Approximately 90% of biomanufacturing initiatives fail to scale successfully beyond laboratory settings due to challenges such as inefficient scaling processes, suboptimal bioreactor performance, and high operational costs. Traditional technologies like GC-MS often require substantial investment and can be overly complex for delivering the specific insights needed to optimize bioprocesses.

In this project, SQALE ApS and DevLabs ApS are partnering to develop a compact, non-invasive, and cost-effective eNose sensor system designed to deliver the critical data required to scale bioprocesses effectively

SQALE and DevLabs have previously validated the use of sensor technology combined with AI-driven analytics to monitor gas composition, fermentation stages, biomass concentration, and growth rates under optimal conditions.

However, variations in client-specific setups and parameters can affect the accuracy and lifespan of the eNose system.

The primary goal of this project is to develop advanced hardware and AI algorithms, validated through pilot projects across diverse fermentation environments, to create a robust, non-invasive bioreactor monitoring solution capable of partially replacing conventional GC-MS instrumentation.