Uses and Limits of Microbial Testing - ACS Publications - American

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Chapter 13

Uses and Limits of Microbial Testing

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Robert L. Buchanan Center for Food Safety and Applied Nutrition, Food and Drug Administration, U.S. Department of Health and Human Services, 5100 Paint Branch Parkway, College Park, MD 20740

Microbiological testing is an integral part of most microbiological food safety problems, making up a significant amount of the effort of many food microbiologists. Effective use of these analytical tools requires both a thorough understanding of the technical details of methods being employed and how the performance of the methods is influenced by sampling limitations. However, too often the latter is incompletely evaluated or understood. In particular, improvements in the sensitivity and specificity of new methods can be effectively lost i f it is accompanied by a decrease in sample size. The basis for sampling requirements, particularly when a microorganism of concern is present at low levels, is based on the probability of detecting the specific portion of food being tested from a larger population of food portions. Two general approaches based on statistical requirements are most often employed in the testing of food samples, "within­

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-batch testing" and "between-batch testing." The purpose of with-in batch testing is to establish that a specific lot of food is "safe" in regard to a specific hazard at a specified level of confidence. This approach assumes no prior knowledge of the history of the food. Practical sampling considerations generally limits its effectiveness to batches where more than 1% of the samples are contaminated. Often referred to as process control testing, the purpose of between-batch testing is © 2006 American Chemical Society

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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verification that a process is operating as intended. This type of testing assumes that there is extensive knowledge of the system being evaluated. This approach can be very sensitive to changes above an established baseline, however, that sensitivity is again dependent on the number of samples taken and the inherent number of contaminated portions produced by the system. A n understanding of which tool to employ and the limitations of those tools are critical to the effective use of microbiological testing.

A n essential component of virtually every microbial food safety program worldwide is the periodic testing of foods to assess microbiological quality and/or safety. These programs are initiated, in part, based on the intuitive feeling of consumers, industry and food control agencies alike that microbiological testing provides data critical to determining i f the food supply is safe. In fact, microbial testing can be an extremely useful and powerful tool when used effectively and appropriately (7,2). However, the effective use and interpretation of microbial testing schemes is dependent on both the providers and the users of the results having a clear understanding of the scientific and statistical principles underlying such testing, including the basic assumptions that are inherent every time a food sample is analyzed. Regretfully, the basic training for individuals that conduct or use the results of microbial food testing too often does not provide the type of indepth consideration of the characteristics of microbial testing schemes that is needed to design testing schemes that provide the required data in a cost effective manner. The purpose of the current manuscript is to provide a brief overview of key attributes, principles, underlying assumptions and decisions that need to be considered in establishing microbial testing programs. O f necessity, this review will focus on introducing key concepts and parameters, and will not get into the details of the various decision tools that are available. These are available through a variety of references. In particular, the International Commission on Microbiological Specifications of Foods (ICMSF) has been instrumental in articulating the principles for microbiological testing (3). The critical phase in any microbial food safety testing program is the initial design phase. It is during that phase that decisions must be reached and documented in relation to the testing program's goal(s), underlying basic assumptions, required stringency, criteria for interpreting results in relation to

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

186 subsequent actions that should be taken, and unique characteristics of the food and microorganisms that will affect interpretation of the results. Some of the key questions that need to be asked during the design phase include: • •

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• • •

• • • • •

What are the microorganisms of concern that affect the quality or safety of the food? What are the sources of contamination, conditions, or activities that lead to the microorganism(s) of concern being present at unacceptable levels or frequencies? What conditions (both uses and abuses) are the foods likely to experience once they have left the manufacturer's control? What is the purpose of each proposed assay? What are the methods that will be employed and what are their performance characteristics (e.g., lower limit of detection, repeatability, ruggedness, variance)? What information is provided by each proposed assay? What are the acceptance/rejection criteria for each proposed assay and what are the actions that will be taken as a result of these results? What are the consequences of mistakenly releasing foods that should have been rejected? What are the consequences of mistakenly rejecting foods that were actually within required specifications? What are the basis and limits associated with any proposed use of a surrogate microorganism in relation to the pathogen and/or condition that it is being used to assess?

