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2/10/2015 A primer on risk assessment modelling: focus on seafood products

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Title: A primer on risk assessment modelling: focuson seafood products... ZIP version

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3. Harvesttoconsumption modelsIn order to describe a food system, the model generated could include a number of stages. Thesestages are associated with the scope of the system we wish to describe and can include the eventsthat begin prior to the product being harvested and all the events until the product is consumed.These can include contamination pathways that may exist in the environment where the productnaturally exists, contamination events that could occur while the product is being processed, or, forinstance, the effect that consumer behaviour may have on the product. The entire system can bedescribed using a modular approach that focuses on specific sections of the food chain system.

In this section we provide an overview of the various stages or modules that could be developed. Aconvenient separation of the harvest to consumption chain can be performed along the followinglines: preharvest models; harvest models; handling and processing models; storage and distributionmodels; and preparation and consumption models. The separation into these stages is done forconvenience and logic purposes, but does not always have to be done in this way.

The starting point along the chain for the modelling exercise is dictated by the decision supportinformation that is being sought by the modelling exercise. For instance, assume the goal of theexercise is to gain a better understanding of mitigations strategies. Further, assume that the systemis such that the events that occur prior to harvest are beyond the control of any available technologyor system. In this case, developing a preharvest model would be noninformative, and as a result,starting at the harvest stage would be a more reasonable approach.

3.1 PREHARVEST MODELS

Preharvest models are concerned with the estimation or description of the introduction orpropagation of a hazard in the products within their rearing environments. In the case of seafoodproducts, this stage would include, for instance, all the events prior to the fish, or shellfish beingcaught and loaded onto the shipping vessel. In the preharvest stage, the modelling that is done isprimarily concerned with generating an estimate at the point of harvest, for the level of the hazardpresent on or in the product, and the frequency, or how often, the product is contaminated.

The type of information required by the modelling exercise should dictate the complexity of themodelling, similar to the decision on the starting point for the model. To illustrate, let’s assume that ahazard present in a fish species is the result of contamination occurring somewhere inland. Thiscontamination is then transported to the watershed via some subterranean transport processes. Thecontamination transported to the watershed eventually gets into the fish species that are thenharvested and sent to market. Modelling the contamination of fish could proceed along two potentialdirections: First, the contamination at the original source could be used and incorporated withcomplex subterranean transport modelling to arrive at an estimate of the concentration in thewatershed, which would then be combined with additional modelling to estimate the level ofcontamination in the fish. Alternatively, the model could begin with the level in the watershed andskip the original source level and subterranean modelling.

These two approaches illustrate the logic process that should be considered prior to commencing on

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the development of the model. If there is no interest at the decisionmaking level, or no action thatcan be taken prior to the watershed contamination levels, then the second approach (starting at thewatershed contamination level) should be used, because this approach requires the least additionalmodelling and associated assumptions to be taken. In some cases the data, and not the decisionmaking criteria, will decide on the starting level and complexity of the modelling process. Forinstance, if there is no data on watershed contamination levels, and there is data on original sourcecontamination levels, then the more complex approach may be employed, even though there are noactions that can be taken prior to the watershed level.

It is not possible to provide a catalogue of all the different models that could be used in a preharvestmodule. The key concept that needs to be kept in mind, is that the ultimate goal of the module is toattempt to derive an estimate of the level and frequency of contamination at the point that theproduct is harvested. As long as the key outputs we are attempting to estimate are remembered, thespecific models that are employed are essentially limitless.

To illustrate the type of modelling that might be used in a preharvest model, the following examplefrom the United States Food and Drug Administration (FDA) risk assessment for Vibrioparahaemolyticus in shellfish is summarized (FDA, 2001).

3.1.1 Example (preharvest module)

The FDA preharvest module focuses on estimating the likelihood that shellfish in a growing areawill contain diseasecausing (pathogenic) strains of V. parahaemolyticus and the levels at which theywould be found.

The FDA preharvest module starts with a review of the various pathways of introduction of V.parahaemolyticus into shellfish growing areas and in shellfish. These pathways of introduction aredescribed and include the release of ballast water or natural introduction by terrestrial and aquaticanimals. In addition to the introduction of the pathogen into the growing areas, the assessment alsodescribes and characterizes the factors that have an influence on the survival and establishment ofthe pathogen in the growing areas. Issues such as temperature, weather patterns, salinity, tidalflushing and other predictive parameters are identified.

