Bio-physical modeling of time-resolved forward scattering by Listeria colonies

Abstract

We have developed a detection system and associated protocol based on optical forward scattering where the bacterial colonies of various species and strains growing on solid nutrient surfaces produced unique scatter signatures. The aim of the present investigation was to develop a bio-physical model for the relevant phenomena. In particular, we considered time-varying macroscopic morphological properties of the growing colonies and modeled the scattering using scalar diffraction theory. For the present work we performed detailed studies with three species of <i>Listeria; L. innocua, L. monocytogenes</i>, and <i>L. ivanovii</i>. The baseline experiments involved cultures grown on brain heart infusion (BHI) agar and the scatter images were captured every six hours for an incubation period of 42 hours. The morphologies of the colonies were studied by phase contrast microscopy, including measurement of the diameter of the colony. Growth curves, represented by colony diameter as a function of time, were compared with the time-evolution of scattering signatures. Similar studies were carried out with <i>L. monocytogenes </i>grown on different substrates. Non-dimensionalizing incubation time in terms of the time to reach stationary phase was effective in reducing the dimensionality of the model. Bio-physical properties of the colony such as diameter, bacteria density variation, surface curvature/profile, and transmission coefficient are important parameters in predicting the features of the forward scattering signatures. These parameters are included in a baseline model that treats the colony as a concentric structure with radial variations in phase modulation. In some cases azimuthal variations and random phase inclusions were included as well. The end result is a protocol (growth media, incubation time and conditions) that produces reproducible and distinguishable scatter patterns for a variety of harmful food borne pathogens in a short period of time. Further, the bio-physical model we developed is very effective in predicting the dominant features of the scattering signatures required by the identification process and will be effective for informing further improvements in the instrumentation.