Bacterial phenotype identification using Zernike moment invariants

Abstract

Pathogenic bacterial contamination in food products is costly to the public and to industry. Traditional methods for detection and identification of major food-borne pathogens such as Listeria monocytogenes typically take 3-7 days. Herein, the use of optical scattering for rapid detection, characterization, and identification of bacteria is proposed. Scatter patterns produced by the colonies are recognized without the need to use any specific model of light scattering on biological material. A classification system was developed to characterize and identify the scatter patterns obtained from colonies of various species of Listeria. The proposed classification algorithm is based on Zernike moment invariants (features) calculated from the scatter images. It has also been demonstrated that even a simplest approach to multivariate analysis utilizing principal component analysis paired with clustering or linear discriminant analysis can be successfully used to discriminate and classify feature vectors computed from the bacterial scatter patterns.