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Microorganisms not only contribute to the spoilage of food but can also cause illnesses through consumption. Consumer concerns and doubts about the shelf life of the products and the resulting enormous amounts of food waste have led to a demand for a rapid, robust, and non-destructive method for the detection of microorganisms, especially in the food sector. Therefore, a rapid and simple sampling method for the Raman- and infrared (IR)-microspectroscopic study of microorganisms associated with spoilage processes was developed. For subsequent evaluation pre-processing routines, as well as chemometric models for classification of spoilage microorganisms were developed. The microbiological samples are taken using a disinfectable sampling stamp and measured by microspectroscopy without the usual pre-treatments such as purification separation, washing, and centrifugation. The resulting complex multivariate data sets were pre-processed, reduced by principal component analysis, and classified by discriminant analysis. Classification of independent unlabeled test data showed that microorganisms could be classified at genus, species, and strain levels with an accuracy of 96.5 % (Raman) and 94.5 % (IR), respectively, despite large biological differences and novel sampling strategies. As bacteria are exposed to constantly changing conditions and their adaptation mechanisms may make them inaccessible to conventional measurement methods, the methods and models developed were investigated for their suitability for microorganisms exposed to stress. Compared to normal growth conditions, spectral changes in lipids, polysaccharides, nucleic acids, and proteins were observed in microorganisms exposed to stress. Models were developed to discriminate microorganisms, independent of the involvement of various stress factors and storage times. Classification of the investigated bacteria yielded accuracies of 97.6 % (Raman) and 96.6 % (IR), respectively, and a robust and meaningful model was developed to discriminate different microorganisms at the genus, species, and strain levels. The obtained results are very promising and show that the methods and models developed for the discrimination of microorganisms as well as the investigation of stress factors on microorganisms by means of Raman- and IR-microspectroscopy have the potential to be used, for example, in the food sector for the rapid determination of surface contamination.
The globalisation and the increasing international trade have raised the number and risk of introduction of foreign species and invasive pests for years. Although native species have adapted to the native habitat over many years and generations, invasive intruders often possess characteristics that are superior to native species. Thus, and because of a lack of natural enemies, they bear the potential of decimation or complete displacement of the native species; furthermore, the introduction of pathogens or nematodes as a vector possesses a high damage potential. The available measures of the local plant protection services to combat invasive species are confined. They are limited to the felling of infested trees or plants and regular controls within the infested area. A spread of single infestations can thereby be prevented, but undetected infestations can unimpededly spread, which points out the main challenge: the detection of the species. This concerns the infestation in open land as well as the single animal on its path of introduction. Concerning the development of new adequate detection systems for invasive species, there is only little research activity going on. For other fields like detection of explosives or narcotics, the research activities date back for more than one decade and consequently there are detection systems available, which are, for example, used for explosive detection in airports. The detection principle bases on the chemistry of these substances.