Open AccessMathematical Modeling of the Film Influence on the Salting Time of Mozzarella Cheese in a Static and Dynamic System: Application of Artificial Neural Networks of the Multilayer Perceptron Type
Author(s) -
Dionísio Borsato,
Winnicius de Souza,
Talita de Oliveira,
Marco A. J. Clemente,
Hágata Silva,
Ana Carolina Gomes Mantovani,
Leticia Thaís Chendynski,
Karina Gomes Angilelli
Publication year - 2022
Publication title -
journal of the brazilian chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 70
eISSN - 1678-4790
pISSN - 0103-5053
DOI - 10.21577/0103-5053.20210128
Subject(s) - artificial neural network , multilayer perceptron , salting , sample (material) , biological system , mathematics , computer science , materials science , artificial intelligence , statistics , chemistry , chromatography , food science , biology
The NaCl and KCl diffusion in the film formed on the cheese surface during salting was simulated by the finite element method. The time and salts concentration values on the cheese surface were determined, tabulated, and presented to the multilayer perceptron neural network (MLP) for the regression modeling. The samples were divided into 70, 15 and 15% for training, testing, and validation, respectively. The networks with the best performance showed 5 to 12 hidden layers. The Tukey’s test showed that there was no significant difference, at the 5% level, between the time value used and the mean value modeled for training, testing, and validation for the NaCl. For the KCl, a significant difference was observed only for 2 training samples and 1 test sample. Sensitivity analysis showed that the discrete variable Z, which represents the static and dynamic systems, was the most important in the models’ construction.