A note on the output of a coordinate-exchange algorithm for optimal experimental design

status: publishe

Optimizing Oxygen Input Profiles for Efficient Estimation of Michaelis-Menten Respiration Models

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Models based on mass balances and Michaelis-Menten respiration kinetics are increasingly used to determine optimal storage conditions of fresh fruits and vegetables. The model parameters are usually estimated from respiration experiments at different, but fixed, gas conditions according to a response surface design. This is a tedious procedure that requires a gas mixing facility or a series of gas cylinders with appropriate composition. In this paper, we consider a simpler approach, in which the respiration kinetics of pear fruit are modeled using a single experiment with a time-varying O 2 input profile. To optimize the information content produced by the O 2 profile, we apply optimal dynamic experimental design principles and present a modified coordinate-exchange algorithm to achieve this goal. Finally, we demonstrate the added value of our approach by comparing the optimal O 2 input profiles to several intuitive benchmark experiments.status: publishe

Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables

Dynamic models based on non-linear differential equations are increasingly being used in many biological applications. Highly informative dynamic experiments are valuable for the identification of these dynamic models. The storage of fresh fruit and vegetables is one such application where dynamic experimentation is gaining momentum. In this paper, we construct optimal O(2) and CO(2) gas input profiles to estimate the respiration and fermentation kinetics of pear fruit. The optimal input profiles, however, depend on the true values of the respiration and fermentation parameters. Locally optimal design of input profiles, which uses a single initial guess for the parameters, is the traditional method to deal with this issue. This method, however, is very sensitive to the initial values selected for the model parameters. Therefore, we present a robust experimental design approach that can handle uncertainty on the model parameters

SciML/DifferentialEquations.jl: v7.10.0

DifferentialEquations v7.10.0 Diff since v7.9.1 Merged pull requests: Bump actions/checkout from 3 to 4 (#982) (@dependabot[bot]) CompatHelper: bump compat for SciMLBase to 2, (keep existing compat) (#984) (@github-actions[bot]) Closed issues: 22 seconds to 3 and now more: Let's fix all of the DifferentialEquations.jl + universe compile times! (#786