Quantitative microbiological models predicting proliferation of microorganisms relevant for food safety and/or food stability are useful tools to limit the need for generation of biological data through challenge testing and shelf-life testing. The use of these models requires quick and reliable methods for the generation of growth data and estimation of growth parameters. Growth parameter estimation can be achieved using methods based on plate counting and methods based on measuring the optical density. This research compares the plate count method with two optical density methods, namely, the 2-fold dilution (2FD) method and the relative rate to detection (RRD) method. For model organism Bacillus cereus F4810/72, the plate count method an...
AbstractTitration of microorganisms in infectious or environmental samples is a corner stone of quan...
When bacteria are exposed to osmotic stress, some cells recover and grow, while others die or are un...
Partial support for this work was provided by NSF Expeditions in Computing Program Award #1522074 as...
Quantitative microbiological models predicting proliferation of microorganisms relevant for food saf...
The selection of a primary model to describe microbial growth in predictive food microbiology often ...
Copyright © 2015 Maŕıa-Leonor Pla et al. This is an open access article distributed under the Creat...
The time-to-detection (TTD) method is a rapid and high throughput approach for the estimation of mic...
The spectrophotometer has been used for decades to measure the density of bacterial populations as t...
Temperature effect on growth rates of Listeria monocytogenes, Salmonella, Escherichia coli, Clostrid...
The aim of this study was to evaluate the suitability of several mathematical functions for describi...
Growth curve measurements are commonly used in microbiology, while the use of microplate readers for...
A method for accurate quantification of growth rate and yield of bacterial populations at low densit...
A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many ...
When building models to describe the effect of environmental conditions on the microbial growth rate...
The modified Gompertz model was applied for predicting the growth of Bacillus cereus under various c...
AbstractTitration of microorganisms in infectious or environmental samples is a corner stone of quan...
When bacteria are exposed to osmotic stress, some cells recover and grow, while others die or are un...
Partial support for this work was provided by NSF Expeditions in Computing Program Award #1522074 as...
Quantitative microbiological models predicting proliferation of microorganisms relevant for food saf...
The selection of a primary model to describe microbial growth in predictive food microbiology often ...
Copyright © 2015 Maŕıa-Leonor Pla et al. This is an open access article distributed under the Creat...
The time-to-detection (TTD) method is a rapid and high throughput approach for the estimation of mic...
The spectrophotometer has been used for decades to measure the density of bacterial populations as t...
Temperature effect on growth rates of Listeria monocytogenes, Salmonella, Escherichia coli, Clostrid...
The aim of this study was to evaluate the suitability of several mathematical functions for describi...
Growth curve measurements are commonly used in microbiology, while the use of microplate readers for...
A method for accurate quantification of growth rate and yield of bacterial populations at low densit...
A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many ...
When building models to describe the effect of environmental conditions on the microbial growth rate...
The modified Gompertz model was applied for predicting the growth of Bacillus cereus under various c...
AbstractTitration of microorganisms in infectious or environmental samples is a corner stone of quan...
When bacteria are exposed to osmotic stress, some cells recover and grow, while others die or are un...
Partial support for this work was provided by NSF Expeditions in Computing Program Award #1522074 as...