As an illustration of how the complexity of the model is adjusted based on the informationrequirements and the data availability, the FDA risk assessment does not model all the parametersidentified that could have an effect on the introduction or establishment of the pathogen in thegrowing areas. The FDA risk assessment acknowledges that although there are a number of factorsthat have been identified as potentially affecting the levels of pathogenic V. parahaemolyticus inoysters at time of harvest, there is insufficient quantitative data available to incorporate all of thesefactors into a predictive model.

The FDA risk assessment notes that in order to incorporate an environmental factor into thesimulation as a predictor of V. parahaemolyticus densities at harvest, it is necessary to identify boththe relationship of V. parahaemolyticus densities to the parameter of interest, and the regional andtemporal variation of the parameter within the environment. As an example, if we were toincorporate the effects of weather on V. parahaemolyticus densities, we would have to determinethe actual relationship between weather and V. parahaemolyticus density, as well as a predictivemodel for the changes in weather in different locations and at different times. Obviously, this could insome cases be very useful; however, given the complexity that would be required and the lack ofany quantitative information upon which to base the estimates, it is not very fruitful to pursue thismodelling pathway.

The FDA risk assessment generated a model that considers two primary components asdeterminants of the level of V. parahaemolyticus in oysters at harvest. According to researchhighlighted in the risk assessment, the effects of water temperature and water salinity areconsidered to be the most important parameters. Figure 3.1 shows the structure of the model andthe parameters considered.



Figure 3.1Schematic for FDA V. parahaemolyticus preharvest model.

The preharvest module reviewed the best available data on the relationship of total V.parahaemolyticus densities in oysters (and water) versus water temperature and salinity anddetermined that a study by DePaola et al. (1990) to be the most appropriate. This study examinedseasonal changes and collected samples from all four regions of the United States (i.e. Northeast,Gulf Coast, MidAtlantic, and Pacific Northwest). The risk assessment looked at other studies andnoted that while there had been several other surveys of V.parahaemolyticus between 1982 and1995, these studies were typically limited to specific regions and/or seasons, and few had reportedquantitative data. Typically, data on the presence or absence of detectable V. parahaemolyticus is oflimited value in developing a quantitative risk assessment.

The preharvest FDA module generated a model that characterized the effect of temperature on themean log10 total V. parahaemolyticus densities. This relationship was found to be approximatelylinear over the range of environmental water temperatures. With regard to salinity, a quadratic effectwas found to be significant, suggesting that V.parahaemolyticus increase with increasing salinity upto an optimal level and then decrease with increasing salinity thereafter. There was no significantinteraction between temperature and salinity that was evident based on the data. As a result, themodel used to describe the concentration of V. parahaemolyticus as a function of salinity andtemperature was of this form:

log(Vp / g) = α + β * TEMP + γ1 * SAL + γ2 * SAL2 + ε

where TEMP denotes temperature in °C; SAL denotes salinity in parts per thousand (ppt); ∀, ∃, (1,and (2 are regression parameters and , is a random normal deviate with zero mean and variance φ

2.

The resulting parameter estimates were reported as:

α = 2.6β = 0.12γ1 = 0.18



γ2 = 0.004σ2 = 1.0

Figures 3.2 and 3.3 show the estimated relationships between total V. parahaemolyticus densities inoysters versus water temperature and salinity.

FIGURE 3.2Observed log10 V. parahaemolyticus (Vp) densities in oysters versus water temperature

at different salinities

FIGURE 3.3Observed log10 V. parahaemolyticus (Vp) densities in oysters versus salinity at different

temperatures



The FDA V. parahaemolyticus risk assessment is a good example of the critical analysis of the dataand the use of a logical thought process in order to determine the level of complexity required by themodelling exercise. The FDA risk assessment analysed the data and, together with experience andexpert opinions, found that the extremes of salinity (below 5 ppt) were detrimental to the survival ofV. parahaemolyticus. However, the influence of salinity within a range of moderate environmentalsalinities (i.e. 5–35 ppt) was not as clear. Based on the regression analysis, a quadratic relationshipfor V. parahaemolyticus densities versus salinity within the 5–35 ppt range was found to beconsistent with the data. However, this projected effect of salinity was not as strong as that oftemperature. Within a broad range around the optimal salinity of 22 ppt, the results of the regressionsuggested that the differences in salinity actually encountered in oyster harvesting had relatively littleeffect on the V. parahaemolyticus population.

