The initial freezing temperature of foods at high pressure

被引:14
作者
Guignon, B. [1 ]
Torrecilla, J. S. [2 ]
Otero, L. [1 ]
Ramos, A. M. [3 ]
Molina-Garcia, A. D. [1 ]
Sanz, P. D. [1 ]
机构
[1] CSIC, Inst Frio, Dept Engn, Malta Consolider Team, E-28040 Madrid, Spain
[2] Univ Complutense Madrid, Dept Chem Engn, E-28040 Madrid, Spain
[3] Univ Complutense Madrid, Fac Matemat, Dept Appl Math, E-28040 Madrid, Spain
关键词
hydrostatic pressure; high pressure freezing/thawing processes; freezing/ melting point; (P; T)-phase diagram modelling; artificial neural network;
D O I
10.1080/10408390701347736
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
The 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 0.4C. 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.
引用
收藏
页码:328 / 340
页数:13
相关论文
共 74 条
[2]  
BARRY H, 1998, HIGH PRESSURE FOOD S, P343
[3]  
Bell LN., 2000, Moisture Sorption - Practical Aspects of Isotherm Measurement and Use, V2nd
[4]  
Benet G. U., 2004, Innovative Food Science & Emerging Technologies, V5, P413, DOI 10.1016/j.ifset.2004.06.001
[5]   USE OF NEURAL NETS FOR DYNAMIC MODELING AND CONTROL OF CHEMICAL PROCESS SYSTEMS [J].
BHAT, N ;
MCAVOY, TJ .
COMPUTERS & CHEMICAL ENGINEERING, 1990, 14 (4-5) :573-583
[6]   DYNAMICS AT THE SOLID LIQUID TRANSITION - EXPERIMENTS AT THE FREEZING-POINT [J].
BILGRAM, JH .
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 1987, 153 (01) :1-89
[7]   Thermal properties estimation during thawing via real-time neural network learning [J].
Boillereaux, L ;
Cadet, C ;
Le Bail, A .
JOURNAL OF FOOD ENGINEERING, 2003, 57 (01) :17-23
[8]   Water, in the liquid and five solid forms, under pressure [J].
Bridgman, PW .
PROCEEDINGS OF THE AMERICAN ACADEMY OF ARTS AND SCIENCES, 1912, 47 (13/22) :441-558
[9]  
CHEN CS, 1988, LEBENSM WISS TECHNOL, V21, P256
[10]  
Chevalier D, 1999, FOOD SCI TECHNOL-LEB, V32, P25