Articulation and documentation of these and related questions are an important tool in developing a microbiological testing program. It helps ensure that the testing program meets the needs, provides a blueprint to those implementing the program, and serves as a historical record for future evaluation of program effectiveness. These questions help ensure that the critical thinking and decision-making needed for an effective testing program is achieved. The current chapter will focus on key decisions related to the design of testing programs and sampling plans. A detailed consideration of specific methods applications (e.g., P C R , ELISA, culture techniques) will not be covered, but standard references are available for the interested reader.

Why Test? As stated above, one of the key questions in designing a microbiological testing program is the reason for performing the microbiological assays. Too

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

187 often the reason for testing is not clearly defined, which can lead to the incorrect design of testing programs or the incorrect interpretation of results obtained from them. The reasons for most microbiological testing fall into one of five areas: •

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• • • •

Characterization of a food and/or a food control system to establish a "microbial history," Determination of the microbiological safety or quality of a specific lot/batch, Verification that a food control system is operating as intended, Environmental testing, and Investigational testing.

Each of these testing types has a different purpose and different assumptions and statistical basis. For example, lot testing is based on the assumption that the tester has no advanced knowledge of the history of the lot being examined, and also on the assumption that the presence of microorganisms in the product may or may not have specified distribution (e.g. log normal). Conversely, process control testing to verify that a food control system is operating as intended presumes that the tester has extensive knowledge of how the product was produced, and is largely determining i f there has been an alteration in one of the basic parameters or assumptions. Briefly, the purposes of the types of testing programs can be summarized as follows: •



Characterization of a food control system. These are testing programs undertaken to establish the performance characteristics of a food product and the manufacturing system used for its production. This type of testing is typically done prior to the initiation of a new product or method of manufacture and establishes the baseline data what can be expected when the production system is "under control." The characterization process may require the collection of data over an extended period, particularly i f there are seasonal differences associated raw ingredients or product manufacture, distribution, marketing, or use. Testing of lots/batches for safety. This is the traditional testing of single lots of food against a specified criterion, and is often the basis for decisions related to whether a consignment of food is or is not safe. As noted above, this assumes that the tester has no knowledge about the history of the product. The effectiveness of this approach is largely limited by the large number of samples required to provide the high degree of assurance typically required when the level of "contaminated units" within a lot is small.

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

188 •

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Verification that a food control system is operating as intended. Often referred to as "process control testing," this approach is based on "betweenlot" testing to evaluate whether a system is operating as originally designed. The focus of this type of testing is on the process and not on verifying that any single lot of product has achieved a microbial safety or quality. This approach typically requires extensive knowledge of the microbiological history of the production system. Environmental testing. This type of testing is employed most often to determine i f a food production facility is continuing to follow good hygienic practices. It is most effective when used in conjunction with a well established baseline microbiological history against which individual results can be compared. When used in relation to food safety decisions, there is an underlying assumption that there is a relationship between the incidence of pathogens (or a surrogate microorganism) in the environment and in the final product. Investigational testing. This is a broad category of microbiological testing approaches that may be employed when a problem (i.e., loss of control) occurs. The purpose of such testing is to identify the source and cause of the problem so that it can be corrected. Investigational testing can range from the simple (e.g., swaps in processing area) to the highly sophisticated (e.g., molecular subtyping of the isolates from environmental swabs). Investigational sampling is also an integral part of epidemiological investigations when there has been an adverse event involving foods. Typically, there are fewer statistical guidelines with investigational testing.

Since one o f the primary goals of the current chapter is to provide an overview of the issues that have to be addressed during the design and implementation of testing programs and sampling plans, only within-lot testing and process control (between-lot) testing will be discussed further. More information on the other types of microbiological testing programs can be obtained from standard references (J). In considering the two types of testing that will be discussed further, a term that will be used is "lot." There appears to be no formal definition of lot, nor are there specific guidelines for the establishing the size of lots within production runs. ICMSF (5) recommended that "Ideally, a lot is a quantity of food or food units produced and handled under uniform conditions." Often lots are established in terms of production runs on specific lines during a limited or specified period of time. Regulatory agencies often define lots in relation to potential recalls as the period of time between complete cleanups of a manufacturing facility. Increasingly, food manufacturers are being required to more rigorously define and maintain lot identity in order to facilitate potential recalls.

