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By using Box Behnken design, response surface methodology was applied to optimize the process and a well fitting model was obtained with PEF parameters, voltage, frequency, pulse width, and residence time.
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Well-fitting models were successfully generated for the responses of free fatty acids (R2 = 0.9954) and peroxide value (R2 = 0.9365) with the probability value less than 0.0001, which demonstrated a high significance for the regression models.
The use of an improved optimisation approach based on MCMC and Bayesian techniques provided increased confidence than in previous analyses that the best fitting model was obtained, as well as allowed a more thorough assessment of model adequacy and model parameters to be made.
A pure diffusion fitting model was employed.
The worst fitting model was (d), the unbalanced input noise model.
The best fitting model was selected based on the AIC.
Our best fitting model is described in table 6.
In this case, the bad fitting model is rejected.
The best fitting model is printed in bold font.
Our fitting model is described by the following optimization problem.
For each model, the difference between its AIC score (AIC) and that of the best-fitting model was calculated as well as the Akaike weight.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com