• Journal of Korean Society on Water Environment
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• v.25 no.1
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• pp.69-75
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• 2009

Water stress has become a major concern in agriculture. Korea suffers from limited agricultural water supply, and wastewater reuse has been recommended as an alternative solution. A study was performed to examine the effects of microorganism concentration in the ponded-water of a paddy rice field with reclaimed-water irrigation for evaluating the microbial risk to farmers and neighborhood children. Most epidemiological studies were performed based on an upland field, and they may not directly applicable to paddy fields. Beta-Poisson model was used to estimate the microbial risk of pathogen ingestion. Their risk value increased significantly high level after irrigation and precipitation. It implies that agricultural activities such as plowing, and fertilizing, and precipitation need be practiced a few days after irrigation considering health risks. The results about field application of the microbial risk assessment using E. coli showed difference according to monitoring time and treatment plot. Result of the microbial risk assessment showed that risk values of ground-water and reclaimed secondary wastewater irrigation were lower than directly use of wastewater treatment plants' effluent. This paper should be viewed as a first step in the application of quantitative microbial risk assessment of E. coli to wastewater reuse in a paddy rice farming.

• Journal of Food Hygiene and Safety
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• v.14 no.4
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• pp.319-326
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• 1999

Recently, it is continuously rising to concern about the health risk being induced by microorganisms in food such as Escherichia coli O157:H7 and Listeria monocytogenes. Various organizations and regulatory agencies including U.S.FPA, U.S.DA and FAO/WHO are preparing the methodology building to apply microbial quantitative risk assessment to risk-based food safety program. Microbial risks are primarily the result of single exposure and its health impacts are immediate and serious. Therefore, the methodology of risk assessment differs from that of chemical risk assessment. Microbial quantitative risk assessment consists of tow steps; hazard identification, exposure assessment, dose-response assessment and risk characterization. Hazard identification is accomplished by observing and defining the types of adverse health effects in humans associated with exposure to foodborne agents. Epidemiological evidence which links the various disease with the particular exposure route is an important component of this identification. Exposure assessment includes the quantification of microbial exposure regarding the dynamics of microbial growth in food processing, transport, packaging and specific time-temperature conditions at various points from animal production to consumption. Dose-response assessment is the process characterizing dose-response correlation between microbial exposure and disease incidence. Unlike chemical carcinogens, the dose-response assessment for microbial pathogens has not focused on animal models for extrapolation to humans. Risk characterization links the exposure assessment and dose-response assessment and involve uncertainty analysis. The methodology of microbial dose-response assessment is classified as nonthreshold and thresh-old approach. The nonthreshold model have assumption that one organism is capable of producing an infection if it arrives at an appropriate site and organism have independence. Recently, the Exponential, Beta-poission, Gompertz, and Gamma-weibull models are using as nonthreshold model. The Log-normal and Log-logistic models are using as threshold model. The threshold has the assumption that a toxicant is produce by interaction of organisms. In this study, it was reviewed detailed process including risk value using model parameter and microbial exposure dose. Also this study suggested model application methodology in field of exposure assessment using assumed food microbial data(NaCl, water activity, temperature, pH, etc.) and the commercially used Food MicroModel. We recognized that human volunteer data to the healthy man are preferred rather than epidemiological data fur obtaining exact dose-response data. But, the foreign agencies are studying the characterization of correlation between human and animal. For the comparison of differences to the population sensitivity: it must be executed domestic study such as the establishment of dose-response data to the Korean volunteer by each microbial and microbial exposure assessment in food.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.12
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• pp.1684-1688
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• 2017

Objective: From a review of published information on genetic association studies, a meta-analysis was conducted to determine the influence of the genes ${\kappa}-casein$ (CSN3) and ${\beta}-lactoglobulin$ (LGB) on milk yield traits in Holstein, Jersey, Brown Swiss, and Fleckvieh. Methods: The GLIMMIX procedure was used to analyze milk production and percentage of protein and fat in milk. Models included the main effects and all their possible two-way interactions; not estimable effects and non-significant (p>0.05) two-way interactions were dropped from the models. The three traits analyzed used Poisson distribution and a log link function and were determined with the Interactive Data Analysis of SAS software. Least square means and multiple mean comparisons were obtained and performed for significant main effects and their interactions (p<0.0255). Results: Interaction of breed by gene showed that Holstein and Fleckvieh were the breeds on which CSN3 ($6.01%{\pm}0.19%$ and $5.98%{\pm}0.22%$), and LGB ($6.02%{\pm}0.19%$ and $5.70%{\pm}0.22%$) have the greatest influence. Interaction of breed by genotype nested in the analyzed gene indicated that Holstein and Jersey showed greater influence of the CSN3 AA genotype, $6.04%{\pm}0.22%$ and $5.59%{\pm}0.31%$ than the other genotypes, while LGB AA genotype had the largest influence on the traits analyzed, $6.05%{\pm}0.20%$ and $5.60%{\pm}0.19%$, respectively. Furthermore, interaction of type of statistical model by genotype nested in the analyzed gene indicated that CSN3 and LGB genes had similar behavior, maintaining a difference of more than 7% across analyzed genotypes. These results could indicate that both Holstein and Jersey have had lower substitution allele effect in selection programs that include CSN3 and LGB genes than Brown Swiss and Fleckvieh. Conclusion: Breed determined which genotypes had the greatest association with analyzed traits. The mixed model based in Bayesian or Ridge Regression was the best alternative to analyze CSN3 and LGB gene effects on milk yield and protein and fat percentages.

• Journal of Food Hygiene and Safety
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• v.35 no.1
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• pp.37-44
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• 2020

This study investigated the probability of foodborne illness caused by raw oyster consumption contaminated with high risk Vibrio species such as V. vulnificus and V. cholerae. Eighty-eight raw oyster samples were collected from the south coast, west coast and Seoul areas, and examined for the prevalence of high risk Vibrio species. The growth patterns of V. vulnificus and V. cholerae in raw oysters were evaluated, and consumption frequency and amounts for raw oyster were investigated from a Korean National Health and Nutrition Examination Survey. With the collected data, a risk assessment simulation was conducted to estimate the probability of foodborne illness caused by intake of raw oysters, using @RISK. Of 88 raw oysters, there were no V. vulnificus- or V. cholerae-positive samples. Thus, initial contamination levels of Vibrio species in raw oysters were estimated by the statistical methods developed by Vose and Sanaa, and the estimated value for the both Vibrio spp. was -3.6 Log CFU/g. In raw oyster, cell counts of V. vulnificus and V. cholerae remained unchanged. The incidence of raw oyster consumers was 0.35%, and the appropriate probabilistic distribution for the consumption amounts was the exponential distribution. A risk assessment simulation model was developed with the collected data, and the probability of the foodborne illness caused by the consumption of raw oyster was 9.08×10^-15 for V. vulnificus and 8.16×10^-13 for V. cholerae. Consumption frequency was the first factor, influencing the probability of foodborne illness.