ComBase TUTORIAL for ComBase Browser and Predictor

Transcription

1 ComBase 2015 TUTORIAL for ComBase Browser and Predictor 1. First we show examples on the tens of thousands of records that constitute the core of the ComBase system. This database is available by a Browser from the ComBase home page: [Login and go to ] For the example organism, select Listeria from the pull-down menu by clicking the arrow of the box next to the organism or type the first few letters of Listeria and the browser automatically gives you the available options. You can select more organisms, too. Clicking Add another field, you can select the (food) matrix you are interested in, and other environmental conditions for the bacterial response. The Static or Dynamic option indicates whether the changes in the environmental conditions (primarily the temperature, ph and water activity) were recorded during the observation In this example we want to know what data are available for Listeria and E.coli in either cheese or culture medium between zero and 15 Celsius where the temperature was constant (static) during the observation: Click on Search. The resultant screen shows that more than 3700 records satisfy the query. The returned records are shown in pages, each page containing ten records. Page 1 (out of >370) starts with the low temperature data, with extracts of available information on each record and a small plot on the response, as logcount v. time(h)

2 2. If the plot on the output screen shows a straight line then the available response is the growth/death rate, represented by the slope of the line. If the line is horizontal, then no significant change was detected in the log cell concentration during the observation time. Observe the? symbol that can provide help; also the Export button that can export the data to csv files for further processing

3 3. If the plot shows points, then logcount measurements were recorded. In this case you may want to do a quick estimation of the growth/death rate yourself. You can do it by clicking the heading of the record and selecting a fitting method

4 4. Also you can compare the data with predictions. Note that predictions are based on data, generated on optimum culture medium. These data are in the ComBase database, too, ca 2500 records for the ca 30 models. By using the slides for the different environmental factors, this screen makes it possible to test how much environmental change would give the predicted rate and lag; a visualisation of the confidence in the prediction

5 5. This leads us to a case study demonstrating the use of the other main tool, the ComBase Predictor, that can be accessed via the navigation menu. Select Listeria again, compare their growth at 8 and 13 degrees. You can establish that (the other factors, ph, Aw being optimal), 4 C decrease in storage temperature may ca half the growth rate

6 6. As an exercise, find out how much added salt could provide the same decrease in growth rate. Click on the NaCl button to rescale the slide on humectant and adjust it in such a way that the lower predictions cover each other. You can see that under these conditions, >6% NaCl could provide the same inhibitory effect than 4 C less temperature. Note that the water activity NaCl conversion assumes NaCl solved in water, so this conversion is not necessarily valid in food. The Aw measure is the generic one; even unsalted food can have very low water activity (e.g. other humectant as added or the food got dried). Notice that here we clicked on the upper left corner of the window, that leave only icons (not the text) for the menu visible, this way leaving more space for the figure

7 ADVANCED TOPICS A1. Uncertainty measurements can also be visualized by clicking the Uncertainty tab. This plot shows the band ( logcount/h) in which the growth rate falls with ca 90% probability. Next to it the respective growth curve can be seen. You may study the effect of the environmental factors on the uncertainty measure by using the provided slides

8 A2. An important uncertainty measure is that on the so-called physiological state (α 0 : Alpha0). This a number between 0 and 1; it expresses that α 0 fraction of the inoculum could have resulted in the exponential phase at the observed time had that fraction started to respond immediately, without lag. The lag / shoulder time is in fact -ln(α 0) / μ where μ is the specific growth/death rate (rate on the natural log scale, i.e. ca 2.3-times of the rate observed on the log10 scale)