Title: Assessing the optimal experiment setup for first order kinetic studies by Monte Carlo analysis
Authors: Poschet, Filip ×
Geeraerd, Annemie
Van Loey, Ann
Hendrickx, Marc
Van Impe, Jan #
Issue Date: Dec-2005
Publisher: Butterworths
Series Title: Food Control vol:16 issue:10 pages:873-882
Conference: date:Katholieke Univ Leuven, Dept Chem Engn, BioTeC Bioproc Technol & Control, B-3001 Heverlee, Belgium; Catholic Univ Louvain, Lab Food Technol, Dept Food & Microbial Technol, B-3001 Heverlee, Belgium
Abstract: In inactivation studies of microorganisms and quality influencing enzymes a log linear relation between dependent and independent variables, generally denominated as a first order kinetic, is frequently encountered. Reliable application of a kinetic model to predict inactivation requires a proper quantification of the variation on the model parameters. The aim of the present research is the assessment of the most optimal experiment setup leading to first order kinetic parameters with minimal variation, and, by consequence, to model predictions with minimal variation. As a vehicle for this research, the first order inactivation of pectin methyl esterase (PME), commonly encountered in fruits, is considered. Based on a bootstrap assessment of the PME activity measurement variation, a Monte Carlo analysis fully reveals the optimal experiment setup and leads to two important conclusions, valid for all first order kinetic Studies. First, if the logarithm of the dependent variable has a constant variance as function of the independent variable, the optimal sampling scheme is a 50-50 division at the two extremes of the independent variable range. It is indicated how this relates with classical linear regression analysis. Second, if the logarithm of the dependent variable has a non-constant variance, this variance should be fully characterized and the optimal sampling scheme should be obtained via Monte Carlo analysis. It is shown how, in such a case, a 50-50 division is not necessarily the most optimal. (c) 2004 Elsevier Ltd. All rights reserved.
Description: [**]
ISSN: 0956-7135
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Bio- & Chemical Systems Technology, Reactor Engineering and Safety Section
Division of Mechatronics, Biostatistics and Sensors (MeBioS)
× corresponding author
# (joint) last author

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