Development and Validation of Predictive Models of Esherichia coli O157:H7 Growth in Paprika

Abstract

This study was carried out to develop and validate predictive models of E. coli O157:H7 growth. Growth data of E. coli O157:H7 in Paprika were collected at 12, 24, 30 and 36 o C. The population increased into 3.0 to 3.8 log10 CFU/g within 4 days, then continued to increase at a slower rate through 10 days of storage at 12 o C. The lag time (LT) and maximum specific growth rate (SGR) obtained from each primary model was then modeled as a function of temperature using Davey and square root equations, respectively. For interpolation of performance evaluation, growth data for a mixture of E. coli O157:H7 were collected at time intervals in paprika incubated at the different temperatures, which was not used in model development. Results of model performance for interpolation data demonstrated that induced secondary models showed acceptable goodness of fit. Relative errors in the LT and SGR model for interpolation data (18 and 27 o C) was 100%, which show acceptable goodness of fit and validated for interpolation. The primary and secondary models developed in this study can be used to establish tertiary models to quantify the effects of temperature on the growth of E. coli O157:H7 in paprika.

Development and validation of a predictive model for pathogenic Escherichia coli in fresh‐cut produce

“…These results demonstrated a pattern similar to the growth of E. coli O157:H7 in paprika by Yun et al. ( 2013 ), where at 10°C, bacteria were maintained at the inoculation levels and subsequently killed, and at 12°C, the number of bacteria gradually increased after 50 h. Other studies showed that E. coli O157:H7 started to grow in romaine lettuce at temperatures above 15°C (Khalil & Frank, 2010 ), and that LPD was observed from 10.5°C in meat (Tamplin et al., 2005 ). The results of the study above suggest that minimum temperatures for the growth of pathogenic E .…”

“…Ingesting food contaminated with pathogenic E . coli can cause food poisoning, which, in severe cases, can result in death (Hwang et al., 2019 ; Yun et al., 2013 ).…”

Predictive model of Staphylococcus aureus growth on egg products

“…A f was 1.1-1.2, which was close to 1.3, and B f was 0.8-1.1, showing a value between 0.7 and 1.5, indicating a proper statistical fit. Similar results were obtained when compared to other studies (Cho et al, 2011b) on the growth prediction model of L. monocytogenes in smoked salmon (A f : 1.03-1.58, B f : 1.01-1.55), a study (Kang et al, 2010) on the growth prediction of S. aureus and Bacillus cereus in RTE foods (A f : 1.04-1.37, Bf: 0.90-1.11), the growth prediction model (Park et al, 2009) of S. aureus in green-bean sprouts provided in school meals (A f : 1.10-1.31, Bf: 0.97-1.03, MSE: 0.002-0.02), and the suitability verification value (Yun et al, 2013) of the growth prediction model for pathogenic E. coli in paprika (A f : 1.04-1.18, B f : 0.98-1.00, MRE: -1.03, -0.04). RMSE for the growth prediction model for grilled eggs, peeled quail eggs, and the whole egg liquid was 0.16, 0.26, and 0.27, respectively, showing a value that is closer to 0 than 0.300-0.5344, the RSME value measured in the study (Kim et al, 2013) on the prediction of growth of S. aureus in cheese.…”