Detection and identification of bacteria using antibiotic susceptibility and a multi-array electrochemical sensor with pattern recognition.

This work proposes the use of amperometric signals generated by a 96-well multi-array dissolved oxygen multi-electrode sensor (DOX) coupled with principal component analysis for continuous monitoring, identification and differentiation of bacteria. Two types of differentiation mechanisms were tested: (1) direct monitoring of respiratory activity via oxygen consumption and (2) quantification of the effect of three broad-spectrum antibiotics on bacteria growth and respiration over time. Five species of bacteria were examined including: Escherichia coli, Escherichia adecarboxylata, Comamonas acidovorans, Corynebacterium glutamicum and Staphylococcus epidermidis. The addition of small concentrations of antibiotics to the growth medium alters the oxygen consumption of the cells and a unique fingerprint is created for a specific cell. This fingerprint is shown to evolve over a specific concentration range that is dependant of instrumental constraints of the DOX system. The application of principal component analysis (PCA) to classify the data was also examined. It was shown that bacteria could be classified simply by their oxygen consumption rates over a varying concentration range. Discrimination between species can also be increased by the effects of the antibiotics on the oxygen consumption of varying concentrations of cells. The proposed DOX-PCA system illustrates a generic template that can be tailored to meet specific research goals by the selection of specific cell/antibiotic combinations and concentrations.