¹Department of Orthopedic and Trauma Surgery, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Deutschland
²Mannheim Technical University, Mannheim, Deutschland

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Objectives and questions: This study aims to present an innovative approach for the rapid spectral detection of biofilm formation using a novel mid-infrared (MIR) scanning system. The key objective is to evaluate the performance of this MIR system and compare it to a commercially available Fourier-transform infrared (FTIR) scanner in detecting biofilm formation on implant surfaces, particularly focusing on the biofilm production of Staphylococcus epidermidis (SE).

Material and methods: SE biofilms were grown for 3 days, and their biofilm formation was measured using both the FTIR and MIR scanning systems. Processing times were compared between the two methods, with the FTIR scanner requiring ~8 hours/cm² and the MIR system reducing this time to mere seconds. K-means clustering analysis was used to assess the distribution of bacterial strains, differentiating between biofilm-producing (RP62A) and non-biofilm-producing (ATCC 12228) strains. The analysis focused on the presence of poly-N-acetylglucosamine (PNAG), a key component of extracellular polymeric substances (EPS) in SE.

Results: The novel MIR system demonstrated significantly faster processing times than FTIR, achieving a reduction from hours to seconds. Additionally, the MIR system exhibited substantially higher sensitivity, allowing for clear differentiation between the chemical signatures of biofilm and planktonic strains. The K-means clustering analysis revealed distinct patterns in the distribution of clusters, with pronounced differences between biofilm-producing and non-biofilm-producing bacterial strains.

Discussion and conclusions: Our findings show that the novel MIR scanning system provides not only faster detection times but also enhanced sensitivity when compared to the FTIR system. The ability to clearly differentiate between biofilm and planktonic strains makes this MIR approach a promising tool for detecting biofilm formation in clinical diagnostics. By integrating advanced data analytics with a rapid MIR prototype, this approach provides a valuable alternative to traditional microbial detection methods, which is crucial for diagnosing complex infections such as PJIs.