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Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs

Rolinger, Laura 1; Rüdt, Matthias 1; Hubbuch, Jürgen ORCID iD icon 1
1 Institut für Bio- und Lebensmitteltechnik (BLT), Karlsruher Institut für Technologie (KIT)


A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L−1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L−1. The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000137046
Veröffentlicht am 12.09.2021
DOI: 10.1002/bit.27894
Zitationen: 16
Zitationen: 16
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Bio- und Lebensmitteltechnik (BLT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0006-3592
KITopen-ID: 1000137046
Erschienen in Biotechnology and Bioengineering
Verlag John Wiley and Sons Inc
Band 118
Heft 11
Seiten 4255-4268
Nachgewiesen in Scopus
Web of Science
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