Leveraging Broad-Spectrum Fluorescence Data and Machine Learning for High-Accuracy Bacterial Species Identification

Abstract

Rapid and accurate identification of bacterial species is essential for the effective treatment of infectious diseases and suppression of antibiotic-resistant strains. The unique autofluorescence properties of bacterial cells are exploited for rapid and cost-effective identification that is suitable for point-of-care applications. Fluorescence spectroscopy is combined with machine learning to improve the diagnostic accuracy. Good training data for machine learning can be obtained to achieve the same diagnostic accuracy for bacterial species as when each wavelength is measured in detail over a broad spectral width. Experiments were performed testing 14 bacterial strains. The excitation-emission matrix was analyzed, and Bayesian optimization was used to identify the most effective combinations of wavelengths. The results showed that fluorescence spectra using three specific excitation light regions or excitation spectra using two broad fluorescence detection regions could be used as supervised data to realize diagnostic accuracy comparable to that obtained with more complex instruments.