the inactivation of Escherichia coli in apple juice was explored. Based on the analysis of energy consumption of these technologies it was concluded that MF and UV have the potential to consume less specific energy than HTST, PEF, and HPP. Differences in energy consumption within each group of technologies were also observed and these could be attributed to differences in the systems. The differences in energy consumption within each group of technologies illustrate that there is potential of improvement in most technologies.

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Prior to processing milk and cream were standardised and homogenised. Skim milk was cross-flow microfiltered (CFMF) prior to treatment with pulsed electric fields (PEF) or high temperature short time (HTST) pasteurization. The effect of temperature of the skim milk and product composition on the efficacy of PEF treatment was determined. The electrical conductivity of the product was related to fat and solids content and increased 5% for every g/kg increase of solids and decreased by nearly 0·7% for every g/kg increase of fat. From the three microbial groups analyzed (mesophilic, coliform, and psychrotroph) in milks differences (P<0·05) in the inactivation of mesophilic microorganisms were observed between the counts following PEF treatment, while HTST pasteurization resulted in higher reductions in all different counts than those obtained after PEF. Increasing the skim milk temperature prior to PEF treatment to about 34°C showed equivalent reductions in microbial counts to skim milk treated at 6°C in half the time. The reductions achieved by a combination of CFMF and PEF treatments were comparable to those achieved when CFMF was combined with HTST pasteurization. A higher reduction in coliform counts was observed in homogenised products subjected to PEF than in products that were only standardised for fat content.

The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance in astrophysics. However the computational time is also important because the study of  large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network  architecture that we use is general Regression Neural Network (GRNN). For the parallel approach, we use Message Passing Interface (MPI) on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA™) language on Graphics Processing Units (GPUs). We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state-of-the-art methods.