Modeling the Level of Contamination of Staphylococcus aureus in Ready-to-Eat Kimbab in Korea

Abstract

The risk of Staphylococcus aureus in ready-to-eat kimbab (rice rolled in laver) sold in Korea was evaluated by a mathematical modeling approach. Four nodes were constructed from preparation at retail to consumption. A predictive microbial growth model and survey data were combined with probabilistic modeling to simulate the level of S. aureus in a single kimbab at the time of consumption. We estimated the mean level of S. aureus to be 2.92 log CFU/g for a typical kimbab (150 to 200 g each) at the time of consumption. Our model also showed that 29.73% of the kimbabs had > or = 100,000 S. aureus CFU/g, which poses some risk of illness, since some level of enterotoxin would be expected from toxigenic strains. However, because of the lack of dose-response models for staphylococcal enterotoxin, the final level of S. aureus in the kimbabs could not be used to estimate how many people would become ill from eating them. Correlation sensitivity results showed that consumer eating patterns and initial contamination levels at retail stores were the most significant risk factors for illness and that temperature control under 10 degrees C was a critical control point in kimbab retail establishments to prevent the growth of S. aureus.

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

“…Sensitivity and scenario analyses in these studies have identified the initial contamination levels together with temperatures and storage/holding times 127,130,131,133 and pH 131 as having the greatest impact on the assessment endpoints. In one study, the assumption concerning the threshold level for the number of S. aureus cells required for hazardous levels of enterotoxin to be produced contributed most to the uncertainty in the risk estimate.…”

“…Risk assessments of S. aureus encompass a range of approaches from illustrative examples 125 and partial risk assessments, 126 to quantitative microbial risk assessments (QMRA) based on probabilistic modeling. 127 Food products assessed include milk, 128,129 skim milk, 130 unripened raw-milk cheese, 131 pork-based Korean food, 127 kimbab, 132,133 home-cooked foods 126 and cream-filled baked goods. 10 The results of risk assessments are equally varied.…”

Word-of-mouth generated influences of different prepared dishes via online consumer purchases: Preliminary text-based research findings from "Jingdong Mall" flagship shops

“…Early research in foreign countries mainly focused on the impact of raw materials and their processing methods on the sensory quality of products [6,7], the difference between the vacuum low-temperature treatment process and the nutritional quality of traditional dishes [8,9]. With the passage of time, the research direction is gradually shifting to the establishment of the risk model for preventing bacterial contamination of prepared dishes [10], detection of MSG addition in food products [11], nutritional quality assessment of prepared dishes [12], Sterilisation technologies for the treatment of prepared dishes [13], the development of functional products, such as low-sodium seasonings [14] and the impact of industrialised prepared dishes and traditional dishes on public consumption habits [15]. In recent years, foreign research has focused more and more on improving the quality of prepared dishes.…”

Predictive growth modeling of Yersinia enterocolitica in fresh kimchi cabbage brassica pekinensis as a function of storage temperature

“…Recently, predictive food microbiology has been actively studied as an approach for controlling and managing pathogenic microorganisms that cause food poisoning [ 1 ]. Predictive microbiology provides a mathematical model for the proliferation, growth, and death of food-poisoning bacteria during the exposure assessment phase of microbial risk assessment to quantitatively evaluate food hazards [ 2 ]. Predictive microbiology is related to internal factors such as water activity, pH and NaCl content and external factors such as the storage temperature, humidity, and nutrition that affect microbial growth throughout the production, distribution, consumption and storage processes [ 3 ].…”