Near-Infrared Spectroscopy and Multivariate Analysis as an Effective Method to Discriminate Escherichia coli in...

Escherichia coli is a bacterium that inhabits the gastrointestinal system and is considered to be an essential part of the intestinal microbiota. However, some strains can be pathogenic, causing urinary tract infections. These bacteria can develop antibiotic resistance during prolonged or inadequate treatments, and sensitive and specific tests are necessary for diagnosis. In this study, we aimed to determine, through NIR spectroscopy combined with variable selection techniques such as the successive projections algorithm (SPA) and genetic algorithm (GA) integrated with linear discriminant analysis (LDA), the discrimination of E. coli strains (sensitive vs resistant). The two E. coli strains resulted in a total of 162 spectral data, classified into 81 sensitive and 81 resistant spectra. These data were later subdivided into 114 for training, 24 for validation, and 24 for testing. Each of these sets maintained a balanced proportion between the two strains, containing half of the sensitive and half of the resistant strains. The variables selected by these methods were used to differentiate the species. Additionally, we evaluated the influence of spectral preprocessing techniques such as Savitzky-Golay smoothing and extended multiplicative scatter correction (EMSC) on the spectral data. The results showed that both models (SPA-LDA and GA-LDA) presented 100% sensitivity and specificity for both sensitive and resistant strains. This demonstrates that NIR spectroscopy combined with variable selection techniques can be an effective method for rapid and accurate identification of bacterial strains, offering a promising alternative for microbiological diagnostics.