Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials.

Moist/hydrated biofilms have been well-studied in the medical area, and their association with infections is widely recognized. In contrast, dry-surface biofilms (DSBs) on environmental surfaces in healthcare settings have received less attention. DSBs have been shown to be widespread on commonly used items in hospitals and to harbor bacterial pathogens that are known to cause healthcare-acquired infections (HAI). DSBs cannot be detected by routine surface swabbing or contact plates, and studies have shown DSBs to be less susceptible to cleaning/disinfection products. As DSBs are increasingly reported in the medical field, and there is a likelihood they also occur in food production and manufacturing areas, there is a growing demand for the rapid in situ detection of DSBs and the identification of pathogens within DSBs. Raman microspectroscopy allows users to obtain spatially resolved information about the chemical composition of biofilms, and to identify microbial species. In this study, we investigated Staphylococcus aureus mono-species DSB on polyvinylchloride blanks and stainless steel coupons, and dual-species (S. aureus/Bacillus licheniformis) DSB on steel coupons. We demonstrated that Raman microspectroscopy is not only suitable for identifying specific species, but it also enables the differentiation of vegetative cells from their sporulated form. Our findings provide the first step towards the rapid identification and characterization of the distribution and composition of DSBs on different surface areas.

Raman Microscopic Identification of Microorganisms on Metal Surfaces via Support Vector Machines.

An easy, inexpensive, and rapid method to identify microorganisms is in great demand in various areas such as medical diagnostics or in the food industry. In our study, we show the development of several predictive models based on Raman spectroscopy combined with support vector machines (SVM) for 21 species of microorganisms. The microorganisms, grown under standardized conditions, were placed on a silver mirror slide to record the data for model development. Additional data was obtained from microorganisms on a polished stainless-steel slide in order to validate the models in general and to assess possible negative influences of the material change on the predictions. The theoretical prediction accuracies for the most accurate models, based on a five-fold cross-validation, are 98.4%. For practical validation, new spectra (from stainless-steel surfaces) have been used, which were not included in the calibration data set. The overall prediction accuracy in practice was about 80% and the inaccurate predictions were only due to a few species. The development of a database provides the basis for further investigations such as the application and extension to single-cell analytics and for the characterization of biofilms.