Rapid Cellular Identification by Dynamic Electromechanical Response

Quick and reliable identification of individual prokaryotic organisms and cellular types is of utmost importance for various applications. A number of strategies for cellular identification are currently used to meet this challenge. All of the existing techniques require culturing bacteria prior to measurement, which increases the time needed for experimentation and analysis considerably. Here, we report on a method for rapid cellular identification and mapping using the detection of broadband electromechanical response. Electromechanical spectra from \textit{M. Lysodeikticus} and \textit{P. Fluorescens} deposited on PLL-coated mica were collected over wide frequency range. Principal component analysis of the spectra bundled with neural network analysis provides a robust algorithm for identification of the cellular organisms based on their electromechanical properties.