Comparison of Response Surface Methodology and Artificial Neural Network for Modeling Xylose-to-Xylitol Bioconversion
Fábio Coelho Sampaio
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorJanaína Teles de Faria
Institute of Agricultural Sciences, Federal University of Minas Gerais, Av. Universitária, n. 1000 – Bairro Universitário, 39400-401 Montes Claros, Minas Gerais, Brazil.
Search for more papers by this authorGabriel Dumond de Lima Silva
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorRicardo Melo Gonçalves
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorCristiano Grijó Pitangui
Department of Technology and Civil Engineering, Computation, Humanities, Federal University of São João Del-Rei, Campus Alto Paraopeba, Rod.: MG 443, Km 7, 36420-000, Ouro Branco, Minas Gerais, Brazil.
Search for more papers by this authorAlessandro Alberto Casazza
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.
Search for more papers by this authorSaleh Al Arni
Department of Chemical Engineering, Kind Saudi University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
Search for more papers by this authorCorresponding Author
Attilio Converti
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.Search for more papers by this authorFábio Coelho Sampaio
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorJanaína Teles de Faria
Institute of Agricultural Sciences, Federal University of Minas Gerais, Av. Universitária, n. 1000 – Bairro Universitário, 39400-401 Montes Claros, Minas Gerais, Brazil.
Search for more papers by this authorGabriel Dumond de Lima Silva
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorRicardo Melo Gonçalves
Department of Pharmacy, Federal University of Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, Km 583, n. 5000 – Alto da Jacuba, 39100-000 Diamantina, Minas Gerais, Brazil.
Search for more papers by this authorCristiano Grijó Pitangui
Department of Technology and Civil Engineering, Computation, Humanities, Federal University of São João Del-Rei, Campus Alto Paraopeba, Rod.: MG 443, Km 7, 36420-000, Ouro Branco, Minas Gerais, Brazil.
Search for more papers by this authorAlessandro Alberto Casazza
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.
Search for more papers by this authorSaleh Al Arni
Department of Chemical Engineering, Kind Saudi University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
Search for more papers by this authorCorresponding Author
Attilio Converti
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.
Department of Civil, Chemical, and Environmental Engineering, University of Genoa, via Opera Pia 15, 16145 Genoa, Italy.Search for more papers by this authorAbstract
Previous experimental data of xylose-to-xylitol bioconversion by Debaryomyces hansenii carried out according to a 33 full factorial design were used to model this process by two different artificial neural network (ANN) training methods. Models obtained for four responses were compared with those of response surface methodology (RSM). ANN models were shown to be superior to RSM in the predictive capacity, whereas the latter showed better performance in the generalization capability step. RSM with optimization using a genetic algorithm was revealed as a whole to be the best modeling option, which suggests that the comparative performances of RSM and ANN may be a highly problem-specific issue.
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