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dc.contributor.authorGuignon, Bérengère-
dc.contributor.authorTorrecilla, J. S.-
dc.contributor.authorOtero, Laura-
dc.contributor.authorRamos, A. M.-
dc.contributor.authorMolina García, Antonio D.-
dc.contributor.authorSanz Martínez, Pedro D.-
dc.identifierdoi: 10.1080/10408390701347736-
dc.identifierissn: 1040-8398-
dc.identifier.citationCritical Reviews in Food Science and Nutrition 48: 328- 340 (2008)-
dc.description.abstractThe Pure water (P,T)-phase diagram is known in the form of empirical equations or tables from nearly a century as a result of Bridgman's work. However, few data are available on other aqueous systems probably due to the difficulty of high-pressure measurements. As an alternative, six approaches are presented here to obtain the food phase diagrams in the range of pressure 0.1-210 MPa. Both empirical and theoretical methods are described including the use of an artificial neural network (ANN). Experimental freezing points obtained at the laboratory of the authors and from literature are statistically compared to the calculated ones. About 400 independent freezing data points of aqueous solutions, gels, and foods are analysed. A polynomial equation is the most accurate and simple method to describe the entire melting curve. The ANN is the most versatile model, as only one model allows the calculation of the initial freezing point of all the aqueous systems considered. Robinson and Stokes' equation is successfully extended to the high pressures domain with an average prediction error of 0.4°C. The choice of one approach over the others depends mainly on the availability of experimental data, the accuracy required and the intended use for the calculated data. Copyright © Taylor and Francis Group, LLC.-
dc.description.sponsorshipThis work has been supported by the “National Plan of Spanish I+D+I” of the Ministry of Education and Science (MEC) through the projects CSIC 200550F0191,AGL2007-63314/ALI, MALTA CONSOLIDER-INGENIO 2010 CSD2007-00045, MTM2007-64540 and CSD2006-00032/FUT-C2-0044 and from the ‘Comunidad de Madrid (CAM)’ and UCM through the CCG07-UCM/ESP-2787 project. Also by the “IV PRICIT(2005–2008) CAM” through the 200670M060 project-
dc.publisherTaylor & Francis-
dc.subjectHydrostatic pressure-
dc.subjectHigh pressure freezing/thawing processes-
dc.subjectArtificial neural network-
dc.subjectPT phase diagram-
dc.subjectFreezing/melting point-
dc.titleThe initial freezing temperature of foods at high pressure-
dc.description.versionPeer Reviewed-
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