@article{BreuchKleinSiefkeetal.2020,
  author    = {Ren{\´e} Breuch and Daniel Klein and Eleni Siefke and Martin Hebel and Ulrike Herbert and Claudia Wickleder and Peter Kaul},
  title     = {Differentiation of meat-related microorganisms using paper-based surface-enhanced Raman spectroscopy combined with multivariate statistical analysis},
  series   = {Talanta},
  volume    = {219},
  publisher = {Elsevier},
  issn      = {0039-9140},
  doi       = {10.1016/j.talanta.2020.121315},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-49666},
  year      = {2020},
  abstract  = {Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030, Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5 \% and 97.5 \%, respectively.},
  language  = {en}
}