Professor Wallhäußer Award - Third Place

A new artificial intelligence–driven optical microbiology approach could significantly accelerate microbiological quality control in regulated industries by eliminating traditional incubation-based workflows and delivering results in minutes rather than days.

Microbiological testing in sectors such as pharmaceuticals, water quality, and environmental monitoring has long relied on culture-based methods that require incubation, extensive manual handling, and significant use of reagents and consumables. These processes, while well established and regulatory compliant, are often slow and operationally demanding, leading to delays in obtaining critical quality data.

The proposed technology replaces conventional incubation with a biophotonic imaging system combined with machine learning. Instead of cultivating microorganisms, samples are directly illuminated, and their intrinsic spectral signatures are analysed by trained algorithms. This reagent-free approach enables detection and quantification of microorganisms in approximately 10 minutes.

The workflow integrates standard sample preparation steps with a single-use filtration device designed to reduce contamination risks and ensure compatibility with GMP environments. After filtration, a controlled imaging system captures optical data, which is processed locally using embedded AI models. The system outputs quantitative results such as colony-forming units (CFU) per millilitre, gram, or square centimetre, covering key metrics like total aerobic microbial count and yeast and mold enumeration.

Results are automatically transferred to a digital dashboard, enabling centralized data management, automated trend analysis, and full traceability. The system is designed to comply with regulatory data integrity requirements, including 21 CFR Part 11, supporting auditability and secure data handling.

The technology is intended for applications where rapid microbiological insight is critical, including water systems, environmental monitoring, and product bioburden testing. By reducing time-to-result, it enables earlier detection of deviations and faster decision-making in quality control processes. Future development aims to extend capabilities toward microorganism identification and even rapid sterility testing.

Validation work has assessed performance characteristics such as accuracy, linearity, and repeatability in line with established guidance. Developers emphasize that regulatory alignment and data integrity remain central to the system’s design, supporting potential adoption in highly regulated environments.

If successfully implemented at scale, the approach could reshape microbiological testing by combining artificial intelligence with optical analysis to deliver faster, more efficient, and fully digital workflows without compromising compliance requirements.