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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Where to Test? Traditionally, the manufacture of a food product is envisioned as a series of sequential steps that begins with raw ingredients and ends with a finished product that is ready to be introduced into the marketplace. In recent years, our improved understanding of the epidemiology of foodborne disease, the globalization of the food industry and changes in the marketing and consumption of foods have increasingly required that the food chain be extended conceptually and practically to encompass all of the steps from the production of agricultural commodities on the farm to the consumption of the final product in the home. The ICMSF (3) conceptually viewed the performance of an entire food safety control system as being described by the equation: H - E R + II3.0 would be considered unacceptable based either on binned quantitative or qualitative data where the method had a lower limit of detection of1000 CFU/g. (Adapted from ICMSF, 2002)

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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195 The discriminatory power of a within-lot microbiological testing program is dependent on a number of factors related to analytical methodologies, particularly the methods lower limits of sensitivity. However, in most instances where standard methods are employed and the level of contamination is reasonably low, the primary factor affecting detection is the design sampling plan. The three parameters that determine the ability of a sampling plan to detect a contaminant is the number of sample units examined («), the number of defective samples permitted among the sample units tested (c), and the size of the analytical unit being examined. Examples of how η affects the likelihood of detecting unacceptable lots and their ability to correctly distinguish between acceptable and unacceptable lots are depicted in Figure 4. Particularly in the case of presence/absence tests, the size of the analytical unit directly affects the lower limit of sensitivity of a method and thus the m value for the sampling plan. Thus, i f the mean concentration of a pathogen in a food is 1 per 10 g, the likelihood of detecting it with a presence/absence assay in a single sample i f the analytical unit is 1 g is approximately 10%. However, i f the sample size was increased to 25 g, the probability of detecting the pathogen would approach 100%. In this instance, manipulating the size of the analytical unit effectively changes the lower limit of detection for the analysis and thus the m value for the sampling plan. As introduced above, the percent of defective samples is an important determinant of the discriminatory power of a sampling plan. The percent of defective samples also posed significant practical limits for microbiological testing schemes. The relationship between η and percent defective on the discriminatory power of a sampling plan to correctly identify lots containing unacceptable portions is depicted in Table I. It is apparent that a practical limit for microbiological testing is faced when applied to lots with defective rates of < 2%. Microbiological testing in those instances is generally impractical due to the large number of samples required to achieve a reasonable level of confidence. In some instances it may be possible to pool large numbers of sample units such as is done for the testing o f eggs for Salmonella Enteritidis where the percent o f defective eggs is in the range 1 per 10,000, however, even there the level of confidence is limited. On the other end of the scale, presence/absence testing of lots where the normal percent of defectives is high (e.g., > 30%) is also of limited discriminatory value. It would be impractical to produce foods where even with a single sample, more than a third of the lots would be expected to be rejected. In those instance the manufacturer would need to consider alternative means for producing the product, or in those instances where the level of a hazard is important, switching to a quantitative sampling scheme and establishing decision criteria based on binned data (e.g., acceptable: < 100 CFU/g).

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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Figure 4. Examples of the effect of number of sample units (n) examined in a 2class sampling attribute sampling plan on the probability of accepting a lot as a function of the percent defective units within the lot. (Adaptedfrom ICMSF, 2002)

While presence/absence data are qualitative in nature, they can be used to estimate the levels of a microorganism within a lot i f the distribution of the microorganism can be deduced. The distribution of microorganisms within a lot of food has generally been found to be log normally distributed, i.e., the level of a microorganism expressed as a log number is normally distributed (3). When this distribution can be assumed and there is historical data on the standard deviation of the distribution, the % defective rate can be used to estimate the mean log concentration within a lot. Conceptually, this is similar to the approach used to assign most likely concentration values when performing a Most Probable Number analysis except that one is performing it with a single dilution and a larger number of "tubes." This concept is useful since it allows confidence

In Advances in Microbial Food Safety; Juneja, V., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

197 intervals for different sampling plans to be calculated based on the mean log concentration of the microbiological agent within the lot and provides a relative measure for evaluating the relative effectiveness of different sampling plans. For example, selected "ICMSF cases" (3) for a 2-class attribute sampling plan as a function of mean log concentration are depicted in Table II.

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Table I. Probability of Accepting a Lot as a Function of the Percentage of Defective Sample Units and the Number of Sample Units Examined. (Adapted from ICMSF (2002)). % Defective 2 5 10 20 30 40 50 60 70 80 90

3 0.94 0.86 0.73 0.51 0.34 0.22 0.13 0.06 0.03 0.01