A quantitative microbial risk assessment model for total coliforms and E. coli in surface runoff following application of biosolids to grassland

2017 ◽  
Vol 224 ◽  
pp. 739-750 ◽  
Author(s):  
Rachel Clarke ◽  
Dara Peyton ◽  
Mark G. Healy ◽  
Owen Fenton ◽  
Enda Cummins
Keyword(s):  
Risk Assessment ◽  
Surface Runoff ◽  
Assessment Model ◽  
Quantitative Microbial Risk Assessment ◽  
Risk Assessment Model ◽  
Microbial Risk Assessment ◽  
Total Coliforms ◽  
Microbial Risk ◽  
E Coli

Comparing the Effect of Various Contamination Levels for Salmonella in Chicken Meat Preparations on the Probability of Illness in Belgium

2009 ◽  
Vol 72 (10) ◽  
pp. 2093-2105 ◽  
Author(s):  
MIEKE UYTTENDAELE ◽  
KATLEEN BAERT ◽  
KOEN GRIJSPEERDT ◽  
LIEVEN DE ZUTTER ◽  
BENOIT HORION ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Chicken Meat ◽  
Assessment Model ◽  
Contamination Level ◽  
Quantitative Microbial Risk Assessment ◽  
Risk Assessment Model ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Increased Risk ◽  
Fixed Input

At the urging of competent national authorities, a limited risk assessment on Salmonella in chicken meat preparations in Belgium was undertaken following a retail-to-table approach. The input distribution of Salmonella was based on surveillance data in Belgium. To analyze the relative impact of reducing the risk of salmonellosis associated with a decrease in the Salmonella contamination level, different distributions based on the actual situation but limiting the number of portions containing Salmonella at 1 CFU per 1, 10, and 25 g of meat were used in the quantitative microbial risk assessment model. The quantitative microbial risk assessment model also was run several times with a theoretical fixed input of Salmonella assuming all portions possessed the same fixed contamination level set at 1,000, 100, 10, and 1 CFU/g of meat and 1 CFU per 10, 25, 100, and 1,000 g of meat. With regard to the initial contamination level, the results indicate, both by the narrowing of the current distribution and by the fixed input, that especially the higher levels of contamination (>1 CFU/g) contribute to the increased risk for salmonellosis.

Quantitative Assessment of the Microbial Risk of Leafy Greens from Farm to Consumption: Preliminary Framework, Data, and Risk Estimates

2011 ◽  
Vol 74 (5) ◽  
pp. 700-708 ◽  
Author(s):  
MICHELLE D. DANYLUK ◽  
DONALD W. SCHAFFNER
Keyword(s):  
Risk Assessment ◽  
Risk Model ◽  
Assessment Model ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
E Coli ◽  
Leafy Greens ◽  
Risk Estimates ◽  
Washing Process

This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of −1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of −1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.

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