A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward-scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and serovar. The notable similarity between FSP technology and spectroscopies is their reliance on statistical machine learning to perform recognition. Currently used methods utilize traditional supervised techniques which assume completeness of training libraries. However, this restrictive assumption is known to be false for most experimental conditions, resulting in unsatisfactory levels of accuracy, poor specificity, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates application of the BARDOT system to classify bacteria belonging to the Salmonella class in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated detection of unknown bacterial classes.