Raman-guided Sample Subset Selection for Cost-efficient Offline Calibration in Bioprocesses

In bioprocess engineering, model-based methods play a vital role in understanding complex dynamics of novel species or strains. However, the development of these methods is often hampered by prohibitive costs associated with redundant reference analyses and poor and arbitrary planned experiments due to insufficient prior knowledge about the process dynamics. To address this challenge, we propose a novel PAT approach for sample subset selection using constrained vector quantization on online Raman spectroscopy data, particularly focusing on optimizing the selection of data points for costly offline analyses. This Raman-guided sample subset selection (RGSS) is demonstrated with Saccharomyces cerevisiae fermentations and evaluated with nonlinear model identification and practical identifiability tests with reduced data sets. By applying the presented sample subset selection strategy, we can select samples from experiments using spectroscopic data, which are then analyzed offline, such that the costs are reduced by more than half. This shows the effectiveness of the method in reducing analytical costs and resource usage, minimizing waste and offering a sustainable solution for model-based biotechnological process development. The proposed RGSS reduces offline reference analysis by up to 77% (5 samples instead of 22), providing a sustainable and data-efficient alternative to conventional sampling selection strategies.