Which commercial interest would you prefer?

The inspection cartel wants you to obsess about uncertainty of measurement, or measurement error, as some prefer to call it. Sometimes it’s small, sometimes it’s big. But it is real, it’s often important, but in many situations it’s not much help. It’s universal value is to give inspectors material to inspect and nitpick. As if it’s making an important difference to customers. If it were making a difference this is probably not how you would discover its effects. The ISO imposes standardised methods that are usually important in precision engineering or chemistry to disciplines where they are of limited help, such as quantitative microbiology.

The author of the article below has a different vested interest – an operationally simple, rapid microbiology test in which calculation of uncertainty is of little relevance.

The rapid method’s correlation with plate counts isn’t bad, but where in the range of plate counts?

Which commercial interest would you prefer? European or American? A parasitic cartel that makes you pay them to do work for them, or a company that innovates with simpler, cheaper, faster methods for you and your customers?

What Do Microbiology Test Results Really Mean?

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To quote one of the founding fathers, “In this world nothing can be said to be certain, except death and taxes.” Though not a microbiologist by trade, Benjamin Franklin’s wise words resonate all the way to the interpretation of your microbiology test results. It is academically and universally recognized that no microbiological measurement is perfect due to statistical and practical uncertainty. In fact, acknowledging the uncertainty of a measurement is as important as the measurement itself.

Uncertainty is even more complex in food microbiology due to the particulate nature of bacteria and their ability to reproduce by binary fission. This results in localized pockets of higher concentrations of bacteria where each individual represents a unique variable entity. Consequently, there is an uneven distribution of microbes even in well-mixed samples that create problems not only for test methods but sampling in order to get a meaningful result for the batch. The working group of the International Laboratory Accreditation Cooperation states “it is virtually impossible to know the exact microbial concentration in any sample, natural or artificial.”

The vagaries of microbial measurement are often conveniently forgotten, resulting in unreasonable expectations of both laboratories and the methods deployed. So what do microbiological test results actually mean? What can be expected and do expectations apply equally to both product and environmental samples?

Food products are generally well controlled and manufactured to a consistency where microbial specifications are established. Conversely, there are no agreed standards for microbes for environmental surface samples that are less controlled and more variable. Each facility is expected to do “the best it can” for monitoring cleaning processes due the uniqueness of each manufacturing facility. Thus food manufacturers strive for high hygienic standards to protect their products, brands, and ultimately consumers.

Sources of Variation and Considerations

The unit of measurement for the enumeration of microbes is a colony forming unit (CFU) derived from plate count methods. This technique has remained largely unchanged since the pioneering days of Pasteur and Koch in the 19th century. It is defined as “a rough estimate of the number of viable bacteria or fungal cells in a sample” because it relies on the false assumption that each colony is derived from a single bacterium. Microbes exist as clumps or chains and are often difficult to separate into single cells. Hence, there is a large natural variation in CFU results from plate counts particularly if single replicate samples are used and single tests are conducted.

There are several steps in the plate count method where additional variation can be introduced. To obtain the optimum number of colonies for counting (30 to 300 CFU), dilutions of the sample have to be prepared. Since the distribution of microbes in the sample is not uniform, each series may produce different numbers of CFUs. More variation occurs if there are fewer than 30 colonies per plate. The normal expected variation from plate counts is typically 0.2 to 0.5 Log units, so for a target 1,000 CFU (Log 3.0), an actual result can be anywhere between 300 to 3,000 CFU and still be considered microbiologically equivalent.

Such variation is well known and regularly examined among accredited testing laboratories. Under the proficiency testing scheme, laboratories using standard methods are provided with several replicates of stable, homogenous samples. Results are expected to show a 10 fold (1 Log) variation between laboratories. Sometimes this variation is exceeded by >2 Logs for plate counts such as coliforms or Enterobacteriaceae.

