Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation

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• 作者：Eva Balsa-Canto ; Maria Rodriguez-Fern ; ez ; Julio R. Banga
• 关键词：Optimal experimental design ; Thermal processing ; Thermal degradation kinetics ; Parameter estimation
• 刊名：Journal of Food Engineering
• 出版年：2007
• 出版时间：September 2007
• 年：2007
• 卷：82
• 期：2
• 页码：178-188
• 全文大小：273 K

文摘

Thermal processing is widely used for ensuring food safety and extended shelf life. However, standard methods of thermal processing have a significant impact on food quality due to thermal degradation of nutrients and other quality factors. Model-based methods can be successfully used for thermal process design, optimization and control. However, building sound models requires suitable estimation of the unknown kinetic parameters. Further, the accuracy of these estimates will largely depend on the quality and quantity of the available experimental data.

Optimal experimental design (OED) of dynamic experiments allows for the calculation of the scheme of controls and measurements which improve the estimation of model parameters. In this contribution, the OED problem is formulated as a general dynamic optimization problem where the objective is to find those experimental conditions which result in maximum information content, as measured by the Fisher information matrix. The numerical solution of this problem is then approached using a combination of the control vector parameterization approach with a non-linear global optimization solver.

As an illustrative application, we consider the optimal experimental design for the parameter estimation of the thiamine degradation kinetic parameters during the thermal processing of canned tuna. Results confirm that the use of optimal dynamic experiments not only improves identifiability but also results in reduced confidence regions for the parameters (a maximum error of the 2 % in the parameter estimates), substantially decreasing the experimental effort (up to a 50 % ). Particularly the use of six optimally designed experiments results in a 30 % reduction of the confidence regions with respect to previously published results using 10 typical experiments.