Comparison of response surface methodology and artificial neural network for modeling xylose-to-xylitol bioconversion

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dc.creatorFábio Coelho Sampaio
dc.creatorJanaína Teles de Faria
dc.creatorGabriel Dumond de Lima Silva
dc.creatorRicardo Melo Gonçalves
dc.creatorCristiano Grijó Pitangui
dc.creatorAlessandro Alberto Casazza
dc.creatorSaleh al Arni
dc.creatorAttilio Converti
dc.date.accessioned2022-12-07T18:59:52Z
dc.date.accessioned2025-09-08T23:53:45Z
dc.date.available2022-12-07T18:59:52Z
dc.date.issued2017
dc.identifier.doihttps://doi.org/10.1002/ceat.201600066
dc.identifier.issn0930-7516
dc.identifier.urihttps://hdl.handle.net/1843/47828
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofChemical Engineering & Technology
dc.rightsAcesso Restrito
dc.subjectRedes neurais (Computação)
dc.subjectLevedos
dc.subject.otherArtificial neural networks
dc.subject.otherDebaryomyces hansenii
dc.subject.otherResponse surface methodology
dc.subject.otherXylitol
dc.subject.otherYeast
dc.titleComparison of response surface methodology and artificial neural network for modeling xylose-to-xylitol bioconversion
dc.typeArtigo de periódico
local.citation.epage129
local.citation.issue1
local.citation.spage122
local.citation.volume40
local.description.resumoPrevious 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.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://onlinelibrary.wiley.com/doi/full/10.1002/ceat.201600066

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