For a fast identification of eukaryotic cells such as yeast species without a cultivation step it should be possible to perform the investigation on only one single cell. Since yeasts as eukaryotes are heterogeneous and their Raman spectra are therefore dependent on the measuring position, one Raman spectra is not representative of the whole cell. In this contribution we demonstrate the application of average Raman spectra of a line scan over single yeast cells. These average spectra are used for classification with the help of a support vector machine.

Fast analysis of bioaerosols in clean room environments is necessary in order to prevent contamination of pharmaceutical products, minimize machine downtimes, or both. The detection and identification of microbes will be carried out in several steps: After impaction of the aerosol on a surface, the particles are presorted with glancing light illumination and fluorescence imaging in order to distinguish between abiotic and biotic particles. Since only the biotic particles are of interest, the analysis time can be minimized due to reduction of the data set. The biotic particles are then analyzed further with Raman spectroscopy and identified with a support vector machine.

UV-resonance Raman spectroscopy is applied as a method for the identification of lactic acid bacteria from yogurt. Eight different strains of bacteria from Lactobacillus acidophilus, L. delbrueckii ssp. bulgaricus, and Streptococcus thermophilus were investigated. At an excitation wavelength of 244 nm signals from nucleic acids and proteins are selectively enhanced. Classification was accomplished using different chemometric methods. In a first attempt, the unsupervised methods hierarchical cluster analysis and principal component analysis were applied to investigate natural grouping in the data. In a second step the spectra were analyzed using several supervised methods: K-nearest neighbor classifier, nearest mean classifier, linear discriminant analysis, and support vector machines.

A rapid analysis of microorganisms is necessary for medical, pharmaceutical or food technology applications to identify harmful bacteria. Conventional identification methods require pure cultures from isolates and are often time demanding. Raman spectroscopy offers an alternative approach to identify microorganisms. With Raman microscopy it is possible to measure structures in the sub micrometer range, and therefore single bacteria cells are accessible. Micro-Raman mapping experiments proof that the bacterium shows a spatial homogeneity, since bacteria normally exhibit no compartments, therefore one spectrum of a single vegetative bacterial cell is sufficient to identify the strain. In contrary bacterial spores and yeast cells exhibit a high spatial dependency of the observed Raman spectra. For heterogeneous samples like single spores or yeast cells a mean spectrum from up to ten different positions is required to describe the complete cell.Using micro-Raman spectra of single bacterial cells and average spectra of yeast cells it is possible to create a database and identify microorganisms on species or even strain level.

ABSTRACT Microorganisms, such as bacteria, which might be present as contamination inside an industrial food or pharmaceutical clean room process need to be identified on short time scales in order to minimize possible health hazards as well as production downtimes causing financial deficits. Here we describe the first results of single-particle micro-Raman measurements in combination with a classification method, the so-called support vector machine technique, allowing for a fast, reliable, and nondestructive online identification method for single bacteria.

Microbial contamination is not only a medical problem, but also plays a large role in pharmaceutical clean room production and food processing technology. Therefore many techniques were developed to achieve differentiation and identification of microorganisms. Among these methods vibrational spectroscopic techniques (IR, Raman and SERS) are useful tools because of their rapidity and sensitivity. Recently we have shown that micro-Raman spectroscopy in combination with a support vector machine is an extremely capable approach for a fast and reliable, non-destructive online identification of single bacteria belonging to different genera. In order to simulate different environmental conditions we analyzed in this contribution different Staphylococcus strains with varying cultivation conditions in order to evaluate our method with a reliable dataset. First, micro-Raman spectra of the bulk material and single bacterial cells that were grown under the same conditions were recorded and used separately for a distinct chemotaxonomic classification of the strains. Furthermore Raman spectra were recorded from single bacterial cells that were cultured under various conditions to study the influence of cultivation on the discrimination ability. This dataset was analyzed both with a hierarchical cluster analysis (HCA) and a support vector machine (SVM).