Two considerations suggested that neglecting the effect of salinity did not adversely affect thepredictive value of a model based on temperature alone. First, as shown in Figure 3.3, predictedmean V. parahaemolyticus densities vary by less than 10 percent from the optimal (maximum)density while salinity varies from 15 to 30 parts per thousand (ppt). Secondly, measurements ofoyster liquor salinity at the retail level, which are strongly correlated with salinity of harvest water,suggested that oysters are harvested from the more saline areas of the estuaries year round. Themean oyster liquor salinity was found to be 24 ppt with a standard deviation of 6.5 ppt based on 249samples. This study was conducted year round with samples obtained from all regions of the UnitedStates. These two considerations suggest that the effect of variation of salinity on predicteddistributions of V. parahaemolyticus densities would be minor, and modelling proceeded withtemperature as the only predictive variable.

The prediction of V. parahaemolyticus densities was based on a regression analysis of the data withwater temperature as the only effect in the model. The resulting regression equation was reportedas:

log(Vp / g) = α + β * TEMP + ε

where TEMP denotes temperature in °C, ∀ and ∃ are regression parameters for temperature effecton mean log10 densities, and , is a random normal deviate with zero mean and variance φ

2.

Parameter estimates obtained for this equation were reported to be:

α = 1.03β = 0.12σ2 = 1.1

The predicted mean log V. parahaemolyticus level versus temperature for the temperature onlyregression is shown in Figure 3.4. Clearly, this relationship is comparable to that which would beobtained by fixing the salinity to a near optimal value (22 ppt) in the prediction equation based onboth water temperature and salinity. The temperature only regression was used to model therelationship between temperature and density of total V. parahaemolyticus at the time of harvest.

FIGURE 3.4Observed log10 V. parahaemolyticus (Vp) densities in oysters versus

water temperature



3.2 HARVEST MODELS

The models that might be employed during harvest are those that describe the effect that specificharvesting practices might have on a hazard or in initiating the hazard. Given the scope of theevents that could take place during harvest, it is often reasonable to collapse the harvest and preharvest models into one overall modelling stage. Clearly, the decision to separate or combinemodelling stages is not a rigid guideline, but rather needs to be left to the discretion of the riskassessor. The risk assessor can combine the steps or separate them, taking into consideration suchthings as data availability and the needs for decision information and for complexity.

3.3 HANDLING AND PROCESSING MODELS

Similar to models developed for the harvest stage, the handling and processing models that aredeveloped will primarily describe activities taking place during this stage that could have an effect oneither the level or frequency of contamination. The level or frequency of contamination could beimpacted by handling and processing steps that either kill the pathogen (either through thermaleffects or some other process step), allow growth (through timetemperature conditions being madeavailable), or allow additional contamination to be introduced (through crosscontamination). Ingeneral, it is difficult to provide specific models for handling and processing issues that are likely tobe extremely varied.

3.4 STORAGE AND DISTRIBUTION MODELS

Similar to other stages, the storage and distribution models are concerned with characterizing theeffect that events occurring during these stages will have on the hazard. During storage anddistribution, depending on the product we are assessing, contamination could occur during thisstage with the hazard being introduced onto the product. This could occur, for instance, if theproduct is stored in an unpackaged state and environmental contamination is then allowed to enterthe storage environment contaminating the product. Although the introduction of contamination ontothe product is a possibility, a primary concern during this stage is the potential for microbial hazardsto increase as a result of growth due to favourable time and temperature conditions, or to die off asa result of unfavourable time and temperature combinations. It is important to recognize that virusesand protozoa are generally unable to multiply in food products, and as a result, modelling growth inthe postharvest stages is usually limited to bacteria.

