1 Red Wolf Consulting, 325 East Grand River Avenue, Suite 345, East Lansing, Michigan 48823 (ORCID: https://orcid.org/0000-0003-1234-257X ).
2 American Peanut Council, 1500 King Street, Suite 301, Alexandria, Virginia 22314.
J Food Prot. 2019 Apr;82(4):579-588. doi: 10.4315/0362-028X.JFP-18-314.
Peanut products were the target of the largest food recall in United States history from 2008 to 2009, with more than 3,200 products implicated, economic losses estimated at $1 billion, and more than 700 reported illnesses and 9 deaths. Predictive modeling tools such as quantitative microbial risk assessment can be used to aid processors in making risk management decisions that may reduce the chances of foodborne illness, but published risk assessment for peanuts is not currently available. A quantitative microbial risk assessment was performed to quantify salmonellosis risk from consumption of peanuts in the United States. Prevalence and concentration data for Salmonella on raw, shelled peanuts were used in combination with probability distributions of simulated log reductions achieved during production steps before consumption. Data for time-temperature combinations used in each step were obtained from published literature, industry surveys, or expert opinion, and survival data were obtained from the literature. A beta-Poisson dose-response model was used to predict probability of illness from ingestion of Salmonella cells. The model predicted 14.2 (arithmetic mean) or 0.0123 (geometric mean) illnesses per year. Sensitivity analysis showed that thermal inactivation log reductions applied had the biggest impact on predicted salmonellosis risk, followed by consumer storage time, Salmonella starting concentration, Salmonella starting prevalence, and number of originally contaminated 25-g servings per originally positive 375-g sample. Scenario analysis showed that increasing log reduction variability increased mean salmonellosis risk. Removing the effect of storage on Salmonella survival increased the arithmetic and geometric means to 153 and 0.598 illnesses per year, respectively. This study indicated that the risk of salmonellosis from consumption of peanuts can be lowered by reducing field contamination, control of storage steps, and monitoring of appropriate critical limits in peanut roasting.
花生制品是 2008 年至 2009 年美国历史上最大规模食品召回的目标，涉及 3200 多种产品，经济损失估计达 10 亿美元，报告的疾病超过 700 例，死亡 9 例。预测性建模工具，如定量微生物风险评估，可用于帮助加工商做出风险管理决策，从而降低食源性疾病的发生几率，但目前尚未公布针对花生的风险评估。本研究采用定量微生物风险评估方法，对美国食用花生导致沙门氏菌病的风险进行量化。使用了生的、去壳花生中沙门氏菌的流行率和浓度数据，并结合了消费前生产步骤中模拟的对数减少概率分布。各步骤中使用的时间-温度组合数据来源于已发表文献、行业调查或专家意见，生存数据则来源于文献。采用 Beta-Poisson 剂量-反应模型预测摄入沙门氏菌细胞引起疾病的概率。该模型预测每年会发生 14.2 例（算术平均值）或 0.0123 例（几何平均值）疾病。敏感性分析表明，应用的热失活对数减少对预测沙门氏菌病风险的影响最大，其次是消费者储存时间、沙门氏菌起始浓度、沙门氏菌起始流行率以及每批原始阳性 375 克样品中最初污染的 25 克份的数量。情景分析表明，增加对数减少变异性会增加平均沙门氏菌病风险。去除储存对沙门氏菌生存的影响，将算术平均值和几何平均值分别提高到每年 153 例和 0.598 例。本研究表明，通过降低田间污染、控制储存步骤以及监测花生烘烤过程中的适当关键限值，可降低食用花生导致沙门氏菌病的风险。
