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These exercises relate closely to Stephen Forsythe's Microbiology of Safe Foods 2nd Edition.

Temperature Treatment
Predictive Microbiology
Microbiological Risk Assessment
Microbial Bioinformatics (6.5 MB pdf file download)


Temperature Treatment

See chapter 2, section 2.5 for background information and similar examples. Use the tables referred to in the book to complete the following exercises.

Exercise 1

D value determination for E. coli O157:H7 in beef at 60oC.

Time (min)

Log10viable count (cfu/g)

















Calculate the D value of the organism under these conditions and compare with the values given in Table 2.3.

Note: The initial minute values are ignored in the best fit determination as these are probably due to sublethal injury (Chapter 5, Section 5.2.2) causing a 'shouldering' effect on the viable count.


Exercise 2

Z value determined for Campylobacter jejuni

Temperature (oC)

Log10 D value (min)











Calculate the Z value of the organism and compare with the values given in Table 2.3.


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Predictive Microbiology

(See chapter 2, section 2.8.5 for background information and figures for similar examples).

Exercise 3

Using the Pathogen Modeling Program choose the following settings:


Organism : Yersinia enterocolitica

Aerobic conditions

Temperature 20oC

pH 6.5

Sodium chloride 3.5 (Water activity = 0.98)

Sodium nitrite 150

Initial inoculum value (log10) 3.0

Level for concern:(log10) 6.0

Time scale in hours

Go to File to check 'Show Graph'


Note the time to reach the level of concern is 32.2 hours.

(1) Alter the pH to values between 4.5 and 6.0 and see the effect on the predicted growth curve. Why does the program not allow you to go below pH 4.5? (See Table 2.8).

(2) Return the pH to 6.5 and alter the temperature to 8oC (recommended refrigeration temperature) and 10oC (poor refrigeration).

Note the affect on time to reach the level of concern. Also note that this organism, unlike most foodborne pathogens, is able to grow under refrigeration temperatures.

(3) Repeat the steps above Salmonella as the chosen organism as a comparison.

Compare the predicted results with those for these organisms in Table 2.8.

(4) Reduce the inoculum level to 100 (log2) organisms and note the time to reach level of concern. Further reduce to 10 (log101) and 1 (log100) organism and compare the time taken to reach the level of concern.


Although the model is very useful for illustrating principles of microbial growth it is not perfect. An example is that if Salmonella is selected then the program does not predict any growth for the organism under anaerobic conditions. However Salmonella is a facultative anaerobe.

Note: A 100-fold decrease in initial inoculum size does not cause a 100-fold delay in the time to reach level of concern. This is because micro-organisms grow exponentially. Hence an increase from 1 - 2 - 4 - 8 - 16 - 32 - 64 - 128 (ie approx. 100-fold increase)is equal to 7 doubling times. If the doubling time is 30 minutes then 7 doublings would take 7 x 20 minutes = 210 minutes (3.5 hours). See Table 2.8 for examples of doubling times.


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Microbiological Risk Assessment

(See chapter 9, section 9.5 for background information and figures for similar examples).

Exercise 4

This examples focuses on the data given in Section 10.1.5

(a) Using published data on the incidence of Salmonella spp. in raw chicken the figure 10.6 is obtained.

(b) The D value at 60oC is 0.4 minutes.

Hence a cooking period of 3 minutes at 60oC is equal to:

3.0/0.4 = 7.5 decimal reductions, or approx. 7D kill

A 7D kill means:

108 Salmonella/g being reduced to 101 Salmonella/g

and also:

105 Salmonella/g being reduced to 0.01 Salmonella/g, which is 0.1 Salmonella/10g, or 1 Salmonella/100g


By calculating the decimal reduction for the Salmonella population spread the incidence of surviving Salmonella is determined. This is not an easy calculation and is why programs such as @RISK (Palisaide-Europe) are invaluable. Subsequently the surviving Salmonella are able to multiply in the time period during storage (use the Pathogen Modeling Program to investigate this and also refer to Tables 2.3 and 2.8).

In summary this predicts that 1% of the chicken samples had sufficient numbers of Salmonella that survived the cooking process to give a probability of infection (Pi) of 4.1 x 10-8/g food consumed. See Section 9.1.4 for an explanation of Pi.

Note: There are relatively few studies giving the relative spread of microbial counts in food products. Most studies give the range only.



Microbial Bioinformatics

Click here for a 6.5 MB pdf file of exercises for you to work through; bioinformatics exercises.

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Society for Applied Microbiology (SfAM) Food safety in the future.Food Safety Summit