Strategy for an automatic system of counting and classification of Bacteria Signals based on Magnetic cytometry detection events

Flow cytometry is a powerful technique to analyze cells in a solution. Magnetic flow cytometers combine the potential for cell detection and counting with the advantages of a lab-on-chip technology. In these devices, magnetic sensors are combined with magnetic particles as reporters and microfluidics for sample transport. Herein, a signal analysis method is used to aid the identification of the number of magnetically labeled bacteria associated with each detection event. Comparing the sensor output signal (pulse amplitude versus pulse-width analysis) with simulations, we have developed a reliable classification method, based on a MATLAB algorithm, to identify the number of magnetically labeled bacteria. Discrimination of the number of bacteria from the sensor event signatures improves the accuracy of the detection system.