Comparison of Growth Kinetics of Various Pathogenic E. coli on Fresh Perilla Leaf

Kim, Juhui; Ro, EunYoung; Yoon, Ki Sun

doi:10.3390/foods2030364

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Additional information on the proximate composition of the vegetables and fruits under investigation are shown in Table 2 . These values are comparable to those reported by Kim et al, 34 for perilla leaves inoculated with E. coli O157:H7 at 36 °C (LPD = 2.40 ± 0.32 h; EGR = 0.55 ± 0.11 CFU/mL/h). The growth of the E. coli O157:H7 strain on the epicarp of the vegetables and fruits was slower than that observed under controlled laboratory conditions.…”

Section: Resultssupporting

confidence: 90%

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits

Klanian

Sánchez-Solís

2018

Brazilian Journal of Microbiology

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Despite the increasing reports on the incidence of fresh vegetables and fruits as a possible vehicle for human pathogens, there is currently limited knowledge on the growth potential of Escherichia coli O157:H7 on different plant substrates. This study analyzed the selective adhesion and growth of E. coli O157:H7 on chili habanero (Capsicum chinense L.), cucumber (Cucumis sativus), radish (Raphanus sativus), tomato (Lycopersicon esculentum), beet (Beta vulgaris subsp. vulgaris), and onion (Allium cepa L.) under laboratory conditions. The Gompertz parameters were used to determine the growth kinetics. Scanning electron microscopy was used to visualize the adhesion of E. coli O157:H7 on the epicarp of the samples. Predictive models were constructed to compare the growth of E. coli O157:H7 on the samples with different intrinsic factors and to demonstrate the low selectivity of the pathogen. No significant difference was observed in the lag-phase duration (LPD), generation time (GT), and exponential growth rate (EGR) of the pathogen adhered to the samples. The interaction between the microorganism and the substrate was less supportive to the growth of E. coli O157:H7 for onion, whereas for tomato and cucumber, the time for the microorganism to attain the maximum growth rate (M) was significantly longer than that recorded for other samples.

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Section: Resultssupporting

confidence: 90%

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits

Klanian

Sánchez-Solís

2018

Brazilian Journal of Microbiology

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“…The predicted SGR and LT values for generic E. coli in Garaetteok were determined to be 0.198 h -1 and 10.025 h at a storage temperature of 18°C (Table 3). A marked increase ([ 3-fold) and decrease ([ twofold) in the SGR and LT values, respectively, were observed for the E. coli in Garaetteok in this study compared with a study reported by Kim et al (2013), who used similar storage temperatures for predictions: the predicted SGR and LT values for E. coli O157:H7 were found to be 0.055 h -1 and 20.40 h on fresh Perilla leaf at a storage temperature of 17°C. This difference may be due to the different substrates and different strains of E. coli used in these studies.…”

Section: Predictive Model Development In Garaetteoksupporting

confidence: 67%

Predictive growth model of the effects of temperature on the growth kinetics of generic Escherichia coli in the Korean traditional rice cake product “Garaetteok”

Park

2017

J Food Sci Technol

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In this study, a predictive growth model of generic in at a range of storage temperatures (T, 10-40 °C) was developed. The primary models of specific growth rate (SGR) and lag time (LT) fit well (R ≥ 0.985) using a Gompertz equation. Secondary polynomial models were obtained by non-linear regression and calculated as SGR = - 0.01,570 + 0.0183T + 0.000008T; LT = 43.2064 - 2.4824T + 0.0355T. The appropriateness of the secondary models was verified by mean square error (MSE; 0.0006 for SGR, 0.282 for LT), bias factor (B ; 0.948 for SGR, 0.942 for LT), accuracy factor (A ; 1.163 for SGR, 1.355 for LT), and coefficient of determination (r; 0.986 for SGR, 0.996 for LT), and these models were found to be in good agreement with the experimental values used for validation. The secondary models developed in this study may thus be used as practical prediction models for generic growth in. These newly developed secondary models of SGR and LT for generic in may thus be incorporated into tertiary modeling programs such as the Korea Pathogen Modeling Program, in which they can easily be used to predict the growth kinetics of as a function of storage temperature. Ultimately, model developed in this study may be a vital tool for the reduction of levels in food production, processing, and distribution processes, which in turn will lead to enhanced safety of rice products.

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“…Differences in growth kinetics have been reported among strains of foodborne pathogens such as E . coli [ 51 ]. Similarly, the differences in the growth rate obtained in the present work and those reported by Young et al [ 25 ] may be attributed to strain biological variability.…”

Section: Discussionmentioning

confidence: 99%

A model for predicting Xanthomonas arboricola pv. pruni growth as a function of temperature

et al. 2017

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A two-step modeling approach was used for predicting the effect of temperature on the growth of Xanthomonas arboricola pv. pruni, causal agent of bacterial spot disease of stone fruit. The in vitro growth of seven strains was monitored at temperatures from 5 to 35°C with a Bioscreen C system, and a calibrating equation was generated for converting optical densities to viable counts. In primary modeling, Baranyi, Buchanan, and modified Gompertz equations were fitted to viable count growth curves over the entire temperature range. The modified Gompertz model showed the best fit to the data, and it was selected to estimate the bacterial growth parameters at each temperature. Secondary modeling of maximum specific growth rate as a function of temperature was performed by using the Ratkowsky model and its variations. The modified Ratkowsky model showed the best goodness of fit to maximum specific growth rate estimates, and it was validated successfully for the seven strains at four additional temperatures. The model generated in this work will be used for predicting temperature-based Xanthomonas arboricola pv. pruni growth rate and derived potential daily doublings, and included as the inoculum potential component of a bacterial spot of stone fruit disease forecaster.

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Comparison of Growth Kinetics of Various Pathogenic E. coli on Fresh Perilla Leaf

Cited by 9 publications

References 24 publications

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits

Predictive growth model of the effects of temperature on the growth kinetics of generic Escherichia coli in the Korean traditional rice cake product “Garaetteok”

A model for predicting Xanthomonas arboricola pv. pruni growth as a function of temperature

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