Review: Examining the reliability of manual plate colony counts in environmental monitoring
15 November 2018
Plate counts of colony forming units (CFUs) are the gold standard of microbial enumeration and therefore essential to environmental monitoring in the pharmaceutical industry. There are strict regulations regarding bioburden at each stage of pharma production, from raw materials to finished products1,2, but we often fail to question whether current methods are rigorous enough to fulfil these requirements3.
Nearly all the methods used for monitoring clean room environments (Fig. 1) depend on accurate plate counts to quantify the microbial bioburden, determined from the number of colony forming units (CFUs) recorded on a plate4. Usually, this relies on a human operator’s interpretation of the number of colonies present, and this is when the repeatability and reproducibility of the plate count method are most at risk:
Reproducibility is the variability between operators, also known as “many appraisers, one instrument” error5, and this variation can often be substantial3,6.
Repeatability is the innate random variability of measurements taken by the same operator, also known as “within operator” variation or “one appraiser, one instrument” error5. Some of this variation can be attributed to changes in an individual’s emotional or physical state, which can change from one moment to the next7.
Examples of factors that may affect a test’s reproducibility and repeatability are considered in Box 1. A gage repeatability and reproducibility study (Gage R and R) can be used to estimate the measurement uncertainty associated with a given methodology. Arienzo et al. used Gage R and R to compare methods for the microbiological analysis of food and found that plate counts gave the most variable results6.
Examples of questions that must be considered when investigating within- and between- operator variability in plate colony counts.
Boredom & Automatic Behaviour
If the frequency of contaminated plates is low, will operators stay alert throughout the process?
Stress & Time Pressures
Will all operators spend a consistent amount of time examining each plate?
If detecting contamination leads to extra work for the operator, and potentially shuts down pharmaceutical production, could unconscious bias lead to lower colony counts?
Experience & Technique
How well can operators distinguish between individual colonies? Will they quantitatively interpret unusual-shaped/oriented colonies the same way?
Could lighting and/or temperature be distracting the operator? Optimal conditions for one operator may be considered too hot/cold or too dim/bright by another.
Poor repeatability and high levels of uncertainty in plate counts is identified in a number of studies that compare methods for microbial enumeration8,9,10. However, apart from training operators and monitoring their performance, there is little consideration given as to how plate counting can be improved. One way to increase reliability of counts would be to perform multiple repeats, with multiple operators, for each plate and average the results for a ‘best guess’ CFU value3. However, this introduces additional costs and handling time that slow manufacture and delay product release.
The narrow range of countable colonies (approximately 25-250 CFU per standard plate) is a substantial limitation to the plate-count method. The accuracy of CFU estimates for low colony counts (<25) is extremely poor, and the error introduced here can be significant3. There is also a practical limit to the number of colonies that may be counted on a plate. This upper limit of plate counts, referred to as TNTC (too numerous to count) is reported in a variety of ways: reporting CFU as “>upper limit”, plating out multiple dilutions, or counting a fraction of the plate and inferring the total from this sample. Sutton outlined the drawbacks of each of these strategies, noting that: “It is not clear to the author how either of these methods is greatly superior to guessing”3.
Automation and plate counting
Whilst automated counting methods cannot influence the statistical limitations of the plate counting, they can provide a consistent, fast and repeatable way of determining CFU values through colony counts. A variety of automatic colony counters are commercially available, although they vary greatly in the performance of the equipment used to image the culture plates, and the software or program that recognises and quantitates the colonies.
A colony-counting assembly developed by Brugger et al.11 demonstrated a far superior performance to manual counting when presented with single-species bacterial colony plates, although it could not process plates with multiple bacterial species or identify colonies with non-circular morphology.
Despite their potential as high-throughput, consistent alternatives to manual colony counting, automated systems are difficult to validate and struggle to: discriminate between clustered colonies, detect colonies at edge of culture plates, or process colonies of varied size and morphology4,11,12.
Beyond human error
Although improving repeatability and reliability of plate counting can increase the accuracy of colony counts, it is important to consider the innate ambiguity of CFU values as a measure of bioburden. CFU counts only capture a subpopulation of the microorganisms present in a sample; only those that grow and proliferate on the culture media are recorded3,13. There is no single culture medium, incubation temperature or incubation time that permits growth of all potential contaminants in a sample13, therefore any variation in culture protocol may significantly affect CFU counts14. Furthermore, a single countable colony does not indicate a single starting cell; each colony may arise from several thousand contaminant cells and it is only when cells are sufficiently separated that we can discriminate between their resulting colonies3,4.
