Department of Civil and Environmental Engineering and Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
Environ Sci Technol. 2010 Mar 1;44(5):1705-12. doi: 10.1021/es902237f.
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.
基于枚举的方法常用于量化水系统中的微生物和微观离散颗粒，但其在样品处理过程中可能会造成损失，或在计数过程中出现错误。分析回收率（分析人员使用特定的枚举方法成功计数样品中每个感兴趣的微生物或颗粒的能力）通常通过对用已知数量的微生物或颗粒接种的样品进行计数来评估。概率模型被开发出来以解释接种和分析误差对回收率数据的影响，通过蒙特卡罗模拟获得的概率区间用于评估回收率实验设计（即接种方法、接种颗粒的数量和样品的数量）。矩法、最大似然估计和置信区间被用于统计分析回收率实验结果。发现低数量或不确定数量的接种颗粒会导致回收率数据的变化，而这种变化不是由于分析回收率造成的，如果可能的话，应尽量避免。这种额外的变异性降低了实验结果的重现性，需要使用统计分析技术，例如使用概率模型进行最大似然估计，该模型考虑了回收率数据中采样和分析误差的影响。
