Welcome to MicBase!

MicBase is a food microbial response database hosted by the Bioinformatics Group at Cranfield University. It is based on an application developed by Michael Nandris for his MSc thesis project (Design and Development of a Food Microbial Responses Database), supervised by Dr Fady Mohareb, and completed in January 2014. Said application was further based on one developed using VBA in Microsoft Excel by Psomas et al. at the Agricultural University of Athens, Greece.

You can contact the administrator at


Accessing data

Tables of datasets and subsets available in MicBase can be viewed by clicking the respective Datasets or Subsets tabs at the top of the page. The individual data points which make up subsets cannot be viewed in the current application.

At present additional datasets can only be added to MicBase by the administrator. If you would like to add a dataset to MicBase, please contact him via e-mail.

Filtering data

The MicBase data tables initially display all their data. This can be narrowed down via filtering. Select the text field at the top of any data column and enter some text. The data in the table below will be automatically narrowed down to include only sets matching your filter criteria.

Plot generation

To generate a scatter plot representing all data points in a subset, select a subset in the subset data table and click the Open plotter button at the bottom of the page. The plot should appear in a dialog box shortly. You can select multiple subsets by holding down CTRL or SHIFT.

To attempt to fit a primary growth model (Gompertz, Baranyi, or Buchanan) to the data displayed on a scatter plot, click the name of the model next to the plot. A curve (or curves, if more than one subset is selected) representing the model fit should appear on the plot shortly. Fit parameters should also appear next to the plot key on the right. Note that since the nonlinear regression depends on randomised initial parameters, the exact results of the fit can be different in different attempts.

Once a primary growth model is successfully fitted to the data, a secondary (Ratkowsky) model can be selected to try to fit the primary model parameters to a secondary model representing the change of growth rates as a function of temperature. A basic assumption of this model is that all parameters and conditions of the primary growth curves other than the temperature are constant.

Changelog (v0.3)

To-do list