Τίτλος:
Heterogeneity of single cell inactivation: Assessment of the individual cell time to death and implications in population behavior
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
A direct microscopic time-lapse method, using appropriate staining for cell viability in a confocal scanning laser microscope, was used for the direct assessment of Salmonella Agona individual cell inactivation in small two-dimensional colonies exposed to osmotic stress. Individual cell inactivation times were fitted to a variety of continuous distributions using @Risk software. The best fitted distribution (LogLogistic) was further used to predict the inactivation of Salmonella populations of various initial levels using Monte Carlo simulation. The simulation results showed that the variability in inactivation kinetics is negligible for concentrations down to 100 cells and the population behavior can be described with a deterministic model. As the concentration decreases below 100 cells, however, the variability increases significantly indicating that the traditional D-value used in deterministic first order kinetic models is not valid. At a second stage, single cell behavior was monitored in larger three dimensional colonies. The results showed that colony size can affect the inactivation pattern. The effect of colony size on microbial inactivation was confirmed with validation experiments which showed a higher inactivation rate for populations consisting of single cells or small colonies compared to those consisting of cells organized in larger colonies. © 2019 Elsevier Ltd
Συγγραφείς:
Aspridou, Z.
Balomenos, A.
Tsakanikas, P.
Manolakos, E.
Koutsoumanis, K.
Περιοδικό:
Food Microbiology
Εκδότης:
INSTAP Academic Press
Λέξεις-κλειδιά:
bacterial count; biological model; biological variation; confocal microscopy; kinetics; microbial viability; osmotic pressure; physiology; Salmonella enterica; statistical model; time lapse imaging, Biological Variation, Individual; Colony Count, Microbial; Kinetics; Microbial Viability; Microscopy, Confocal; Models, Biological; Models, Statistical; Osmotic Pressure; Salmonella enterica; Time-Lapse Imaging
DOI:
10.1016/j.fm.2018.12.011
