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This research aims to enhance the monitoring of bioprocesses using Raman spectroscopy without needing extensive recalibrations.

February 12, 2026Biotechnology ProgressOpen Access

Innovative methodology for flexible monitoring of various bioprocesses by using pre‐existing Raman data coupled with automated transfer learning technique

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Innovative methodology improves bioprocess monitoring efficiency using automated transfer learning techniques.

This research aims to enhance the monitoring of bioprocesses using Raman spectroscopy without needing extensive recalibrations.
Developed an automated transfer learning method with the dynamic orthogonal projection algorithm.
Used pre-existing chemometric models from one process to monitor a target process.
Validated the approach on Chinese Hamster Ovary cell cultures to assess metabolic parameters.
Achieved around 10% average relative errors for glucose and lactate compared to offline values after one batch transfer.
Relative errors were further reduced below 10% for most parameters after transferring with two batches.

Schini et al. (2026) studied this question.

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