Document Type
Article
Publication Date
9-19-2022
Abstract
Thiols (cysteine and glutathione) were explored as potential decolorization agents to mitigate green pigment formation in chlorogenic acid quinone-lysine solutions. Reparameterizations of the Weibull cumulative distribution function were applied to describe the time-dependence of greening under varying pH conditions. Repeated fitting of 3-parameter models (RMSE = 0.0111, CVRMSE = 1.55%) indicated the linear dependence of model parameters on thiol concentration. A 6-parameter Weibull model incorporating time and initial thiol concentration (RMSE = 0.0255, CVRMSE = 3.56%) accurately predicted green color development. Calculated model parameters descriptive of greening rate, terminal greening magnitude, and lag time before greening onset facilitated comparison of the relative effectiveness of each thiol based on concentration. Bayesian regression of the model yielded nearly similar predictive power (RMSE = 0.0272, CVRMSE = 3.80%). Glutathione yielded longer lag time durations at both pH 8.0 and 9.0 but yielded less green solutions only at pH 9.0.
Recommended Citation
Charles Taylor Drucker, Lilian Were Senger, Criselda Toto Pacioles. 2023. Application of the Weibull model to describe the kinetic behaviors of thiol decolorizers in chlorogenic acid-lysine solutions, Journal of Food Engineering, 339,111287, ISSN 0260-8774, https://doi.org/10.1016/j.jfoodeng.2022.111287.
Peer Reviewed
1
Copyright
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Food Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Food Engineering, volume 339, in 2022. https://doi.org/10.1016/j.jfoodeng.2022.111287
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