Njage Patrick Murigu Kamau, Sawe Chemutai Tonui, Onyango Cecilia Moraa, Habib I, Njagi Edmund Njeru, Aerts Marc, Molenberghs Geert
Department of Food Science, Nutrition and Technology, University of Nairobi, Nairobi, Kenya.
Department of Plant Science and Crop Protection, University of Nairobi, Nairobi, Kenya.
J Food Prot. 2017 Jan;80(1):177-188. doi: 10.4315/0362-028X.JFP-16-233.
Current approaches such as inspections, audits, and end product testing cannot detect the distribution and dynamics of microbial contamination. Despite the implementation of current food safety management systems, foodborne outbreaks linked to fresh produce continue to be reported. A microbial assessment scheme and statistical modeling were used to systematically assess the microbial performance of core control and assurance activities in five Kenyan fresh produce processing and export companies. Generalized linear mixed models and correlated random-effects joint models for multivariate clustered data followed by empirical Bayes estimates enabled the analysis of the probability of contamination across critical sampling locations (CSLs) and factories as a random effect. Salmonella spp. and Listeria monocytogenes were not detected in the final products. However, none of the processors attained the maximum safety level for environmental samples. Escherichia coli was detected in five of the six CSLs, including the final product. Among the processing-environment samples, the hand or glove swabs of personnel revealed a higher level of predicted contamination with E. coli , and 80% of the factories were E. coli positive at this CSL. End products showed higher predicted probabilities of having the lowest level of food safety compared with raw materials. The final products were E. coli positive despite the raw materials being E. coli negative for 60% of the processors. There was a higher probability of contamination with coliforms in water at the inlet than in the final rinse water. Four (80%) of the five assessed processors had poor to unacceptable counts of Enterobacteriaceae on processing surfaces. Personnel-, equipment-, and product-related hygiene measures to improve the performance of preventive and intervention measures are recommended.
当前诸如检查、审核和成品检测等方法无法检测微生物污染的分布和动态情况。尽管实施了现行食品安全管理体系，但与新鲜农产品相关的食源性疾病暴发仍时有报道。采用微生物评估方案和统计模型，对肯尼亚五家新鲜农产品加工和出口公司核心控制与保证活动的微生物表现进行了系统评估。针对多变量聚类数据的广义线性混合模型和相关随机效应联合模型，随后进行经验贝叶斯估计，能够将关键采样地点（CSL）和工厂的污染概率作为随机效应进行分析。最终产品中未检测到沙门氏菌属和单核细胞增生李斯特菌。然而，没有一家加工商在环境样本方面达到最高安全水平。在六个CSL中的五个，包括最终产品中，检测到了大肠杆菌。在加工环境样本中，人员的手部或手套擦拭样本显示大肠杆菌的预测污染水平更高，并且在这个CSL中，80%的工厂大肠杆菌呈阳性。与原材料相比，最终产品显示出具有最低食品安全水平的更高预测概率。尽管60%的加工商原材料大肠杆菌呈阴性，但最终产品大肠杆菌呈阳性。进水口水中大肠菌群的污染概率高于最终冲洗水。在五家接受评估的加工商中，有四家（80%）在加工表面的肠杆菌科计数较差或不可接受。建议采取与人员、设备和产品相关的卫生措施，以提高预防和干预措施的效果。
