It is Quality and Quantity That Matter

Quantitative and Qualitative Methods in Microbiology

Qualitative Method Insights

“It is quality rather than quantity that matters” is an adage to which many credit the ancient Roman stoic Seneca the Younger. Contrary to Seneca’s assertion, for microbiology at least, both quantity and quality matter, and microbiological methods are classified as being either quantitative or qualitative. Which category they fall into is based on what type of data is actually collected and reported.

In the vernacular of many scientific disciplines the word “qualitative” refers to descriptions or distinction based on some quality or characteristic in contrast to quantity which is a measured amount. In other words the types of data collected and reported by qualitative methods are descriptions, qualities, or characteristics. In (food or clinical diagnostic) microbiology the specific characteristic being assessed is the absence or presence of a particular organism (typically a pathogen) in a given sample. Qualitative tests are usually very sensitive, so they can detect the targeted organism(s) even at very low population levels (as low as 1 cell per sample being tested).

Examples of qualitative tests important in food microbiology include analyses for human pathogens such as Listeria monocytogenes, Salmonella, and E. coli O157:H7/STEC. The results of qualitative tests can be reported in several different ways, including “Negative or “Positive,” “Detected or Not Detected,” or “Absent or Present” per the tested weight or volume analyzed – for example, Negative/25g or Positive/375g.

Quantitative Method Insights

The word “quantitative” refers to a type of information based on quantities or otherwise quantifiable data (objective properties). In other words the type of data collected is a measured, quantifiable (typically numerical) amount. Quantitative testing aims to determine the population size (total numbers) of microorganisms present in a sample. Some quantitative tests are used to count populations of indicator organisms (aerobic plate count, most probable number (MPN), coliform count, total Enterobacteriaceae counts, etc.) while others count populations of specific target organisms (Staphylococcus, Yeast, Mold, etc.).

Interestingly and sometimes confusingly, a qualitative result is inherent in a quantitative method – after all, you can’t count something that isn’t there. Therefore a quantitative value indicates presence of the target organism. These are reported in the form of a number of organisms per unit. To make the matter even more confusing, some methods (i.e. real time PCR/qPCR) have aspects of both quantitative and qualitative methods, and which category they fall into comes down to how they are used and how the data is reported.

As an example, in most qPCR food pathogen tests the qualitative result call (presumptive positive or negative) is based on what is traditionally considered a quantitative output (Ct or Cq value). Moreover, qPCR methods have an LOD which, as you will read next is typically an indicator of quantitative methods. In case you were wondering, in food pathogen diagnostics, the designation “presumptive” positive is unique to qPCR methods, and is a topic for another article.

Interpreting Results for Quantitative Microbiology Methods

Built into all quantitative methods is a “limit of detection (LOD) or “detection limit” — meaning that the method is capable of detecting the target organism(s) at or above that limit. This is sometimes also referred to as the lower limit of detection (LLOD) of the method. Because the limit of detection of any given quantitative method is always greater than zero, results of such tests cannot be reported as negative or absent. Most standard plate count methods have a detection limit of 10 CFU/g and MPN methods have a detection limit of 3 MPN/g (assuming standard ten-fold dilutions).

If no growth of the target organism(s) is detected, the result will be reported as “less than” the limit of detection (i.e. <10 CFU/g or < 3 MPN/g). Often people ask “Is <10 CFU/g the same as a negative result?” The short answer is no, they are not the same. The longer answer is that while it’s not possible to answer that question in the affirmative, we can say that a “less than” result means that the target organism was not detected in the sample, although we cannot say it is absent from the sample.

Regulatory bodies, validation agencies, and others in industry recognize this limitation of quantitative methods and typically treat results that are reported as <LOD as negative for all practical purposes. The concept of LOD is critical for anyone requesting microbiological testing to understand. If the presence of 1 bacterium in a product or process is cause for alarm, then selecting a test method with an LOD of 10 could have disastrous consequences.

Compared to qualitative methods, it can be difficult to wrap your head around the concept of quantitative testing methods and their results. To help illustrate the concept of quantitative methods let’s consider an example problem. A sample is processed by diluting it tenfold with a sterile buffer. The diluent-sample mixture is plated (most typically 100ul or 1ml) on the appropriate growth medium and incubated. The plates are then counted. The result is calculated by multiplying the number of colonies on a plate by the dilution factor divided by the volume plated, and by convention is always expressed as CFU/ml. If there is one colony on the plate and 1ml was plated, the result is 10 CFU/ml. If 100ul were plated the result would be 100 CFU/ml. If there are five colonies on the plate, the count is 50 CFU (or 50 CFU/ml if 1ml is plated). Because there can’t be between 0 and 1 colony, the minimum countable result is 10 cfu.

For samples with microbial population sizes below 10 CFU it is not mathematically possible to determine an accurate count unless one plates out 10 plates of the 1 to 10 dilution, which would increase the cost of doing a plate count by a factor of 10. If you follow the math and logic of the above you will realize that plating onto ten plates is the same as plating 10ml onto one plate (if each plate is 1ml). For practical reasons this is almost never done. In any case, people paying for testing would object to such a costly test, and they can typically understand the theoretical limitations of the methods when counts are very low.

Let’s take another example, and assume there are 5 CFU/g of bacteria present in a given food sample. This is a very low level and it is expressed as per gram because the sample being analyzed is a solid in this case. For purposes of this example it is the same thing as 5 CFU/ml. After the initial tenfold dilution, the concentration will be reduced to 0.5 CFU/g of bacteria. Because you can’t count half a bacterium you stand a 50:50 chance of having no growth on the plate. To go even lower, assume the count were 1 CFU/g in the sample. In this case the odds of finding a colony on a plate would be 1 in 9, hence the majority of times you run the assay you would not find countable plates even though there is bacteria present in the sample. For these reasons the method specifies that a plate with no colonies at the lowest dilution shall be reported as <10 CFU.

Another way to think about this is to imagine a liquid sample that is 10ml and contains just a single bacterium. If you pull and plate 1ml ten times you only have a 1 in 9 chance of pulling the 1ml that happens to have the bacterium in it with the first sampling. Assuming you don’t get it the first time, with each subsequent sampling and plating event your odds get better. If you want to guarantee detection of the bacterium every time (i.e. you want to increase the sensitivity of the test) one way is to sample and plate the entire volume.

There are other ways to do this and you may be able to imagine some on your own. If you happen to come up with any great ideas that have never been thought of or tried before, you stand a good chance of becoming a very wealthy and maybe even famous individual. Probably not as famous as Seneca, but one never knows…

The Author:

Dan DeMarco

Research scientist (Ph.D micro/mol biology), thought middle manager, boulderer, cat lover, fish hater