BACANOVA Project

OBJECTIVES

The BACANOVA project aims to develop and validate novel mathematical techniques to improve the prediction of bacterial lag time.

The project has three main objectives:

To develop a methodology that takes into account the variability of individual cells and complements the current techniques of predictive microbiology.
To predict more accurately the probability of microbial survival, lag and growth in food.
To optimise the effect of processing methods with respect to microbial safety and quality of the food

WORKPLAN

The Bacanova project will involve:

Using carefully controlled environmental growth conditions in laboratory growth media to acquire good quality data on the distribution of individual lag times of vegetative cells and spores;
Examining the effect of history (past treatment) and growth (recovery) conditions on these distributions;
Applying advanced stochastic mathematical modelling techniques to characterise the effect of history and growth conditions on lag times;
Using the model to optimise pre-treatment and to predict the microbial response;
Validating the model predictions in food.

The work is divided into 7 work packages

WP1. Direct measurements (image analysis) of individual lag times of cells/spores

The most direct way to measure the lag times of individual cells is to observe them using a microscope. Images of cells will be grabbed at intervals during lag phase and growth up to the first cell division, and subsequently analysed using image analysis software. These techniques will be used to study the effect of both pre-treatment and growth conditions on the distribution of lag times in vegetative cells and spores.

WP2. Indirect measurements: detection times of subpopulations generated by single cells.

Instead of observing cells directly, it is possible to determine their lag indirectly by measuring the time to a set point on the growth curve of a population generated from a single cell. The distribution of times to detection is a projection of the lag times. A flow cytometry cell sorter will be used to deposit single cells into separate wells of microtitration plates. The time to turbidity (approximately 106-107 cells ml-1) will be measured for each well and used to model the distribution in lag times. The distributions obtained using this indirect single cell method will be compared with those obtained using direct methods (WP1) to insure there are no discrepancies.

WP3. Indirect measurements: detection times of subpopulations generated by different inoculum levels

Food processing treatments or adverse growth conditions may kill or prevent growth from cells as well as altering their lag time distribution. As the distributions are of cells that grow, it is the number of viable cells and not the total number of cells observed that is important. The problem with using one cell per well (or viewing each individual microscopically) is that the amount of data decreases if the treatment kills some cells or prevents them from growing. For example, if a population of cells is heated so that only 1 in 50 survived and a single cell is placed into each of 100 wells of growth media, there will be only two measurements of detection time. The alternative to using a cell sorter is to maximise the growth data obtained by using serial dilution to get an average of one viable cell per well. This method is technologically simple but is less accurate than cell sorting as the observed subpopulations are not necessarily generated by a single cell. The number of viable cells per well follows a Poisson distribution and extensive mathematical modelling will be required.

Bacteria will be subjected to different stresses, serially diluted to an average of one viable cell per unit of growth medium and grown in different conditions (temperature, pH, NaCl). Distributions for serially diluted cells grown in low stress conditions will be compared to those obtained using a single cell per well (WP2) to insure there are no discrepancies.

WP4. Mathematical/statistical analysis of the measured distributions

A database will be created to allow all the data from the project to be entered using the same syntax. Stochastic mathematical modelling techniques will be used for fitting distributions and error estimations. ANOVA type techniques will then be used to analyse the data and test for significant differences between distributions.

Listeria cells

WP5. Stochastic mathematical modelling of the effect of history and growth conditions

The distributions of lag times will be used to create a parameter representing the physiological state of the cells. This parameter will then be combined with the specific growth gate of the organism to create new deterministic predictive models that take account of the physiological history of the cells as well as the current environmental conditions. The model will be equipped with error estimations and tested using Monte-Carlo type methods.

WP 6. Validation studies in food

The distributions of time to growth obtained from direct or indirect measurements in culture media will be compared to results in real food. The distribution of lag times in samples inoculated with a single cell will be obtained using a "vertical distribution". Packs of food inoculated with single cells will be sampled at a given time when growth in most packs is in early exponential phase. The number of bacteria present at sampling will depend on the time at which growth started. The distribution of numbers of cells per pack at that time will reflect the distribution of lag times.

WP 7. Summary of the results in a computerised format; papers and reports

All the experimental data will be computerised and included in the database. The results of the project will also be reported to the Commission and described in scientific papers.

PROJECT OUTPUTS

Information on the effect of cell history on individual cell lag time distributions will be published in scientific literature.
New mathematical tools to describe the growth of bacteria in food and improve predictive modelling will be developed.
A systematic database of bacterial responses to food environments will be created in a browsable format and posted on an IFR Web site.
Guidelines aimed at food manufacturers will be produced detailing how the outputs of the project can be used to improve food processing.

The authors are solely responsible for the information contained in this web site. It does not represent the opinion of the European Commission, and the Commission is not responsible for any use that might be made of data appearing therein.