Rapid Cellular Identification by Dynamic Electromechanical Response

Abstract

Coupling between electrical and mechanical phenomena is ubiquitous in living systems. Here, we demonstrate rapid identification of cellular organisms using difference in electromechanical activity in a broad frequency range. Principal component analysis of the dynamic electromechanical response spectra bundled with neural network based recognition provides a robust identification algorithm based on their electromechanical signature, and allows unambiguous differentiation of model Micrococcus Lysodeikticus and Pseudomonas Fluorescens system. This methodology provides a universal pathway for biological identification obviating the need for well-defined analytical models of Scanning Probe Microscopy response.