Fast and accurate identification of pathogenic bacteria using excitation-emission spectroscopy and machine learning.

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation-emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4'-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye-bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation-emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.