Comment by limel

That’s a very good point, and we actually see fermentation batches as one of the most promising early use cases. In many facilities, smell is already used informally as a pass/fail indicator, but it is subjective and difficult to scale.

We measure both humidity and temperature and use them as additional inputs for the ML models. Regarding sensor drift, it is still difficult to fully assess its impact on the business case. At this stage, our main focus is on the accuracy of the classification models rather than very long-term operation — that would be the next step.

For now, the practical approaches we consider are either on-the-fly calibration through a feedback loop based on the actual process output, or simply replacing the sensor when necessary, as the manufacturing cost is relatively low.”