Raman spectroscopy is successfully used for the reliable classification of complex biological samples. Much effort concentrates on the accurate prediction of known categories for highly relevant tasks in a wide area of applications such as cancer detection and bacteria recognition. However, the resulting recognition systems cannot always be directly used in practice since unseen samples might not belong to classes present in the training set. Our work aims to tackle this problem of novelty

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... tion using a recently proposed approach based on Gaussian processes. By learning novelty scores for a large bacteria Raman dataset comprising 50 different strains, we analyze the behavior of this method on an independent dataset which includes known as well as unknown categories. Our experiment reveals that Gaussian processes can be successfully applied to the task of finding unknown bacterial strains, leading to superior results compared to state-of-the-art methods such as Gaussian mixture models and Support Vector Data Description.