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PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen
PEOPLE@HES-SO - Verzeichnis der Mitarbeitenden und Kompetenzen

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

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Rolinger Laura

Rolinger Laura

Professeur-e HES Associé-e

Hauptkompetenzen

Process Analytical Technology

Chemometrics

Machine Learning

Spectroscopy

Pharmaceutical Manufacturing

Analytical Chemistry

Chemical process engineering

  • Kontakt

  • Lehre

  • Publikationen

Hauptvertrag

Professeur-e HES Associé-e

Büro: ENP.19.516

HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
Rue de l'Industrie 23, 1950 Sion, CH
HEI - VS
Bereich
Chimie et sciences de la vie
Hauptstudiengang
Ingénierie des Sciences du vivant
BSc HES-SO en Ingénierie des sciences du vivant - HES-SO Valais-Wallis - Haute Ecole d'Ingénierie

2025

Development of a blending design space model to control blend uniformity in a mini-batch direct compression line
Wissenschaftlicher Artikel

Rolinger Laura

Powder Technology, 2025

Link zur Publikation

2023

Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements
Wissenschaftlicher Artikel ArODES

Laura Rolinger, Jürgen Hubbuch, Matthias Rüdt

Analytical and Bioanalytical Chemistry,  2023, vol. 415, pp. 841-854

Link zur Publikation

Zusammenfassung:

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control.

2021

Comparison of UV- and Raman-based monitoring of the protein A load phase and evaluation of data fusion by PLS models and CNNs
Wissenschaftlicher Artikel ArODES

Laura Rolinger, Matthias Rüdt, Jürgen Hubbuch

Biotechnology and Bioengineering,  2021, vol. 118, no. 11, pp. 4255-4268

Link zur Publikation

Zusammenfassung:

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. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

2020

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing
Wissenschaftlicher Artikel

Rolinger Laura

Analytical and bioanalytical chemistry, 2020 , vol.  412

Link zur Publikation

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