ANS:  The models were developed over specific ranges of environmental variables. For example, the Aerobic A. hydrophila in Culture Media model was developed over a temperature range of 41°F (5°C) to 107.6F (42°C). The accuracy of predictions made inside (interpolation) this range is known. However, the accuracy of predictions made outside (extrapolation) of this range (e.g., 150°F) is not known, and the software does not permit such values to be entered.

         To print the table, place you mouse over the table, right-click, select “Select All”, then right-click again and select “Print Selected”. To print only specific portions of the table, highlight the cells that you want to print, then right-click and select “Print Selected”.

          To print the entire PMP form, in the Menubar select “File” then “Print” then “Form”. Note - this will not print portions of the table or publication windows that are not visible. To print all of the publication information, highlight the text with your mouse, then hold the CTRL key and press the C key. You can then paste this text into any other document which accepts pasted text, such as Microsoft Word®, NotePad, Correl WordPerfect®, etc.

ANS: 1. The reference(s) or source for each model, is shown in the “Source and/or Relevant Publications” box. For references in which the senior author(s) is a USDA-ARS employee, we provide the PDF file. For articles with other types of authorship, the reference is not provided as a PDF file, due to journal copyright restrictions. Note - there may be multiple publications for a model. Typically this is because different environmental parameters were modeled at different periods of time.

ANS: Currently, there are dynamic temperature models for C. botulinum and C. perfringens, and static temperature models for other pathogens. We are in the process of producing dynamic temperature models for a variety of other pathogens, and expect these to be available in future versions of the PMP.

To use a static model for situations where temperature changes over time, you will need to calculate the growth at specific temperatures, and then add these individual growth calculations to determine the total predicted growth over the entire time-temperature range. For example, suppose you want to predict the growth of L. monocytogenes in a product that has the following time-temperature profile:
                                     0.0 hours 98.6°F
                                     0.5 hours 71.2°F
                                     1.0 hours 63.4°F
                                     2.0 hours 50.1°F

For fail-safe predictions, we will assume that the product was at 98.6°F for 0.5 hours. At 71.2°F for 1 hour, and at 63°F for 2 hours. More conservative estimations can be made if you collect time-temperature data in shorter time intervals.

First, set the environmental conditions to match your product. In this example we will use the aerobic broth culture L. monocytogenes model for NaCl. Set the conditions to: pH=6.5, NaCl=1.0%, 0 ppm nitite. 

Next, set the temperature to 98.6°F (37°C), then click the box “Calculate Growth Data”. Next, click the “No Lag” box (for more fail-safe predictions). Since we are assuming that the product was at 98.6°F for 0.5 hours, we subtract log 3.0 (from the Initial Level) from the “log(CFU/ml)” value at 0.5 hours. However, you’ll notice that there is no value given at 0.5 hours. Therefore, average the count for 0.4 (3.64 log[CFU/ml]) and 0.6 hours (3.76 log[CFU/ml]). This would equal (3.64 + 3.76)/2 = 3.70.

Next do the same calculation at 71.2°F for 1 hour, keeping the initial level at 3.0. The prediction at 1.0 hour is 3.63 log(CFU/ml). Therefore, subtract 3.0 from 3.63, and this equals 0.63 log(CFU/ml).

ANS:  For growth models, chose the “No Lag” prediction option. This option does not add a Lag Time to the growth scenario. Also, choose models that were developed in a sterile broth system. Typically, the Generation Times/growth rates observed in broth media under optimum conditions for NaCl/water activity and pH are equal to or greater than that observed in food.

         For inactivation models, more fail-safe predictions can be attained by setting the environmental parameters to values that predict a greater inactivation rate.

ANS:  Without experience in the use of models, it is difficult to know if the model you use is over- or under-predicting bacterial growth or inactivation when applied to another food matrix. As such, it is best to use models to understand potential trends in bacterial behavior as the environmental conditions change. Only through validation studies (e.g. inoculated pack studies) would you be able to have confidence in model interpretation for your food of interest.

ANS:  In general, this situation is most relevant to growth models. Depending on the pathogen, spoilage flora (e.g., bacteria, fungi) can markedly inhibit the growth of pathogenic bacteria. This is especially apparent at refrigerated temperatures where the growth rates of psychrotrophic (cold-loving) organisms may be greater than that of the pathogen. Therefore, in these situations, the maximum density (level at stationary phase) of a pathogen may be 3 to 5 log10 levels less that observed in a pure culture. Also, the growth rate may be inhibited. Therefore, in general, Generation Time will be shorter and growth rates and maximum population densities will be higher in sterile culture systems compared to systems containing spoilage flora.

ANS:  The Lag Phase is the time required for the cell population to adjust to the food environment and begin growth.

          The Lag Phase is the most unpredictable aspect for a growth model, compared to the Generation Time. This is because the Lag Phase will vary depending on the previous “history” of the organism. For example, the Lag Phase Duration (LPD) of bacteria grown at 98°F in culture medium and then transferred to ground beef at 50°F will be different than the LPD of bacteria grown at 70°F. This is because the previous environment of the bacteria will result in different cellular changes that are necessary before it can again grow in a different environment.

