title: 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.
authors.affiliation: Department of Civil and Environmental Engineering and Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Schmidt
authors.forename: Monica B
authors.initials: MB
authors.lastname: Emelko
authors.forename: Park M
authors.initials: PM
authors.lastname: Reilly
mesh_terms: D000465:Algorithms; D003458:Cryptosporidium / Q000254:growth & development; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D011156:Population Density; D011336:Probability; D018401:Sample Size; D035501:Uncertainty