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Does nonlinear modeling play a role in plasmid bioprocess monitoring using fourier transform infrared spectra?

2016-11Journal article Restricted access
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dc.contributor.authorB. Lopes, Marta
dc.contributor.authorCalado, Cecília
dc.contributor.authorFigueiredo, Mario
dc.contributor.authorBioucas-Dias, Jose
dc.date.accessioned2016-12-21T13:27:52Z
dc.date.available2016-12-21T13:27:52Z
dc.date.issued2016-11
dc.description.abstractThe monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLOPES, Marta; [et al] - Does nonlinear modeling play a role in plasmid bioprocess monitoring using fourier transform infrared spectra?. Applied Spectroscopy. ISSN 1943-3530. Vol. 71, N.º 6 (2017), pp. 1148-1156pt_PT
dc.identifier.doi10.1177/0003702816670913pt_PT
dc.identifier.issn1943-3530
dc.identifier.urihttp://hdl.handle.net/10400.21/6643
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSociety for Applied Spectroscopypt_PT
dc.relation.publisherversionhttps://www.sparrho.com/item/does-nonlinear-modeling-play-a-role-in-plasmid-bioprocess-monitoring-using-fourier-transform-infrared-spectra/a6a3c7/pt_PT
dc.subjectNonlinear modelingpt_PT
dc.subjectBioprocess monitoringpt_PT
dc.subjectPlasmid bioprocesspt_PT
dc.subjectSpectroscopy FT-IRpt_PT
dc.subjectMidinfraredpt_PT
dc.titleDoes nonlinear modeling play a role in plasmid bioprocess monitoring using fourier transform infrared spectra?pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1156pt_PT
oaire.citation.issue6
oaire.citation.startPage1148pt_PT
oaire.citation.volume71
person.familyNameB. Lopes
person.familyNameCalado
person.familyNameFigueiredo
person.givenNameMarta
person.givenNameCecília
person.givenNameMario
person.identifier1195979
person.identifier130332
person.identifier3015485
person.identifier.ciencia-idFD16-A07F-7B12
person.identifier.ciencia-id9418-E320-3177
person.identifier.ciencia-idED1E-A787-3569
person.identifier.orcid0000-0002-4135-1857
person.identifier.orcid0000-0002-5264-9755
person.identifier.orcid0000-0002-0970-7745
person.identifier.ridF-5378-2011
person.identifier.ridE-2102-2014
person.identifier.ridC-5428-2008
person.identifier.scopus-author-id55489480400
person.identifier.scopus-author-id6603163260
person.identifier.scopus-author-id34769730500
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication183b936b-4a1d-4c80-9405-17491abb3d64
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublicationd3d068dc-5887-4ecd-bf7f-067ef06e1943
relation.isAuthorOfPublication.latestForDiscoverye8577257-c64c-4481-9b2b-940fedb360cc

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