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Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease.
de Jesus, Cléber Gomes; da Rocha Rodrigues, Rebeca; da Silva, Carlos Alexandre Moreira; Péres, Laura Oliveira.
Afiliação
  • de Jesus CG; Laboratory of Hybrid Materials, Federal University of São Paulo, Diadema, SP, Brazil.
  • da Rocha Rodrigues R; Laboratory of Hybrid Materials, Federal University of São Paulo, Diadema, SP, Brazil.
  • da Silva CAM; Laboratory of Multiphasic Processes, Federal University of São Paulo, Diadema, SP, Brazil.
  • Péres LO; Laboratory of Hybrid Materials, Federal University of São Paulo, Diadema, SP, Brazil. laura.peres@unifesp.br.
Anal Bioanal Chem ; 416(5): 1217-1227, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38180497
ABSTRACT
Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12 mM, 22 mM, and 32 mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Urease / Técnicas Biossensoriais Tipo de estudo: Prognostic_studies Idioma: En Revista: Anal Bioanal Chem Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Urease / Técnicas Biossensoriais Tipo de estudo: Prognostic_studies Idioma: En Revista: Anal Bioanal Chem Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Alemanha