Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks.

Abstract

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with > 3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection of PyMS in conjunction with ANNs was 3 x 10(4) E. coli UB5201 cells in 1.6 x 10(7) E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.