Identification of fatty acids from açai in mistures subjected to crossflow microfiltration using Artificial Neural Networks

Authors

DOI:

https://doi.org/10.21167/cqdv25e25003

Keywords:

Redes neurais artificiais, Ácidos graxos, Açaí, Microfiltração tangencial

Abstract

This article proposes the use of Artificial Neural Networks as an alternative tool to classify mixtures of fatty acids from açai subjected to crossflow microfiltration. The neural networks were trained with the Levenberg-Marquardt and Bayesian Regularization supervised learning algorithms, using the software MATLAB and datasets from literature, related to pressure, flow speed, time and transmembrane flow rate. The evaluation criteria were consists on analyzing the confusion matriz, the error histogram and the performance graphics. The neural network that used the Bayesian Regularization had 99,1\% accuracy for the mixtures with water/oleic acid and water/palmitic acid and also 96,5\% accuracy when a third class was added to the datasets, containing water and both the fatty acids, showing the validity of the tool.

Author Biographies

Matheus Nonis Passerini, UNESP, Instituto de Química

Student at the Universidade Estadual Paulista "Júlio de Mesquita Filho" - Araraquara Campus for the Chemical Engineering course, Brazilian, 20 years old and main focus of work in the area of ​​mathematics applied to Engineering.

Érica Regina Filletti, Unesp, Instituto de Química

She holds a Bachelor's degree in Mathematics from the University of São Paulo (1999), a Master's degree in Mechanical Engineering from the University of São Paulo (2002) and a PhD in Mechanical Engineering from the University of São Paulo (2007). She is currently an assistant professor at the Universidade Estadual Paulista Júlio de Mesquita Filho, UNESP in Araraquara. He has experience in Multiphase Flows, with an emphasis on numerical solution of Partial Differential Equations, in Artificial Neural Networks applied to Chemistry, Physics and Engineering problems and also in Mathematics Teaching.  

Published

2025-07-07

How to Cite

PASSERINI, M. N.; FILLETTI, Érica R. Identification of fatty acids from açai in mistures subjected to crossflow microfiltration using Artificial Neural Networks. C.Q.D. - Revista Eletrônica Paulista de Matemática, Bauru, v. 25, p. e25003, 2025. DOI: 10.21167/cqdv25e25003. Disponível em: https://sistemas.fc.unesp.br/ojs/index.php/revistacqd/article/view/472. Acesso em: 11 jul. 2025.

Issue

Section

Edição Especial ERMAC 2024