Mathematical models can be applied to statistically assess confidence of results. Measurement Uncertainty is used to calculate the dispersion of the values attributed to a measured quantity. The uncertainty reflects the doubt in the result of the measurement. In the case of a standard method for total bacterial count in milk, this has been calculated as 39.6 percent, i.e. the “true value” of the obtained result (within 95 percent confidence limits) can be expected in a range ±39.6 percent of the result. This means the actual value is not known for certain, and for a sample expected to contain 10,000 CFU, the true value lies somewhere within the range 6,000 to 14,000 CFU on 95 percent of occasions but can also be outside this range 5 percent of the time.

Microbial stress and survival are other factors that can affect test results. In dry, nutrient-poor environments microbial viability declines rapidly in a matter of hours such that there is a large variation in observed contamination levels. Literature shows examples of total counts <2 to 5.0 x 105 CFU/centimeter (cm)2 with E. coli detected on 15.8 percent of the samples with a range of 0.2 to 12 CFU/cm2.

The vagaries of microbial measurement are often conveniently forgotten, resulting in unreasonable expectations of both laboratories and the methods deployed.

Inoculating surfaces is known to result in large losses of viability with hugely variable residual contamination levels. Inoculating surfaces with a suspension containing 1 million bacteria can give a final residual contamination of 10 to 100 bacteria with 100 to 500 fold variation between five replicates from the same inoculum. Re-suspending and recovering contaminants from the surface swab into a diluent prior to testing also introduces another source of variation. Therefore, great care needs to be exercised when assessing the results of environmental tests and also when comparing methods for the assessment of environmental contamination. Accordingly, the enumeration of microbes in environmental samples yields little meaningful information. A qualitative (presence/absence) approach is more appropriate. General guidelines have been suggested by some authors and auditors, e.g. acceptable ≤80 CFU/cm2 and unacceptable ≥1,000 CFU/cm2. Trend analysis is more suitable and gives better management information about risks and emerging problems. The benefits of regular testing, preferably with a simple method giving rapid results for prompt corrective actions, are well established.

Alternative Rapid Method

An example of a new test method that uses traditional microbiology with an end detection system is Hygiena’s MicroSnap. This two-step test procedure has a total time to result of 7 to 8 hours. The sample can be a surface swab, a 1 milliliter (ml) liquid sample, or a food suspension. Samples are mixed with a proprietary enrichment broth in an all-in-one device, and then incubated for 7 to 8 hours. After incubation, a 0.1 ml aliquot is transferred (using the device itself) to a specific end-detection device and measured with the EnSURE luminometer, which also measures adenosine triphosphate sanitation monitoring tests, among other food quality indicators. MicroSnap is formulated for a variety of bacteria and is currently available for Total Counts, Enterobacteriaceae, coliform, and E. coli.

The output of MicroSnap is directly related to inoculum size, i.e. the greater the number of bacteria the shorter the time to detection. Typical results for Total Counts and Enterobacteriaceae have been known to show excellent agreement and a high coefficient of correlation (>0.90) when compared with traditional plate counts. The dynamic range of the single test device is 10 to 10,000 bacteria per ml (or swab), thus negating the need for serial dilutions. A shorter detection time can be set according to the desired specification. For example, 100 Enterobacteriaceae can be detected in 5 hours.

All viable bacteria collected on the swab are cultured and detected within the system. This permits maximum recovery and minimal losses. A study of 300 surface samples showed an 89 percent agreement with the traditional plate count methods for both Total Counts and Enterobacteriaceae and the limit of detection was calculated as 50 to 100 CFU per swab, or ~1 CFU/cm2. In a small proportion of cases (7 percent), samples were positive when tested with MicroSnap and negative when tested with traditional methods.

In summary, the results of microbiological methods are naturally very variable and must be interpreted with care and recognition of limitations. Pragmatism and practical solutions are required to establish “reasonable expectations” for the results from microbiological methods. Results from environmental samples are subject to even greater variation. Therefore, qualitative measurements and trend analysis can provide the most meaningful information.

Dr. Easter is general manager of Hygiena International and has over 30 years of experience in food safety and rapid testing solutions. Reach him at martin.easter@hygiena.net.

(Emphasis added.)

This entry was posted in Cartel, Laboratory medicine, Practical problems, Science and tagged , , . Bookmark the permalink.

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