• Biotechnology and Bioprocess Engineering:BBE
• /
• v.5 no.5
• /
• pp.382-385
• /
• 2000

A mathematical model was developed, based on the time dependent changes of the specific growth rate, for prediction of the typical microbial cell growth in batch cultures. This model could predict both the lag growth phase and the stationary growth phase of batch cultures, and it was tested with the batch growth of Trichoderma reesei and Lactobacillus delbrueckii.

• Preventive Nutrition and Food Science
• /
• v.16 no.4
• /
• pp.364-369
• /
• 2011

Levels of carbon dioxide gas, a metabolite of microbial growth, have been reported to parallel the onset of microbial spoilage and may be used as a convenient index for a packaged food's shelf life. This study aimed to establish a kinetic model of $CO_2$ production from perishable food for the potential use for shelf life control in the food supply chain. Aerobic bacterial count and package $CO_2$ concentration were measured during the storage of seasoned pork meat at four temperatures (0, 5, 10 and $15^{\circ}C$), and their interrelationship was investigated to establish a mathematical model. The microbial growth at constant temperature was described by using model of Baranyi and Roberts. $CO_2$ production from the stored food could be explained by taking care of its yield and maintenance factors linked to the microbial growth. By establishing the temperature dependence of the microbial growth and $CO_2$ yield factor, $CO_2$ partial pressure or concentration in package headspace could be estimated to a limited extent, which is helpful for controlling the shelf life under constant and dynamic temperature conditions. Application and efficacy of the model needs to be improved with further refinement in the model.

• Food Science and Biotechnology
• /
• v.27 no.6
• /
• pp.1865-1869
• /
• 2018

This study examined the effects of alfalfa seed germination on growth of Salmonella enterica. We investigated the population changes of S. enterica during early sprout development. We found that the population density of S. enterica, which was inoculated on alfalfa seeds was increased during sprout development under all experimental temperatures, whereas a significant reduction was observed when S. enterica was inoculated on fully germinated sprouts. To establish a model for predicting S. enterica growth during alfalfa sprout development, the kinetic growth data under isothermal conditions were collected and evaluated based on Baranyi model as a primary model for growth data. To elucidate the influence of temperature on S. enterica growth rates, three secondary models were compared and found that the Arrhenius-type model was more suitable than others. We believe that our model can be utilized to predict S. enterica behavior in alfalfa sprout and to conduct microbial risk assessments.

• Food Science and Biotechnology
• /
• v.17 no.6
• /
• pp.1352-1356
• /
• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Preventive Nutrition and Food Science
• /
• v.13 no.2
• /
• pp.104-111
• /
• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• Food Science and Preservation
• /
• v.24 no.6
• /
• pp.778-785
• /
• 2017

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and $30^{\circ}C$). Baranyi model was used as a primary order model for growth data. The maximum growth rate (${\mu}max$) and lag-phase duration (LPD) for each temperature (except for $10^{\circ}C$ LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.

• The Korean Journal of Mycology
• /
• v.49 no.4
• /
• pp.441-454
• /
• 2021

Fungal diseases including anthracnose, stem rot, blight, wilting, and root rot of crops are caused by phytopathogens such as Colletotrichum species, Sclerotinia sclerotiorum, Phytophthora species, and Fusarium oxysporum and F. solani which threaten the production of chili pepper. In this study, to identify biological control agents (BCAs) of phytopathogenic fungi, potentially useful Bacillus species were isolated from the field soils. We screened out five Bacillus strains with antagonistic capacity that are efficiently inhibiting the growth of phytopathogenic fungi. Bacillus species were characterized by the production of extracellular enzymes, siderophores, and indole-3-acetic acid (IAA). Furthermore, the influence of bacterial strains on the plant growth promoting activity and seedling vigor index were assessed using Brassica juncea as a model plant. Inoculation with Bacillus subtilis SRCM 121379 significantly increased the length of B. juncea shoots and roots by 45.6% and 52.0%, respectively. Among the bacterial isolates, Bacillus subtilis SRCM 121379 showed the superior enzyme activities, antagonistic capacity and plant growth promoting effects. Based on the experimental results, Bacillus subtilis SRCM 121379 (GenBank accession no. NR027552) was finally selected as a BCA candidate.

• Preventive Nutrition and Food Science
• /
• v.13 no.4
• /
• pp.348-353
• /
• 2008

Aerobic bacterial growth on Korean pan.fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real.time basis under dynamic storage conditions. Bacteria counts in the stretch.wrapped foods held at constant temperatures of 0, 5, 10 and $15^{\circ}C$ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

• Journal of Food Hygiene and Safety
• /
• v.28 no.2
• /
• pp.168-173
• /
• 2013

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 $36^{\circ}C$. 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 $12^{\circ}C$. 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 $27^{\circ}C$) 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.

• Food Science and Biotechnology
• /
• v.15 no.5
• /
• pp.773-777
• /
• 2006

Growth of Bacillus cereus was assessed during the storage of seasoned soybean sprouts at 0,5, 10, and $15^{\circ}C$. No lag time in its growth curve was observed and thus the specific growth rate of B. cereus in the exponential growth phase was estimated for bootstrapped microbial count data. The distribution of the specific growth rate could be explained by the BetaGeneral distribution function, and temperature dependence was described by the Ratkowsky square root model. The temperature dependence of the growth could be successfully incorporated into the differential equation of microbial growth to predict the B. cereus count on the seasoned soybean sprouts under fluctuating temperature conditions. Safe shelf lives with different probabilities to reach $10^5\;CFU/g$ were presented at four different temperatures, considering the variation in initial contamination and specific growth rate by the Monte Carlo method and 2-step bootstrapping, respectively. Safe shelf lives defined as the time with a probability of less than 0.1% of reaching the critical limit, were 13.4, 5.2, 3.6, and 2.8 days at 0, 5, 10, and $15^{\circ}C$, respectively.