The modelling of contamination being introduced onto the product needs to be determined on thebasis of the type of data available, or the mechanism by which the hazard is introduced. It is notpossible to prescribe a model to use in order to characterize the introduction of a hazard onto a



stored product. If data is available that allows a purely empirical relationship to be estimatedbetween, for instance, storage conditions and hazard contamination level, then that empiricalapproach could be used. For instance, a linear regression using data on storage condition andhazard level could be used. In the absence of usable data, it may be necessary to generate modelsthat attempt to mechanistically describe how contamination occurs during storage. For instance, amodel that describes the process through which contamination moves from the storage environmentonto the product. Overall, the risk assessor needs to use good modelling practice and judgment aswell as the essential consideration of the need for the step to be modelled based on the importanceof the event, and the decision making information that could be derived from modelling the process.

The modelling of microbial growth and death is a field of study that has undergone extensivedevelopment over the past decade. The field is known as predictive microbiology and usesexperimental data from sources including growth experiments conducted in the lab on broth or realfood substrates, and mathematical equations to describe the behaviour observed in theseexperiments. McMeekin et al., (1993) is a good resource for a comprehensive treatment of the fieldof predictive microbiology. In general, predictive microbiology estimates the growth and death ofmicrobial populations as a function of time, temperature, and other environmental conditions.Typically, temperature has been the primary variable used in these models to dictate the amount ofgrowth that could occur; however, researchers have developed models with numerous factorsincluding pH, salt concentration, lactate and other components. In developing and using models todescribe the growth and death of pathogens, the use of an increasing number of variables to predictthe behaviour of the pathogens needs to be balanced according to the increased accuracy and theadded complexity and data needed to use the model. If the addition of several variables into theequation produces results that are slightly more accurate but require a great deal of new data andinformation that may not be immediately available for the specific product being investigated, thenobviously it would inadvisable to incorporate all these variables in the model.

The field of predictive microbiology, expanded as a result of two large research programmes fundedby the US and the UK. The US programme resulted in the release of a software package that isfreely available called The Pathogen Modelling Program, which includes growth and inactivationmodels for several bacterial species. Food MicroModel is the software package that resulted fromthe UK programme and is a proprietary package for which licences need to be purchased for usage.In addition, there are other published models and data in the international literature that can befound readily using literaturesearching software. A summary of the various packages and theirfeatures is given in Table 3.1.

In order to get a comprehensive treatment of growth modelling, refer to McMeekin et al. (1993). Wepresent a simple hypothetical illustration of growth modelling below.

3.4.1 Example of growth modelling

This example will illustrate an approach to modelling the growth of bacteria with only temperature asa determining factor. The inclusion of additional factors, as stated earlier, can be incorporated intothe growth rate equation if necessary. A commonly used equation to describe the growth rate ofbacteria is the expanded square root model of Ratkowsky et al. (1983). This equation is shownbelow:

where “k” is the growth rate in generations per unit time, T is the temperature, Tmin and Tmax arenotational minimum and maximum temperature for growth respectively, and b and c are regressionparameters.

Figure 3.5 shows the change in the generation rate as a function of temperature. The parameters ofthe square root model would be estimated based on experimental data to which the model is fit. Inthis hypothetical case we are using hypothetical parameters, and would estimate that the optimum



growth temperature for this pathogen would be estimated as approximately 32 °C, at which thegeneration rate is approximately 0.17 generations/minute.

Table 3.1Models available in software packages (adapted from McMeekin et al., 2002 and Ross,McMeekin and Baranyi, 2000)

Food micromodel Type of model Organism Factors modelledgrowth rate, lag time Aeromonas hydrophila Temp, pH, NaCl

Bacillus cereus Temp, pH, NaCl, CO2

Bacillus licheniformis Temp, pH, NaCl Bacillus subtilis Temp, pH, NaCl Bacillus thermosphacta Temp, pH, NaCl Clostridium botulinum Temp, pH, NaCl Clostridium perfringens Temp, pH, NaCl

Escherichia coli Temp, pH, NaCl CO2

Listeria monocytogenes temp, pH, NaCl CO2, nitritelactate,

Staphylococcus aureus Temp, pH, NaCl Salmonellae Temp, pH, NaCl nitrite Yersinia enterocolitica Temp, pH, NaCl Pathogen modellingprogramme

