Quantification of Analytical Recovery in Particle and Microorganism Enumeration Methods

Abstract

Enumeration-based methods that are often used to quantify microorganisms and microscopic discrete particles in aqueous systems may include losses during sample processing or errors in counting. Analytical recovery (the capacity of the analyst to successfully count each microorganism or particle of interest in a sample using a specific enumeration method) is frequently assessed by enumerating samples that are seeded with known quantities of the microorganisms or particles. Probabilistic models were developed to account for the impacts of seeding and analytical error on recovery data, and probability intervals, obtained by Monte Carlo simulation, were used to evaluate recovery experiment design (i.e., seeding method, number of seeded particles, and number of samples). The method of moments, maximum likelihood estimation, and credible intervals were used to statistically analyze recovery experiment results. Low or uncertain numbers of seeded particles were found to result in variability in recovery data that was not due to analytical recovery, and should be avoided if possible. This additional variability was found to reduce the reproducibility of experimental results and necessitated the use of statistical analysis techniques, such as maximum likelihood estimation using probabilistic models that account for the impacts of sampling and analytical error in recovery data.

Enumerating Sparse Organisms in Ships’ Ballast Water: Why Counting to 10 Is Not So Easy

“…13,14 Recovery errors are expected to result in under-counting rather than over-counting (i.e., sample bias, Table S1). Although existing BW testing data are insufficient to accurately parameterize recovery errors, we investigated how hypothetical rates of zooplankton recovery (100, 90, 75, and 50%) strongly affect the power to detect noncompliance.…”

“…In the absence of standardized sampling and analytical protocols, currently available data are insufficient to create a comprehensive model that quantifies all sources of uncertainty for BW discharge analysis, as has been possible for drinking water. 13,14 …”

Importance of Distributional Forms for the Assessment of Protozoan Pathogens Concentrations in Drinking‐Water Sources

“…The Beta‐binomial model for analytical recovery assumes that the number of seeded (oo)cysts is precisely known, the analytical recovery is Beta distributed with mean values of parameters , and the analytical error is binomially distributed (Schmidt, Emelko, & Reilly, 2010). The uncertainty of the Beta distribution parameters was not considered in the analysis.…”

Monitoring the Influence of Anthropogenic Pollution on the Quality of Irrigation Water for Market Gardening in Yamoussoukro, Côte d’Ivoire

“…PCR amplification was performed in a volume of 25 μl containing DNA (15 ng.µl-1), 10 μl from Master Mix, 2.5 μl (5 μM) for the 2 primers, dNTP (10mM). The PCR programme consisted of 35 cycles of denaturation at 94C/50 s, annealing at 53C/40 s and elongation at 72C/30 s (Schmidt et al, 2010). Ten microliters of each amplifier were placed on 1% agarose gel in TBE buffer and then in 0.5 μg/ml Ethidium bromide to verify the expected size.…”