Search

The aim of this study was to develop primary and secondary models to describe the growth kinetics of Staphylococcus aureus in glutinous rice dough in order to provide a basis for monitoring the growth of S. aureus in raw materials for glutinous rice products. Glutinous rice dough was inoculated with a mixture of two S. aureus strains (ATCC 8095 and NCTC 8325) and cultured at 4, 11, 18, 25, 32 or 37 ℃. The results showed that the growth data of S. aureus in glutinous rice dough at all temperatures except 4 ℃, at which S. aureus grew slowly, could be fitted to three primary growth models (the Baranyi, modified Gompertz and Huang models). However, the modified Gompertz model most accurately fitted the growth data of S. aureus at lower and higher temperatures (11 and 37 ℃). The Akaike information criterion (AIC) and the coefficient of determination (R2) showed that the modified Gompertz model could be used as the optimal primary model for the prediction of S. aureus growth. Thereafter, the effect of temperature on the growth rate of S. aureus was analyzed using the Ratkowsky (RSR) and Huang square-root (HSR) models. The results showed that the RSR model provided more accurate predictions of the lowest (0.049 ℃) and highest (47.135 ℃) growth temperatures than did the HSR model, suggesting the RSR model was a more suitable secondary growth model for S. aureus in glutinous rice dough. The results of this study can provide a scientific basis for food companies and regulatory agencies to predict the growth of S. aureus in glutinous rice products for food safety risk assessment.

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dried jujube is a characteristic fruit of Xinjiang. Aspergillus is one of the main pathogens that causes mold on dried jujube, and A. flavus is a toxin-producing species, the aflatoxin produced by A. flavus is extremely toxic and carcinogenic. In this study, the growth kinetic models of A. flavus isolated from red jujube at different temperatures and times were fitted to Huang model and linear equation respectively, the Cardinal model was used to describe the growth rate and lag time of A. flavus on dried jujube agar. It turned out that 30–35 °C was the optimal temperature for growing A. flavus, so dried jujube should avoid storing in this temperature range. The kinetic model established in this study will help to understand the growth characteristics of A. flavus, and lay a foundation for evaluating the quality of stored dried jujube and predictions of shelf life, which are conducive to optimizing storage methods for dried jujube. It can be judged according to the value of Af and Bf, the Huang model had a better fitting effect than the Baranyi model, The two models all had the highest growth rate at 35 °C, and A. flavus grew more vigorously and the lag period shortened as the temperature was increased. The secondary Cardinal model had a good fitting effect on the growth rate and lag time, and the secondary Ratkowsky model had a good fitting effect on the growth rate. This study may have theoretical and application value to strengthen the safety of jujube storage in the future.

