Generic chemometric models for metabolite concentration prediction based on Raman spectra

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dc.contributor.authorYousefi-Darani, Abdolrahim
dc.contributor.authorPaquet-Durand, Olivier
dc.contributor.authorvon Wrochem, Almut
dc.contributor.authorClassen, Jens
dc.contributor.authorTränkle, Jens
dc.contributor.authorMertens, Mario
dc.contributor.authorSnelders, Jeroen
dc.contributor.authorChotteau, Veronique
dc.contributor.authorMäkinen, Meeri
dc.contributor.authorHandl, Alina
dc.contributor.authorKadisch, Marvin
dc.contributor.authorLang, Dietmar
dc.contributor.authorDumas, Patrick
dc.contributor.authorHitzmann, Bernd
dc.date.accessioned2024-10-23T12:25:51Z
dc.date.available2024-10-23T12:25:51Z
dc.date.issued2022de
dc.description.abstractChemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.en
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16811
dc.identifier.urihttps://doi.org/10.3390/s22155581
dc.language.isoengde
dc.rights.licensecc_byde
dc.source1424-8220de
dc.sourceSensors; Vol. 22, No. 15 (2022) 5581de
dc.subjectGeneric model
dc.subjectRaman spectroscopy
dc.subjectOn-line process monitoring
dc.subjectPLS regression
dc.subjectChemometrics
dc.subjectCHO cell cultivation
dc.subject.ddc660
dc.titleGeneric chemometric models for metabolite concentration prediction based on Raman spectraen
dc.type.diniArticle
dcterms.bibliographicCitationSensors, 22 (2022), 15, 5581, https://doi.org/10.3390/s22155581. ISSN: 1424-8220
dcterms.bibliographicCitation.issn1424-8220
dcterms.bibliographicCitation.issue15
dcterms.bibliographicCitation.journaltitleSensors
dcterms.bibliographicCitation.volume22
local.export.bibtex@article{Yousefi-Darani2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16811}, doi = {10.3390/s22155581}, author = {Yousefi-Darani, Abdolrahim and Paquet-Durand, Olivier and von Wrochem, Almut et al.}, title = {Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra}, journal = {Sensors}, year = {2022}, volume = {22}, number = {15}, }
local.export.bibtexAuthorYousefi-Darani, Abdolrahim and Paquet-Durand, Olivier and von Wrochem, Almut et al.
local.export.bibtexKeyYousefi-Darani2022
local.export.bibtexType@article

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