Calibration transfer for bioprocess Raman monitoring using Kennard Stone piecewise direct standardization and multivariate algorithms

In the biopharmaceutical industry, Raman spectroscopy is now a proven PAT tool that enables in-line simultaneous monitoring of several CPPs and CQAs in real-time. However, as Raman monitoring requires multivariate modeling, variabilities unknown by models can impact the monitoring prediction accuracy. With the widespread use of Raman PAT tools, it is necessary to fix instrumental variability impacts, encountered for instance during a device replacement. In this work, we investigated the impact of instrumental variability between probes inside a multi-channel analyzer and between two analyzers, and explored solutions to correct them on model prediction errors in cell cultures. It is shown that the Kennard Stone piecewise direct standardization (KS PDS) method enables to lower model prediction errors between probes of a multi-channel analyzer from 20% to 10% on the cell densities (TCD/VCD). Considering the integration of a new device or the replacement of a previous one, it has been determined that a first cell culture monitoring can be directly performed with the new analyzer calibrated by the KS PDS method based on the dataset from the previous analyzer, with an accuracy better than 10% on the main components of the culture like glucose, lactate, and the cell densities. Then, the new data obtained by the new analyzer can be inserted in a global calibration dataset to integrate instrumental variability in the chemometric model: it is shown that only one batch with the new device in a consistent and equilibrated calibration dataset was sufficient to correct the prediction gap induced by instrumental variability, allowing to exploit the data from previous analyzers considering optimized methods. This methodology provides good multivariate calibration model prediction errors throughout the instrumental changes which is a requirement for model maintenance.