Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorTakahashi, Maria Beatriz
dc.creatorLeme, Jaci
dc.creatorCaricati, Celso Pereira
dc.creatorTonso, Aldo
dc.creatorNuñez, Eutimio Gustavo Fernández
dc.creatorRocha, José Celso
dc.date2015-08-21T17:53:04Z
dc.date2016-10-25T20:55:52Z
dc.date2015-08-21T17:53:04Z
dc.date2016-10-25T20:55:52Z
dc.date2015
dc.date.accessioned2017-04-06T08:44:28Z
dc.date.available2017-04-06T08:44:28Z
dc.identifierBioprocess and Biosystems Engineering, v. 38, n. 6, p. 1045-1054, 2015.
dc.identifier1615-7591
dc.identifierhttp://hdl.handle.net/11449/126758
dc.identifierhttp://acervodigital.unesp.br/handle/11449/126758
dc.identifierhttp://dx.doi.org/10.1007/s00449-014-1346-7
dc.identifier2399590592977330
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/937334
dc.descriptionCurrently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.
dc.languageeng
dc.relationBioprocess and Biosystems Engineering
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleArtificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes
dc.typeOtro


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