Establishment and Thorough External Validation of a FTIR Spectroscopy Classifier for Salmonella Serogroup Differentiation
Oberreuter, H.; Cordovana, M.; Dyk, M.; Rau, J.
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As one of the most relevant food-borne pathogens, the reliable detection, confirmation and fine-typing of Salmonella strains is very important. Salmonella serotype determination by rabbit antisera posts the worldwide-accepted standard but is labor intensive, costly and needs extensive experience. As an alternative, successful discrimination between strains of different serogroups by FTIR spectroscopy has been developed before for various bacterial groups. In the current study, firstly a FTIR Classifier operating on an IR Biotyper(R) spectrometer (Bruker, Germany) was designed to distinguish between n=36 different Salmonella serogroups. A FTIR classifier is an AI-based tool used in FTIR spectroscopy to analyze and classify different materials based on their infrared spectra. Secondly, the differentiation performance of this classifier was determined by a thorough external single-lab validation carried out in line with the Guidelines for Validating Species Identifications Using MALDI-ToF-MS issued by the German Federal Office of Consumer Protection for a targeted identification: The most common Salmonella serogroups in Europe, serogroups O:4 (B), O:6,7 (C1), O:8 (C2-C3) and O:9 (D1) were chosen as target parameters and validated using a total of n=1039 infrared absorbance spectra from a total of n=167 strains pertaining to n=39 serogroups. In summary, serogroups O:4, O:6,7 and O:9 perfectly met the adapted guideline requirements and resulted in a >99% inclusivity each. Serogroup O:8 arrived at a 96.1% true-positive rate due to one deviating strain. This validated classification method can thus be used in routine analysis for quick and easy differentiation of the most common Salmonella serogroups in food surveillance. In addition, using the cluster analysis tools of the IR BT(R), a preselection of isolates before subjecting them to thorough serotyping decreases the workload in current routine analyses.
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