This study examined the effect of layer thickness and temperature on the evolution of moisture during the isothermal dehydration of glutinous rice. To analyse the moisture evolution, empirical models and secondary models (SM) were employed. Global sensitivity analysis (GSA) was used to estimate the importance of the experimental parameters on the developed SM. According to the dehydration curves, the increase in temperature and reduction in the layer thickness significantly shortened (P < 0.05) the dehydration time of glutinous rice. The effective diffusivity, activation energy, and Gibb-free energy were 2.0 × 10 − 12 m 2 / s to 2.53 × 10 − 11 m 2 / s , 43.03 kJ mol − 1 to 51.05 kJ mol − 1 , and 145.10 kJ mol − 1 to 155.85 kJ mol − 1 respectively. The Page and Logarithmic models are suitably considered in evaluating the dehydration kinetics of the glutinous rice with a high coefficient of determination (R 2) values of >0.9989. The SM aptly described moisture evolution with higher accuracy of R 2 = 0.9843. The GSA shows that the variation in the SM output is primarily driven by the variation in the input parameters with low reliance on its interaction with other parameters, indicating that the SM can play a significant role in the optimization of the industrial dehydration process with a wide range of applications in the agricultural production system. • Development of a single exponent function for describing the moisture evolution. • Estimation of dehydration conditions' importance to moisture evolution using GSA. • Secondary model overcame the limitations of the empirical models. • Secondary model output is controlled by the primary effect of the input parameters. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Building secondary models that describe the growth rate as a function of multiple environmental conditions is often very labour intensive and costly. As such, the current research aims to assist in decreasing the required experimental effort by studying the efficacy of both design of experiments (DOE) and optimal experimental designs (OED) techniques. This is the first research in predictive microbiology (i) to make a comparison of these techniques based on the (relative) model prediction uncertainty of the obtained models and (ii) to compare OED criteria for the design of experiments with static (instead of dynamic) environmental conditions. A comparison of the DOE techniques demonstrated that the inscribed central composite design and full factorial design were most suitable. Five conventional and two tailor made OED criteria were tested. The commonly used D-criterion performed best out of the conventional designs and almost equally well as the best of the dedicated criteria. Moreover, the modelling results of the D-criterion were less dependent on the experimental variability and differences in the microbial response than the two selected DOE techniques. Finally, it was proven that solving the optimisation of the D-criterion can be made more efficient by considering the sensitivities of the growth rate relative to its value as Jacobian matrix instead of the sensitivities of the cell density measurements. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When building models to describe the effect of environmental conditions on the microbial growth rate, parameter estimations can be performed either with a one-step method , i.e., directly on the cell density measurements, or in a two-step method , i.e., via the estimated growth rates. The two-step method is often preferred due to its simplicity. The current research demonstrates that the two-step method is, however, only valid if the correct data transformation is applied and a strict experimental protocol is followed for all experiments. Based on a simulation study and a mathematical derivation, it was demonstrated that the logarithm of the growth rate should be used as a variance stabilizing transformation. Moreover, the one-step method leads to a more accurate estimation of the model parameters and a better approximation of the confidence intervals on the estimated parameters. Therefore, the one-step method is preferred and the two-step method should be avoided. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the growth of Staphylococcus aureus in traditional Chinese flour products under isothermal (10, 15, 20, 25, 30, and 37°C) and nonisothermal (10 to 20, 20 to 30, and 25 to 37°C) conditions. Then, models for the growth of S. aureus in flour products as a function of storage temperature, pH, and water activity (aw) were developed, and the goodness of fit of models was evaluated using the determination coefficient (R2), root mean square error (RMSE), bias factor (Bf), and accuracy factor (Af). Based on the above information, S. aureus growth in steamed bread under nonisothermal conditions was predicted from experiments performed under isothermal conditions. It was shown that different combinations of temperature and aw in flour products have a strong influence on the growth of S. aureus. The modified Gompertz model was found to be more suitable for describing the growth data of S. aureus in flour products, with an R2 of >0.99 and an RMSE of <0.37. The newly developed secondary models were validated, and for the specific growth rate and the lag time, the R2 values were 0.96 and 0.97, Af was 1.12 and 1.06, and Bf was 1.13 and 1.05, respectively. The predicted nonisothermal growth curves of S. aureus were in agreement with the reported experimental ones, with RMSE <0.29, Af value 1.02 to 1.09, and Bf value 0.92 to 0.99. These results indicated that the predictive models provided useful information for the establishment of safety standards and a risk assessment for S. aureus in flour products. [ABSTRACT FROM AUTHOR]

Salmonella continues to be a problem associated with low-moisture foods, particularly given enhanced thermal resistance at lower water activity (aw). However, there is a scarcity of thermal inactivation models accounting for the effect of aw. The objective of this study was to test multiple secondary models for the effect of product (wheat flour) aw on Salmonella enterica Enteritidis phage type 30 thermal resistance. A full-factorial experimental design included three temperatures (75, 80, and 85°C) and four aw values (~0.30, 0.45, 0.60, and 0.70). Prior to isothermal treatment, sample aw was achieved by equilibrating samples in a humidity-controlled conditioning chamber. Two primary models (log linear and Weibull type) and three secondary models (second-order response surface, modified Bigelow type, and combined effects) were evaluated using the corrected Akaike information criterion and root mean squared errors. Statistical analyses of the primary models favored the log-linear model. Incorporating the three secondary models into the log-linear primary model yielded root mean squared errors of 2.1, 0.78, and 0.96 log CFU/g and corrected Akaike information criterion values of 460, -145, and --19 for the response surface, modified Bigelow, and combined-effects models, respectively. The modified Bigelow-type model, which exponentially scaled both temperature and aw effects on thermal inactivation rates, predicted Salmonella lethality significantly better (P < 0.05) than did the other secondary models examined. Overall, aw is a critical factor affecting thermal inactivation of Salmonella in low-moisture products and should be appropriately included in thermal inactivation models for these types of systems. [ABSTRACT FROM AUTHOR]

In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D * values (time needed for one log reduction at a temperature of 95 °C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The z T (temperature change needed to cause one log reduction in D -values) estimated by the meta-analysis model were compared to those (‘observed’ z T values) reported in the primary studies, and in all cases they were within the confidence intervals of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes. [ABSTRACT FROM AUTHOR]

