How Online Raman Makes Sense of a Bioreactor

One paper shows that the key challenge in online fermentation monitoring is not just "being able to measure" but "measuring accurately". If glucose and ethanol in a bioreactor are the "invisible protagonists", then online Raman is the beam of light that reveals them in real time.  This study, published in Spectrochimica Acta Part A, focuses on how online Raman can push the limit of detection (LOD) when monitoring bioreactors, and offers highly practical guidance for model development.

What did this study do？

Using alcoholic fermentation as a model system, the authors inserted an in-situ Raman probe directly into the bioreactor environment to monitor glucose and ethanol online quantitatively.

The focus was not simply on whether measurement is possible, but on two more practical questions: first, how to improve the signal-to-noise ratio; second, how to keep the model stable and reliable in a complex mixed system.

The core value of online Raman:

The most noteworthy aspect of this study is that it advances online Raman from a process characterization tool to an online analytical tool suitable for quantitative control. The authors clearly state that online Raman can capture fermentation information in situ, in real time, and non-destructively – a feature especially valuable for bioprocessing applications that require continuous monitoring of substrate consumption and product formation.

More importantly, Raman is insensitive to the water background and is well suited for direct measurement of liquid systems. Therefore, it is far more suitable for online reactor monitoring than many off-line methods that require frequent sampling and sample preparation.

This means online Raman is not just an auxiliary analytical tool – it can be truly embedded into the process control chain and serve the purpose of Process Analytical Technology (PAT).

Innovations of this study:

One highlight is that the authors did not start by building models directly with real fermentation broth. Instead, they first constructed a learning database using pure standard solutions, and then performed validation with mixtures of different ratios.

The advantage of this strategy is that it allows the spectral contribution of each individual component to be resolved clearly, reduces mutual interference, and facilitates the identification of characteristic peaks.

Another highlight is the authors’ strong emphasis on optimizing the signal-to-noise ratio. They compared different exposure times and accumulation numbers, and finally selected 80 s exposure with 3 accumulations as the best trade-off between signal quality and acquisition time.

This demonstrates that the performance of online Raman depends not only on the instrument itself, but also on acquisition parameters, which can significantly affect the final model performance.

What does the results show？

After constructing multiple PLS models, the authors found that by narrowing the concentration range of the calibration set from 0.5–100 g/L to a low-concentration range (0.5–10 g/L), the limits of detection for both ethanol and glucose improved markedly, reaching 0.42 g/L and 1.55 g/L, respectively.

This finding is highly instructive for online monitoring: a lower detection limit does not always come from more data, but may result from a more rational data distribution.

However, the authors also caution about an important trade-off: if the training set is too heavily biased toward low concentrations, predictive performance at high concentrations deteriorates.

In other words, modeling for online Raman is not simply "the narrower the better". Instead, one must balance detection limit and full-range accuracy based on the actual process objectives.

In a single sentence

The most important value of this paper is not "proving that Raman can measure glucose and ethanol", but showing that the path to practical process application of online Raman hinges on low detection limits, robust modeling, and thoughtful dataset design.