title: Microbial Performance of Food Safety Control and Assurance Activities in a Fresh Produce Processing Sector Measured Using a Microbial Assessment Scheme and Statistical Modeling.
abstract: 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.
authors.affiliation: Department of Food Science, Nutrition and Technology, University of Nairobi, Nairobi, Kenya.
authors.forename: Patrick Murigu Kamau
authors.initials: PM
authors.lastname: Njage
authors.affiliation: Department of Food Science, Nutrition and Technology, University of Nairobi, Nairobi, Kenya.
authors.forename: Chemutai Tonui
authors.initials: CT
authors.lastname: Sawe
authors.affiliation: Department of Plant Science and Crop Protection, University of Nairobi, Nairobi, Kenya.
authors.forename: Cecilia Moraa
authors.initials: CM
authors.lastname: Onyango
authors.affiliation: College of Veterinary Medicine, Murdoch University, Murdoch 6150, Western Australia, Australia.
authors.forename: I
authors.initials: I
authors.lastname: Habib
authors.affiliation: Cancer Research UK Cancer Survival Group, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.|Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt, B-3590 Diepenbeek.
authors.forename: Edmund Njeru
authors.initials: EN
authors.lastname: Njagi
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt, B-3590 Diepenbeek.
authors.forename: Marc
authors.initials: M
authors.lastname: Aerts
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt, B-3590 Diepenbeek.|Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium.
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D001499:Bayes Theorem; D015169:Colony Count, Microbial; D003257:Consumer Product Safety; D004926:Escherichia coli; D005506:Food Contamination; D005511:Food Handling; D005516:Food Microbiology; D059022:Food Safety; D005525:Food-Processing Industry; D006801:Humans; D007630:Kenya; D008089:Listeria monocytogenes
keywords: Correlated random effects joint models; Empirical Bayes estimates; Fresh produce; Generalized linear mixed models; Microbial assessment scheme