Type of model Organism Factors modeledgrowth rate, lag time, nonthermal death rate Escherichia coli O157:H7 temp, pH, NaCl nitrite, lactate,

anaerobic,

Listeria monocytogenes temp, pH, NaCl nitrite,anaerobic, lactate

Staphylococcus aureus Temp, pH, NaCl, nitrite,lactate

Salmonellae Temp, pH, NaCl, nitrite

growth rate, lag time Aeromonas hydrophila temp, pH, NaCl nitrite,anaerobic

Bacillus cereus temp, pH, NaCl nitrite,anaerobic

Shigella flexneri temp, pH, NaCl nitrite,anaerobic

Yersinia enterocolitica temp, pH, NaCl nitrite,anaerobic

time to toxigenesis Clostridium botulinum temperature, pH, NaCl Food spoilage predictor Type of model Organism Factors modelledgrowth under fluctuating



conditions, remaining shelf life psychrotrophic pseudomonads temperature, water activity

Delphi Type of model Organism Factors modelledgrowth under fluctuatingconditions “generic” Escherichia coli temperature, anaerobic

Seafood spoilage predictor Type of model Organism Factors modelled

Given the estimates for the generations per unit time (k), this can then be translated to the totalnumber of generations formed at a particular temperature if the duration of time that the bacteriaspend at that temperature is known. In this particular case, if we assume that the product containingthe bacteria is stored at 25 °C for one hour, we can estimate the total amount of growth as follows:

Generation rate at 25 °C = 0.09 generations/minuteTime spent at this temperature = 1 hour = 60 minutesGenerations formed = 0.09 gen/min × 60 min = 5.4 generations

Log growth = log (2generations) = 1.6 log

Therefore, in this case we would estimate that if the product was stored for one hour at 25 °C, therecould be approximately 1.6 logs of growth. Hence, if the original contamination level was 1.0 log andthe product was stored for one hour at 25 °C, we would expect the level of contamination on theproduct to approach approximately 2.6 logs after the storage period.

FIGURE 3.5Hypothetical generation rate per minute, using expanded square root

model with b = 0.02, c = 1.0, Tmin = 10, and Tmax = 35.

It is important to recognize that this is a simplification of bacterial growth, and commonly a lag timeoccurs before growth begins. Subsequently, the growth is likely to be less than that estimated heresince a portion of the one hour will be used up resolving the lag phase prior to the growth



commencing. Similar to the models used to describe the generation rate as a function oftemperature or other environmental factors, there are also models available that describe the rate atwhich the lag phase is resolved.

3.5 PREPARATION AND CONSUMPTION

In the preparation and consumption section of a model, the goal is to first account for the effect thatvarious practices during preparation may have on the hazard, and secondly, to determine how muchof the hazard is consumed via the consumption of the food product.

3.5.1 Preparation

The key components that need to be considered during preparation in order to estimate risk fromthe consumption of the product, is the effect that preparation practices, whether at home or at acommercial enterprise, will have on the hazard. Cooking is an important preparation practice thatcan have an impact on the hazard. Similarly, crosscontamination is also an important event that canoccur during preparation that can have an impact on how the consumer is exposed to the hazard.There could be other specific preparationassociated events that would need to be consideredbased upon the hazardproduct combination being studied. Evaluating these events would entailaccounting for the population consuming the product and their preparation preference for theproduct. In Japan, for example, the consumption of raw fish is common practice; however, in manyother countries fish is always cooked in some form. Similarly, the application of certain spices orfermentation practices could have an impact on the hazard and would need to be considered.Overall, the primary concern during preparation is to account for whatever steps are taken in orderto prepare the product for consumption, and the effect that these steps might have on the exposureof the consumer to the hazard. The preparation steps taken could increase, decrease, or have noeffect on the hazard, but the steps need to be considered and then appropriately handled (i.e. modeland estimate effect or ignore if unnecessary).

