Pre-harvest testing is increasingly used to enhance the microbial safety of fresh produce. Traditional sampling assumes that sample collectors have no information on potential contamination sources. Knowledge of such factors could potentially increase the effectiveness of pre-harvest sampling programs. Simulation modeling and field validation trials were used to evaluate a hybrid “Samples of Opportunity” (SOO) sampling method that included a portion of the samples based on the sampler’s knowledge of risk factors in pre-harvest produce fields. Relative effectiveness of SOO sampling was compared with three traditional sampling methods. These evaluations were based on three non-random contamination scenarios. The mean detection probability of SOO is 96% higher than traditional sampling methods (p<0.001). However, if the site of actual contamination is offset from assumed area of contamination, the detection probability of SOO sampling drops, and becomes similar or even worse than that achieved by the other sampling methods. Preliminary field validation trials indicated indeed that SOO performed better than the other three sampling methods. This study provides a mathematical approach for evaluating the effectiveness of four pre-harvest sampling methods, and suggests that having a priori knowledge of the contamination source in the field would improve effectiveness of sampling, particularly if done using a standardized protocol.

收获前测试越来越多地用于增强新鲜农产品的微生物安全性。传统采样假设采样器没有潜在污染源的信息。对此类因素的了解可能会提高收获前采样计划的有效性。模拟建模和现场验证试验用于评估混合的“机会样本”（SOO）抽样方法，该方法基于采样者对收获前农产品田间风险因素的了解，包括一部分样本。将SOO抽样的相对有效性与三种传统的抽样方法进行了比较。这些评估基于三种非随机污染方案。SOO的平均检测概率比传统采样方法高96％（p <0.001）。然而，如果实际污染的位置偏离了假定的污染区域，则SOO采样的检测概率将下降，并且与其他采样方法所获得的检测相似或什至更差。初步的现场验证试验确实表明，SOO的性能优于其他三种采样方法。这项研究提供了一种数学方法来评估四种收获前采样方法的有效性，并建议对现场污染源的先验知识将提高采样效率，特别是如果使用标准化协议进行采样的话。