Identifying and modeling meteorological risk factors associated with pre-harvest contamination of Listeria species in a mixed produce and dairy farm. (December 2017)

• Record Type: Journal Article
• Authors: Pang, Hao; McEgan, Rachel; Mishra, Abhinav; Micallef, Shirley A.; Pradhan, Abani K.
• Is Part Of: Food research international. Volume 102(2017)
• Page Start: 355
• Page End: 363
• Publication Date: 2017-12
• Subjects: Listeria species -- Meteorological factors -- Mixed farm; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2017.09.029
• Languages: English
• ISSNs: 0963-9969

Abstract

Abstract: This study sought to investigate the prevalence of Listeria species (including L . monocytogenes ) in a mixed produce and dairy farm and to identify specific meteorological factors affecting Listeria spp. presence. Environmental samples were collected monthly from locations within the mixed farm over 14 months and were analyzed for Listeria spp. Meteorological factors were evaluated for their association with the presence of Listeria spp. by using logistic regression (LR) and random forest (RF). The developed LR model identified wind speed and precipitation as significant risk factors ( P < 0.05), indicating higher wind speed at day 2 prior to sampling and higher average precipitation over the previous 25 days before sampling increased the probability of isolation of Listeria spp. from the mixed farm. Results from RF revealed that average wind speed at day 2 prior to sampling and average precipitation in the previous 25 days before sampling were the most important factors influencing the presence of Listeria spp., which supported the findings from LR. These findings indicate that the occurrence of Listeria spp. was influenced by wind speed and precipitation, suggesting run-off and wind-driven dust might be possible routes of pathogen transmission in mixed farms. The developed LR and RF models, with robust predictive performances as measured by the area under the receiver operating characteristic curves, can be used to predict Listeria spp. contamination risk in a mixed farm under different weather conditions and can help with the evaluation of farm management practices and the development of control strategies aimed at reducing pre-harvest microbial contamination in a mixed farming system. Graphical abstract: Highlights: Prevalence of Listeria spp. is influenced by precipitation and wind speed. Prevalence of Listeria spp. can be predicted under different weather conditions. Run-off and contaminated dust are possible routes of Listeria spp. contamination.