Managing microbiological risk: defining alert thresholds from historical data of a production environment

Introduction
Good Preparation Practices (GPP) define microbiological contamination limit values. However, without an intermediate alert threshold, monitoring is limited to identifying non-conformities rather than anticipating them. The aim of this work is to propose a method for determining alert thresholds enabling a shift from corrective management to preventive control of microbiological risk.

Materials and Methods
An assessment of the microbial ecology of the controlled atmosphere area was carried out based on monthly air and surface samples collected between 2023 and 2025 in 1 grade B room and 9 grade D rooms. Statistical analyses were performed using R® software. Alert thresholds were determined by iteratively applying Tukey’s method, which identifies outliers based on the interquartile range.

Results
Out of a total of 1641 samples, 1596 conforming samples were retained to form the reference population. A non-parametric percentile-based approach was applied for each room.
For grade D rooms, air alert thresholds ranged from 13 to 89 CFU/m3, i.e., 2 to 15 times lower than the limits set by GPP. For surfaces, alert thresholds ranged from 0 to 8 CFU/25cm2, i.e., up to 25 times lower than GPP limits. For the grade B room, the alert threshold was set at 6 CFU/m3 for air and 0 CFU/25cm2 for surfaces, thresholds also lower than the GPP limits.

Discussion
The alert thresholds determined by our method are systematically lower than the GPP limit values. This finding is expected and highlights the value of the approach. This difference is particularly marked for certain low-activity areas, where the usually low baseline contamination level leads to a very low alert threshold. A result compliant with GPP standards may therefore deviate significantly from internal expectations and signal a process drift before any declared non-conformity occurs. Furthermore, microbial enumeration, although essential, cannot be interpreted in isolation. Qualitative analysis of the flora adds an essential dimension to risk assessment: in our study, most samples revealed commensal flora of environmental or cutaneous origin, with filamentous fungi being more rarely present.

Conclusion
From our historical data, we were able to build operational alert thresholds for each production environment. These thresholds can help anticipate any drift, whether related to personnel, processes, interventions, or bio-cleaning.