          The LPD represents a distribution of lag times for individual cells in the matrix. As you notice on most growth curves, this produces a concave-shaped curve [See growth curve at right] between the lag phase and the growth phase. Consequently, a portion of this curvature is included in the calculated lag phase and a portion is included in the growth phase.

ANS:  Selecting the “Lag” option will result in a prediction of the Lag Phase Duration based on the experimental data for the model.

          Selecting “No Lag” will remove the period of time for the calculated Lag Phase, and will begin predictions with growth of the bacteria. Note - a portion of the curvature between the LPD and the Growth Phase is included in the Growth Phase. Therefore, the starting level that appears in the chart and table will not be the same as the value that you set as the Initial Level. The time calculated to reach the Level of Concern will use the Initial Level, although it will not appear in the table or chart.

ANS:  The Upper (UCL) and Lower Confidence Limits (LCL) indicate the variation in the predictions at a confidence level of 95%. If you are looking for more fail-safe predictions, then use the UCL. Use the LCL if you want more conservative estimates.

ANS:  This is an arbitrary value that you can set to indicate the initial level of bacteria in the sample at the beginning of the growth scenario. The lowest and highest values that you can select are restricted based on the levels used in the experimental data that form the model.

ANS:  Level of Concern is an arbitrary level that you select for a target level of growth. The calculated time to reach the Level of Concern is shown in the box labeled “Time to Increase [Level of Concern minus Initial Level] logs”.

ANS:  The minimum level of a pathogen that poses a health risk depends on many factors, such as the susceptibility of the affected population and the quantity of food that is consumed. We do not indicate any specific level for a pathogen.

ANS:  In general, bacterial numbers decline when the product is frozen. The extent of decline is based on various factors, such as the freezing temperature and the rate of temperature change to reach a frozen state.

ANS:   Water activity of food is a measure of the level of water that is not tightly bound to the food matrix, and that is available for bacterial growth. This value varies from 0 to 1, with most hazardous foods being in the range of 0.85 to 0.99. This value is based on all of the salts, not just NaCl, that may be in the food matrix. Consequently, if you add say 1% NaCl to a food, you can enter this value in the PMP input box. However, the calculated water activity level is a reflection of this level of NaCl and not that of the total level of salts that are in a food. For this reason, we advise that you measure the water activity of your product and then change the value in the input box to match this value.

a_W = 1 - (0.00519 * [NaCl]) - (0.000155 * [NaCl]² )

ANS: Click on the Reference tab on the File menu and select the article "Model Development". This article provides an abbreviated overview about model development. Also, in the same Reference tab, you can select "Publications List" and look for other publications that we offer in PDF format or as a citation. Another source, although more technical in nature, is a book titled Predictive Microbiology: theory and application by McMeekin et al. (1993). A good book on general Food Microbiology is Modern Food Microbiology by James Jay (2000).

ANS:  The published literature is a good source of information about bacterial behavior in food. However, the behavior of bacteria has not been described for the majority of formulated foods. Another source for data is a free on-line database called ComBase, located at www.wyndmoor.arserrc.gov/combase/. This database contains thousands of records for various pathogenic and spoilage bacteria in many different environmental conditions.

ANS: Models are typically produced from laboratory data where a food or microbiological broth is inoculated with a single bacterial species or a mixture of bacteria. If the researcher would like to model the effects of intrinsic factors of a food/broth, then a range of these factors are added to the sample, such as by adding various levels of salt, organic acid, or preservative. Next, the samples are incubated at various temperatures and the bacterial levels are measured at predetermined time intervals. After the experiments, the data are organized and the a curve is fitted to the data. This is termed Primary Modeling. In the case of growth, parameters for the lag time (if present), growth rate (or generation time) and the maximum population density are calculated. In the case of bacterial inactivation, the lag time (if present) and inactivation rate are calculated.  In Secondary Modeling, the change in a specific parameter is modeled as a function of a change in the environment (e.g., temperature, salt/water activity, pH) is modeled. The variation in predictions (upper and lower confidence levels) are also calculated. Finally, all of these data are developed into a model interface, like the PMP, so that users can easily make predictions of bacterial behavior. For more information on model development, go to the Toolbar, select References and click on “Model Development.”

ANS: While the model window is open, the reference(s) or source of the information can be found in the “Source and/or Related Publications” window.

ANS: 1.  The PMP models are only valid for the conditions used to produce the model. The reference(s) found in the “Source and/or Related Publications” window presents an explanation for the methodologies used to produce the model. Therefore, if the conditions (e.g., food formulation) used to produce the PMP model do not match your food system, then you must validate the model for your specific application. Validation normally involves laboratory studies where your product is inoculated with a specific bacteria and then you record the levels of growth or inactivation. These data can then be compared to the PMP model predictions to see if they are within the predicted 95% confidence intervals. If they do not match, then the PMP model is not valid for your application. In this case, assuming sufficient experimental data have been collected, your data may be used to develop a new model that it would be valid for your food product.