Crosscontamination is a key exposure pathway that could occur during the preparation of theproduct. Unlike cooking, which is deliberately performed in order to prepare the product forconsumption, crosscontamination is an unintentional result of the preparation process. Crosscontamination could allow the hazard to be transferred from the original product to the consumereither through contamination of other products, hands or any number of combinations. Fullydescribing and modelling all the possible events that could occur as a result of crosscontaminationcan be difficult since there can be many possible pathways. If a complete characterization is notpossible in crosscontamination, one should still estimate the magnitude of the problem. In essence,if crosscontamination is estimated to be the dominant exposure pathway, then the cooking modelmay not need to be modelled in detail. Alternatively, if crosscontamination is estimated to be ofsmall magnitude, then great effort may not need to be expended on fully characterizing or collectingmore data on the crosscontamination issue.

Cooking is one of the most common preparation practices, and an example of how it might bemodelled is illustrated here. Other preparation practices should be handled appropriately.

3.5.1.1 Example preparation model (cooking)

The primary output desired from the preparation module is the concentration of the hazard in thefood product at the point of consumption. If the risk to the consumer is the result of toxins that arealready in the food product prior to cooking, then as long as the toxin is heatstable, the effect ofcooking is likely to be minimal and can probably be ignored. If the toxin is not heat stable and getsdenatured as a result of the cooking process, then the degree of reduction could be estimated. Thisexample illustrates how the reduction in the number of pathogens as a result of cooking might beestimated.

A common approach used to estimate the effects of cooking on bacterial numbers is through the use



of “D” and ‘z” values.

The Dvalue is the time required at a specific temperature to destroy 90 percent (1 log decrease) ofthe population. The zvalue is the temperature increase required to reduce the Dvalue by 90percent, or a factor of 10. As an illustration, a Dvalue of 5min at 55 °C means that in order toreduce the population by 1 log, the population has to be held at 55 °C for a period of 5 minutes. Ifthe zvalue for this population is defined as 8 °C, then if the exposure temperature is raised by 8 °C(55 + 8 = 63 °C), the Dvalue will be reduced by 90 percent, or a factor of 10, so it will require only0.5 minutes at 63 °C to reduce the population by 1 log.

The following illustrates an approach used to estimate the reduction of Campylobacter jejuni duringcooking. Although C. jejuni is not a pathogen commonly associated with seafood, the approachillustrated here can be translated to any pathogen, provided the appropriate data is available.

The log reductions from cooking in this example were modelled based on the effects of temperatureon the organism using experimentally determined D and zvalues. As described, the Dvalue is thetime required at a specific temperature to destroy 90 percent (1 log decrease) of the population,while the zvalue is the temperature increase required to reduce the Dvalue by 90 percent, or afactor of 10.

Blankenship and Craven (1982) studied the thermal sensitivity of C. jejuni in poultry meat. Thethermal death times for a fivestrain composite and strain H840 in autoclaved ground chicken weredetermined (Table 3.2). The zvalues for the fivestrain composite and strain H840 were reportedas 6,35 °C and 5,91 °C, respectively.

Table 3.2Thermal death times for 5strain C. jejuni composite (Blankenship and Craven, 1982)

H840zvalue = 5.91 C

Fivestrain compositezvalue = 6.35 C

Temperature (deg C) Dvalue (min) Dvalue (min)49 20.5 ND51 8.77 9.2753 4.85 4.8955 2.12 2.2557 0.79 0.98

In order to estimate the log reductions at different times and temperatures, a linear regression wasperformed on the data. The regression used the logtransformed Dvalues, using an equation of theform shown in Equation 1:

Log(D) = ( a × Temp) + b Equation 1

“a” and “b” are constants estimated through the regression procedure. However, within this equationthe term “a” is equivalent to the inverse of the zvalue. Therefore, the published zvalue for the studywas used and fixed while adjusting the “b” coefficient in order to provide a “least squares fit” to thedata. In the current analysis only, the data for the fivestrain composite in chicken meat was used inthe linear regression; however, the data could be pooled and a linear regression performed on thisdata set as well. The results of both analyses (composite and pooled) are shown in Figure 3.6 andFigure 3.7.

FIGURE 3.6Linear regression using given Zvalue and composite sample in chicken.

