Abstract
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.
Highlights
Besides common microbiological identification methods using a combination of cultivation on different nutrition media and additional tests such as Gram staining, there are more powerful tools such as DNA-based methods or matrix-assisted laser desorption ionization-time of flight mass spectroscopy (MALDI-TOF MS)
Optical spectroscopy in combination with chemometric methods has allowed the identification of microorganisms as well, and several publications have already shown that reliable identification of bacteria is feasible using Raman spectroscopy [3–5]
The results indicate that micro-Raman spectroscopy in combination with support vector
Summary
Besides common microbiological identification methods using a combination of cultivation on different nutrition media and additional tests such as Gram staining, there are more powerful tools such as DNA-based methods or matrix-assisted laser desorption ionization-time of flight mass spectroscopy (MALDI-TOF MS). Optical spectroscopy in combination with chemometric methods has allowed the identification of microorganisms as well, and several publications have already shown that reliable identification of bacteria is feasible using Raman spectroscopy [3–5]. In medical diagnostics, hours could decide about the health status of a patient, and in food, processing time is crucial to ensure product safety and a long shelf life. In 2002, Maquelin et al investigated the potential use of vibrational spectroscopies in medical microbiology emphasizing the great potential for clinical diagnostic microbiology assuming a large database of well-defined strains [6]. Ho et al recently showed that they could distinguish between methicillin-resistant and -susceptible isolates of Staphylococcus aureus with
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