Characterisation and differentiation of oral bacteria using ATR-FTIR spectroscopy

Implant associated infections can result in inflammatory conditions such as periimplantitis, that can ultimately lead to implant loss. This is caused by a large community of bacterial species. These bacteria form a multispecies biofilm on the tooth surface. Its composition can shift as it matures over time, the so-called pathogenic shift occurs once pathogens adhere to the biofilm. We developed a measurement protocol and analysis pipeline based on ATR-FTIR spectroscopy that is able to distinguish between different oral bacteria by detecting slight changes in protein expression. This can easily be done for single species samples with supervised learning algorithms like the k-nearest neighbour algorithm, with which we achieved a prediction accuracy of 99.8 %. Chemometric and deep learning approaches can streamline the process in distinguishing multispecies samples. This would be a step towards early detection of the pathogenic shift in oral biofilms and help avoiding diseases.