(Data from Blankenship and Craven, 1982)



FIGURE 3.7Linear regression using composite and H840 in chicken meat

(Data from Blankenship and Craven, 1982)

The next step is to develop an estimate of the temperature of the product during cooking using thebest available technique. The cooking temperature can be estimated based on experimental studiesmeasuring the temperature in the product during cooking, or using thermodynamic equations thatestimate the temperature reached in the product based on its material properties.

Once the temperature has been determined, Equation 2 and Equation 3 with parameters based onC. jejuni data can be used to estimate the Dvalue. Equation 2 is estimated as a result of the linearregression performed on the experimental data (see Figure 3–6).



Log(D) = ( 0.1575 × Temp) + 9.004 Equation 2

The Dvalue at the temperature is, then, simply the log transform of the value (Equation 3).

D = 10(0.1575 ×Temp)+9.004 Equation 3

Finally, given the Dvalue, and recalling the definition of the Dvalue given earlier, the log reductionthat would occur at that temperature for a given period of time (t) could be estimated using Equation4.

Equation 4

3.5.2 Consumption

The final step needed to estimate the amount of the hazard to which the consumer is exposed, is toobtain information on the consumption of the food product under consideration. This data includesserving sizes, amount consumed on a daily or annual basis, and the frequency that the product isconsumed. The specific type of information will depend on the question addressed by theassessment and the type of data actually available.

If we assume that a certain fish species has a toxin concentration of 5 mg/100 g, and an estimate ofthe amount of toxin consumed by a population per year is required, then the need for theconsumption information can be seen from this simple illustration.”

Concentration = 5 mg/100 grams, individual serving size = 150 grams, number of times consumerseat product annually = 24. From this information, the dose to which the consumer is exposed a perserving basis can be estimated as:

Per serving dose = (5 mg/100 grams) × (150 grams) = 7,5 mg/serving.

The annual exposure can then be estimated by multiplying the per serving exposure by the numberof servings consumed annually:

Annual dose = (7,5 mg/serving) × (24 servings) = 180 mg

Obviously, the form of the data could be different than that shown above, for instance, rather than aper serving consumption size, the data may be reported on an annual consumption amount basis. Itis often the case that data is collected for purposes other than risk estimation; as a result, the datahas to be modified appropriately to extract the necessary information.

There are various sources of food consumption data that differ in terms of how the information iscollected, reported and the stage along the production chain for which data is reported (rawproducts, retail products, consumed product). Generally, there are two types of food consumptiondata available and frequently used for characterizing food consumption patterns for microbiologicalrisk assessment: food production statistics and food consumption surveys.

The most commonly available data type is food production statistics, which provide an estimate ofthe amount of food available to the total population. These reports are usually produced for raw orsemiprocessed agricultural commodities and represent the total annual amount of a commodityavailable for domestic consumption. The total amount available for consumption is divided by thetotal population in the country, which estimates the total annual quantity of food available for eachperson in the total population (per capita amount). The daily per capita amount can then crudely beestimated by dividing the annual amount by 365. Examples of this type of data include the FAOFood Balance Sheets and other national statistics on food production, disappearance or utilization.Because these data are available for most countries and are compiled and reported fairly



consistently across countries, they can be useful in conducting exposure assessments at theinternational level. However, percapita consumption statistics actually represent the food that isavailable for consumption and not actual quantities consumed. The losses in stores, households,private institutions or restaurants are not accounted for. Per capita consumption statistics areindirect measures of actual consumption and may overstate what is actually eaten.

The ideal data for consumptionrelated information comes from food intake survey studies such asthose reported in the United States Department of Agriculture (USDA) Continuing Survey of FoodIntakes by Individuals. This data tends to capture the amount of a specific type of food consumed onan eating occasion, and even separates the data into consumption statistics according to sex andage, which can be important depending on the hazard. These surveys usually include arepresentative sample of individuals from which consumption for the total population or specificpopulation subgroups may be extrapolated. Typically, the surveys are short in duration (one toseveral days for each survey participant), but they provide much more detailed and specificinformation about the types of food consumed. Unfortunately, the surveys tend to be expensiveendeavours, and as a result, food consumption surveys are conducted by a limited number ofcountries.

In addition to these two types of data sets, another source of data comes from retail food purchasereports. This data provides detailed information about specific food products that is often lackingfrom food consumption surveys and can complement the other data types.



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