Bivariate confidence probability plots as a method to test the accuracy and variability of microbiological measures

Abstract

AbstractIntroductionIn an interlaboratory calibration analysis to validate a methodology that will be proposed as a European standard for domestic laundry disinfection, tests were carried out to detect if there are different behaviors in the measurements regarding accuracies and variabilities. Interlaboratory tests using different doses of disinfectant and microorganisms were carried out. ISO 5725-2 and ISO 13528 form the basis of validations of quantitative methods, providing validation specifications for interlaboratory studies. However, a need for a simple graphical method to detect interlaboratory differences in accuracy and variability was observed.ObjectivesThe general goal of this work is to present a new exploratory methodology, graphical and easy to interpret, that can determine the accuracy and variability (precision) of a variable, and compare it to the methodology applied in ISO 5725-2 and ISO 13528.MethodsWe used confidence probability plots of the multivariate Student’s t-distribution to observe the accuracy and variability of microbiological measures carried out by different laboratories during a ring trial exercise. A function in R was built for this purpose: Miriam.analysis.ellipse(Y, factor_a, eel.plot = “ t-Student”). The different observations of accuracy and variability are represented in the ellipses. If any of the points are outside the ellipse with 95% confidence, we can assume a deviation in accuracy and / or variability.ResultsTwo examples are provided with real microbiological data (logarithmic unit reductions (LR) for Pseudomonas aeruginosa, Escherichia coli, Staphilococcus aureus, Enterococcus hirae, Candida albicans and microbial counts in water (WW)). The proposed new method allowed us to detect possible deviations in the WWMEA variable and we believe it has future application for the rapid control of microbiological measures.