When you factor in the resources, incubation time, handling, and storage space required for the plate count method, it is surprising that this remains the gold standard for detecting and quantifying bioburden across multiple industries including pharmaceutical manufacturing, food and water processing, and environmental monitoring15,16,17. Further consideration should be given to alternative methods, such as PCR- and qPCR-based detection, which present a highly sensitive, rapid, reliable alternative to traditional culture techniques for microbial detection8.
RH is undertaking a PIPs internship at Microgenetics Ltd. supported by a SWBio DTP PhD studentship in the School of Life Sciences at the University of Bristol funded by the BBSRC.
- ISO: International Organisation for Standardization (2015) ISO 9001:2015 Quality management systems – Requirements. ISO/TC 176/SC 2 Quality systems. 5th ed. Geneva: ISO/IEC.
- MHRA: Medicines & Healthcare products Regulatory Agency (2017) “The Orange Guide” rules and guidance for pharmaceutical manufacturers and distributors. 10th ed. London: Pharmaceutical Press, pp. 90-109.
- Sutton, S. (2011) Accuracy of plate counts. Journal of Validation Technology 17(3), pp. 42-46.
- Sandle, T. (2015) Pharmaceutical Microbiology: Essentials for quality assurance and quality control. Woodhead Publishing.
- Senvar, O. and Firat, S.U.O. (2010) An overview of capability evaluation of measurement systems and gauge repeatability and reproducibility studies. International Journal of Metrology and Quality Engineering 1(2), pp. 121-127.
- Arienzo, A., Losito, F., Stalio, O. & Antonini, G. (2016) Comparison of uncertainty between traditional and alternative methods for food microbiological analysis. American Journal of Food Technology 11, pp. 29-36.
- Besterfield, D. (2013) Quality improvement (formerly entitled Quality control). 9th ed. Pearson Education.
- Clais, S., Boulet, G., Van Kerckhoven, M., Lanckacker, E., Delputte, P., Maes, L. & Cos, P. (2015) Comparison of viable plate count, turbidity measurement and real-time PCR for quantification of Porphyromonas gingivalis. Letters in Applied Microbiology 60, pp. 79-84.
- Jarvis, B., Correy, J.E.L. & Hedges, A.J. (2006) Estimates of measurement uncertainty from proficiency testing schemes, internal laboratory quality monitoring and during routine enforcement examination of foods. Journal of Applied Microbiology 103, pp. 462-467.
- Van Nevel, S., Koetzsch, S., Proctor, C.R., Besmer, M.S., Prest, E.I., Vrouwenvelder, J.S., Knezev, A., Boon, N. & Hammes, F. (2017) Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring. Water Research 113, pp. 191-206.
- Brugger, S.D., Baumberger, C., Jost, M., Jenni, W., Brugger, U. & Mühlemann, K. (2012) Automated counting of bacterial colony forming units on agar plates. PLoS ONE 7(3), e33695.
- Clarke, M.I., Burton, R.L., Hill, A.N., Litorja, M., Nahm, M.H. & Hwang, J. (2010) Low-cost, high-throughput, automated counting of bacterial colonies. Cytometry 77A, pp. 790-797.
- Allen, M.J., Edberg, S.C. & Reasoner, D.J. (2004) Heterotrophic plate count bacteria – what is their significance in drinking water? International Journal of Food Microbiology 92, pp. 265-274.
- Gensberger, E.T., Gössl, E.M., Antonielli, L., Sessitsch, A. & Kostić, T. (2015) Effect of different heterotrophic plate count methods on the estimation of the composition of the culturable microbial community. PeerJ 3, e862.
- EC: European Commission (2008) Annex 1: Manufacture of sterile medicinal products. In: EudraLex the rules governing medicinal products in the European Union. Volume 4 EU Guidelines of good manufacturing practice for medicinal products for human and veterinary use.
- Environment Agency (2012) The Microbiology of Drinking Water. Part 7 – Methods for the enumeration of heterotrophic bacteria. Standing Committee of Analysts (SCA) Blue Book 238. Available at Gov.UK.
- PHE: Public Health England (2014) Food and water PT schemes: a guide to the scoring systems and statistics used for the PHE proficiency testing schemes for food and water microbiology. London: Public Health England Food and Environmental Proficiency Testing Unit (FEPTU).
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