Microbiological Risk from Minimally Processed Packaged Salads in the Dutch Food Chain

2014 ◽  
Vol 77 (3) ◽  
pp. 395-403 ◽  
Author(s):  
ANNEMARIE PIELAAT ◽  
FRANS M. van LEUSDEN ◽  
LUCAS M. WIJNANDS
Keyword(s):  
Risk Assessment ◽  
Quantitative Microbial Risk Assessment ◽  
Concentration Data ◽  
Production Chain ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
E Coli ◽  
Human Illness ◽  
Campylobacter Spp ◽  
Microbiological Surveillance

The objective of this study was to evaluate the microbial hazard associated with the consumption of mixed salads produced under standard conditions. The presence of Salmonella, Campylobacter spp., and Escherichia coli O157 in the Dutch production chain of mixed salads was determined. Microbial prevalence and concentration data from a microbiological surveillance study were used as inputs for the quantitative microbial risk assessment. Chain logistics, production figures, and consumption patterns were combined with the survey data for the risk assessment chain approach. The results of the sample analysis were used to track events from contamination through human illness. Wide 95% confidence intervals around the mean were found for estimated annual numbers of illnesses resulting from the consumption of mixed salads contaminated with Salmonella Typhimurium DT104 (0 to 10,300 cases), Campylobacter spp. (0 to 92,000 cases), or E. coli (0 to 800 cases). The main sources of uncertainty are the lack of decontamination data (i.e., produce washing during processing) and an appropriate dose-response relationship.

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes in Leafy Green Vegetables Consumed at Salad Bars

2010 ◽  
Vol 73 (2) ◽  
pp. 274-285 ◽  
Author(s):  
E. FRANZ ◽  
S. O. TROMP ◽  
H. RIJGERSBERG ◽  
H. J. van der FELS-KLERX
Keyword(s):  
Public Health ◽  
Escherichia Coli ◽  
Risk Assessment ◽  
Monte Carlo ◽  
Escherichia Coli O157 ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
The Public ◽  
E Coli

Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5°C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.

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.

scholarly journals Quantitative microbial risk assessment related to urban wastewater and lagoon water reuse in Abidjan, Côte d'Ivoire

2013 ◽  
Vol 12 (2) ◽  
pp. 301-309 ◽  
Author(s):  
R. I. Yapo ◽  
B. Koné ◽  
B. Bonfoh ◽  
G. Cissé ◽  
J. Zinsstag ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Quantitative Microbial Risk Assessment ◽  
Urban Wastewater ◽  
Cote D’Ivoire ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
E Coli ◽  
Lagoon Water

We assessed the infection risks related to the use of wastewater in Abidjan, Côte d'Ivoire, by using quantitative microbial risk assessment (QMRA). Giardia lamblia and Escherichia coli were isolated and identified in wastewater samples from the canal and lagoon. The exposure assessment was conducted using a cross-sectional survey by questionnaire with 150 individuals who were in contact with the wastewater during their daily activities of swimming, fishing, washing, and collecting materials for reuse. Risk was characterised using the Monte Carlo simulation with 10,000 iterations. Results showed high contamination of water by G. lamblia and E. coli (12.8 CFU/100 mL to 2.97 × 104CFU/100 mL and from 0 cyst/L to 18.5 cysts/L, respectively). Estimates of yearly average infection risks for E. coli (90.07–99.90%, assuming that 8% of E. coli were E. coli O157:H7) and G. lamblia (9.4–34.78%) were much higher than the acceptable risk (10−4). These results suggest the need for wastewater treatment plants, raising awareness in the population in contact with urban wastewater and lagoon water. Our study also showed that QMRA is appropriate to study health risks in settings with limited data and budget resources.

Modeling the Risk of Salmonellosis from Consumption of Peanuts in the United States

2019 ◽  
Vol 82 (4) ◽  
pp. 579-588 ◽  
Author(s):  
KAITLYN E. CASULLI ◽  
STEPHEN CALHOUN ◽  
DONALD W. SCHAFFNER
Keyword(s):  
United States ◽  
Risk Assessment ◽  
Geometric Mean ◽  
The United States ◽  
Economic Losses ◽  
Quantitative Microbial Risk Assessment ◽  
Concentration Data ◽  
Modeling Tools ◽  
Microbial Risk Assessment ◽  
Microbial Risk

ABSTRACT Peanut products were the target of the largest food recall in United States history from 2008 to 2009, with more than 3,200 products implicated, economic losses estimated at $1 billion, and more than 700 reported illnesses and 9 deaths. Predictive modeling tools such as quantitative microbial risk assessment can be used to aid processors in making risk management decisions that may reduce the chances of foodborne illness, but published risk assessment for peanuts is not currently available. A quantitative microbial risk assessment was performed to quantify salmonellosis risk from consumption of peanuts in the United States. Prevalence and concentration data for Salmonella on raw, shelled peanuts were used in combination with probability distributions of simulated log reductions achieved during production steps before consumption. Data for time-temperature combinations used in each step were obtained from published literature, industry surveys, or expert opinion, and survival data were obtained from the literature. A beta-Poisson dose-response model was used to predict probability of illness from ingestion of Salmonella cells. The model predicted 14.2 (arithmetic mean) or 0.0123 (geometric mean) illnesses per year. Sensitivity analysis showed that thermal inactivation log reductions applied had the biggest impact on predicted salmonellosis risk, followed by consumer storage time, Salmonella starting concentration, Salmonella starting prevalence, and number of originally contaminated 25-g servings per originally positive 375-g sample. Scenario analysis showed that increasing log reduction variability increased mean salmonellosis risk. Removing the effect of storage on Salmonella survival increased the arithmetic and geometric means to 153 and 0.598 illnesses per year, respectively. This study indicated that the risk of salmonellosis from consumption of peanuts can be lowered by reducing field contamination, control of storage steps, and monitoring of appropriate critical limits in peanut roasting.

scholarly journals Quantitative Microbial Risk Assessment for Campylobacter spp. on Ham in Korea

2015 ◽  
Vol 35 (5) ◽  
pp. 674-682 ◽  
Author(s):  
Jeeyeon Lee ◽  
Jimyeong Ha ◽  
Sejeong Kim ◽  
Heeyoung Lee ◽  
Soomin Lee ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Campylobacter Spp
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