Christoph Lange, Simon Seidel, Madeline Altmann, Daniel Stors, Annina Kemmer, Linda Cai, Stefan Born, Peter Neubauer, M. Nicolas Cruz Bournazou
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A Setup for Automatic Raman Measurements in High‐Throughput Experimentation
High‐throughput (HT) experimentation is transforming biotechnology by enabling systematic exploration of complex multi‐dimensional experimental conditions. However, current analytical methods are often unable to handle the rapid pace of sample generation in HT workflows. This study presents an integrated system of physical devices and software to automate and accelerate Raman spectral measurements in HT‐facilities. The setup simultaneously handles eight parallel L samples delivered by a pipetting robot, completing measurement, handling, cleaning, and concentration prediction within 45 s per sample. We introduce a machine learning model to predict metabolite concentrations from Raman spectra, achieving mean absolute errors of for glucose and for acetate during Escherichia coli cultivations. This approach enables consistent high‐throughput spectral data collection for fermentation monitoring, calibration, and offline analysis, supporting the generation of extensive datasets, enabling the training of more robust and generalizable machine learning models.
期刊介绍:
Biotechnology & Bioengineering publishes Perspectives, Articles, Reviews, Mini-Reviews, and Communications to the Editor that embrace all aspects of biotechnology. These include:
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The editors will consider papers for publication based on novelty, their immediate or future impact on biotechnological processes, and their contribution to the advancement of biochemical engineering science. Submission of papers dealing with routine aspects of bioprocessing, description of established equipment, and routine applications of established methodologies (e.g., control strategies, modeling, experimental methods) is discouraged. Theoretical papers will be judged based on the novelty of the approach and their potential impact, or on their novel capability to predict and elucidate experimental observations.