Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

ABSTRACT: The present work modelled the enzymatic hydrolysis of red tilapia (Oreochromis spp.) viscera with Alcalase® 2.4 L in both 0.5 and 5 L reactors. The best conditions for the enzymatic hydrolysis were 60°C and pH 10. The product inhibited the enzymatic hydrolysis, and the enzyme deactivated f...

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Autores:
Álvarez Montoya, Andrés Camilo
Sepúlveda Rincón, Cindy Tatiana
Zapata Montoya, José Edgar
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/39527
Acceso en línea:
https://hdl.handle.net/10495/39527
Palabra clave:
Redes Neurales de la Computación
Neural Networks, Computer
Cinética
Kinetics
Tilapia
Hidrólisis enzimática
Enzymatic hydrolysis
Oreochromis
http://aims.fao.org/aos/agrovoc/c_27512
http://aims.fao.org/aos/agrovoc/c_26596
https://id.nlm.nih.gov/mesh/D016571
https://id.nlm.nih.gov/mesh/D007700
https://id.nlm.nih.gov/mesh/D017210
Rights
openAccess
License
Derechos reservados - Está prohibida la reproducción parcial o total de esta publicación
Description
Summary:ABSTRACT: The present work modelled the enzymatic hydrolysis of red tilapia (Oreochromis spp.) viscera with Alcalase® 2.4 L in both 0.5 and 5 L reactors. The best conditions for the enzymatic hydrolysis were 60°C and pH 10. The product inhibited the enzymatic hydrolysis, and the enzyme deactivated following second-order reaction. KM and Kp from a secondary plot of KM app as a function of inhibitor concentration, and k2, p, and k3 were found by non-linear regression. While the obtained parameters modelled the 0.5 L reactor well, it did not model the 5 L reactor, probably because of unconsidered fluid dynamics in the model. To have a better modelling, a neural network (tensorflow.keras.models module) was built and trained. The neural network modelled the enzymatic hydrolysis of red tilapia at several concentrations of substrate and enzyme. This result proved that neural networks are a powerful tool for modelling biological processes.