Nutrition & Food Science
Permanent URI for this communityhttp://hdl.handle.net/1903/2267
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Item EVALUATION AND MODELING OF RISK FACTORS ASSOCIATED WITH MICROBIAL CONTAMINATION IN PRODUCE PRE-HARVEST ENVIRONMENT(2017) Pang, Hao; Pradhan, Abani K; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Produce (fruits and vegetables) has been frequently linked to foodborne disease outbreaks in the United States and worldwide. Produce-related outbreaks have been traced back to contamination occurring at pre-harvest production level. The overall goal of this dissertation was to identify risk factors for produce pre-harvest contamination and develop models to predict the introduction, survival, and persistence of enteric foodborne bacteria in produce at the pre-harvest level. Produce from mixed farms, where vegetable crops and animals are grown at the same premise, is potentially at higher risk of microbial contamination due to its proximity to environmental reservoirs such animal enclosures and composting facilities. Such contamination can be affected by meteorological factors such as temperature, precipitation, and wind speed. By integrating microbial sampling and meteorological data, the effects of meteorological factors on prevalence and concentration of Listeria species and generic Escherichia coli in samples collected from a mixed produce and dairy farm were analyzed using logistic regression and tree-based methods. The developed models have robust predictive ability and can be used to estimate the risk of microbial contamination in mixed farms under different weather conditions. Survival and persistence of pathogens in field soil is a food safety concern as soil can serve as a source and route for microbial contamination in produce. Regression models were developed to evaluate the effects of meteorological factors, cover cropping, and farming system on the survival and persistence of generic E. coli and L. innocua in produce field soil. The models revealed that survival of E. coli and persistence of L. innocua were predominately influenced by temperature, precipitation, and relative humidity. Further, data from a large microbial sampling study were used to determine the effects of a variety of meteorological, environmental, and farm management factors on the presence and concentration of food safety and quality bacteria indicator in tomatoes and tomato environmental samples. The results suggest that microbial contamination in tomatoes and in tomato production environments can be significantly affected by certain meteorological conditions, environmental factors, and farm management practices. In conclusion, this study identified potential risk factors associated with the presence, concentration, survival, and persistence of enteric foodborne bacteria in produce and in produce production environments. The developed models can be used to predict the risk of microbial contamination in produce farms under different meteorological conditions, geographical regions, and farm management practices. Such information and tools will help growers to improve farm management practices to reduce potential contamination of produce.Item Quantitative Risk Assessment for Escherichia coli O157:H7 in Fresh-cut Lettuce(2013) Pang, Hao; Pradhan, Abani K; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A farm-to-fork quantitative microbial risk assessment (QMRA) model was developed to estimate the risk of illnesses associated with Escherichia coli O157:H7 in fresh-cut lettuce, and to evaluate the effects of potential intervention strategies on reducing public health risks. Assuming a prevalence of 0.1% of lettuce entering the processing plant, the baseline model reflecting current industry practices predicted an average of 2,160 cases per year in the United States. For each of the additional intervention strategies evaluated, health risks were reduced by 11- to 18-fold. Treatment with ultrasound and organic acid combination was the most effective, reducing the mean number of cases by approximately 18-fold compared to baseline model. The developed risk model can be used to estimate the public health risk ofE. coli O157:H7 from fresh-cut lettuce and to evaluate different potential intervention strategies to mitigate such risk.