Modeling of Enzymatic Hydrolysis of Whey Proteins

The aim of this work was to emphasize the limitations of modeling complex phenomena under unrealistic model assumptions. As a case study, the whey protein hydrolysis mechanism was modeled. A stirred batch reactor was used to study the degree of hydrolysis of sweet whey protein concentrate by using t...

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Bibliographic Details
Published in:Food and bioprocess technology 2012-08, Vol.5 (6), p.2596-2601
Main Authors: Martínez-Araiza, Gabriela, Castaño-Tostado, Eduardo, Amaya-Llano, Silvia L., Regalado-González, Carlos, Martínez-Vera, Carlos, Ozimek, Lech
Format: Article
Language:English
Subjects:
Agriculture
Batch reactors
Biotechnology
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Design of experiments
Experimental design
Food Science
Hydrolysis
Modelling
Original Paper
Parameter estimation
Parameter identification
Proteins
Regression models
Substrates
Subtilisin
Whey
Whey protein
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Summary:The aim of this work was to emphasize the limitations of modeling complex phenomena under unrealistic model assumptions. As a case study, the whey protein hydrolysis mechanism was modeled. A stirred batch reactor was used to study the degree of hydrolysis of sweet whey protein concentrate by using the protease alcalase. A completely random two-factorial experimental design was used, three levels of initial enzyme concentrations ( E 0 ) (1.58, 3.18, 6.36 AU (Anson units)/L) times three levels of initial substrate concentrations ( S 0 ) (18.73, 38.45, 81.16 g/L). All treatments were carried out at optimal alcalase—activity conditions: pH 8 and 50 °C. Reactions were monitored for 180 min. The degree of hydrolysis ( h ) curves was finally adjusted for each treatment to the exponential model using nonlinear regression techniques but not assuming a Michaelis–Menten relationship. From the estimation process, the coefficient b was constant (27.26 ± 1.37) and independent of E 0 and S 0 , while coefficient a depended directly on the ratio E 0 / S 0 , ranging from 0.0017 to 0.0497. A noncritical strategy of forward modeling based on unrealistic assumptions was misleading in the face of complex phenomena; instead, a modeling strategy moving from data to the identification and estimation of parameters of practical interest must be considered.
ISSN:1935-5130
1935-5149
DOI:10.1007/s11947-011-0624-5