Abstract: Information is limited about the growth and survival of naturally-occurring Vibrio parahaemolyticus in live oysters under commercially relevant storage conditions harvested from different regions and in different oyster species. This study produced a predictive model for the growth of naturally-occurring V. parahaemolyticus in live Eastern oysters (Crassostrea virginica) harvested from the Chesapeake Bay, MD, USA and stored at 5–30°C until oysters gapped. The model was validated with model-independent data collected from Eastern oysters harvested from the Chesapeake Bay and Mobile Bay, AL, USA and Asian (C. ariakensis) oysters from the Chesapeake Bay, VA, USA. The effect of harvest season, region and water condition on growth rate (GR) was also tested. At each time interval, two samples consisting of six oysters each were analyzed by a direct-plating method for total V. parahaemolyticus. The Baranyi D-model was fitted to the total V. parahaemolyticus growth and survival data. A secondary model was produced using the square root model. V. parahaemolyticus slowly inactivated at 5 and 10°C with average rates of −0.002 and −0.001logcfu/h, respectively. The average GRs at 15, 20, 25, and 30°C were 0.038, 0.082, 0.228, and 0.219logcfu/h, respectively. The bias and accuracy factors of the secondary model for model-independent data were 1.36 and 1.46 for Eastern oysters from Mobile Bay and the Chesapeake Bay, respectively. V. parahaemolyticus GRs were markedly lower in Asian oysters. Harvest temperature, salinity, region and season had no effect on GRs. The observed GRs were less than those predicted by the U.S. Food and Drug Administration''s V. parahaemolyticus quantitative risk assessment. [Copyright &y& Elsevier]

Abstract: Previous conclusions that a 1-step fitting method gives more precise coefficients than the traditional 2-step method are confirmed by application to three different data sets. It is also shown that, in comparison to 2-step fits, the 1-step method gives better fits to the data (often substantially) with directly interpretable regression diagnostics and standard errors. The improvement is greatest at extremes of environmental conditions and it is shown that 1-step fits can indicate inappropriate functional forms when 2-step fits do not. 1-step fits are better at estimating primary parameters (e.g. lag, growth rate) as well as concentrations, and are much more data efficient, allowing the construction of more robust models on smaller data sets. The 1-step method can be straightforwardly applied to any data set for which the 2-step method can be used and additionally to some data sets where the 2-step method fails. A 2-step approach is appropriate for visual assessment in the early stages of model development, and may be a convenient way to generate starting values for a 1-step fit, but the 1-step approach should be used for any quantitative assessment. [Copyright &y& Elsevier]

Abstract: Secondary models, describing the microbial growth rate as a function of temperature, are evaluated with focus on model performance in the suboptimal temperature region. Escherichia coli K12 MG1655 is considered as the case study. A large set of (T)-estimates is fitted with (1) the cardinal temperature model with inflection (CTMI, Rosso et al., 1993), (2) the square root model (SQRT, Ratkowsky et al., 1983), and (3) the CTMI adapted to describe the particular behavior of Listeria at suboptimal temperatures (aCTMI, Le Marc et al., 2002). Compared to the CTMI and the SQRT, a more accurate description of the (T)-relation is obtained with the aCTMI, certainly at temperatures below 30°C. Also, the T min estimate is more realistic, i.e., ≈6°C, compared to 8–8.5°C for the CTMI and SQRT. Use of the aCTMI improved (T)-data description which points at the existence of two phases in the suboptimal temperature region of E. coli K12. The alternation of the (T) is most likely related to the cold shock response. These results reveal a possible shortcoming of the model structure of commonly used secondary models describing the temperature effect on the microbial growth rate. [Copyright &y& Elsevier]

This study was conducted to develop predictive models for the growth of Staphylococcus aureus in kimbab as a function of storage temperatures (7, 10, 12, 14, 16, 20, 25, and 30°C). The growth data were fitted into the modified Gompertz model and the Logistic model, and the goodness-of-fit of primary models was compared using determination of coefficient, mean square error, and Akaike's information criterion. The modified Gompertz model was found to be more suitable to describe the growth data. Therefore, the growth rate (GR) and lag time (LT) obtained from the modified Gompertz model were employed to establish the secondary models. The newly developed models were validated using root mean square error (RMSE), bias factor (B), and accuracy factor (A). The results showed that RMSE<0.20 and B and A values were within the reliable range, which indicated that the presented predictive models can be used to assess the risk of S. aureus infection in kimbab. [ABSTRACT FROM AUTHOR]

The growth of Staphylococcus aureus in sandwich fillings at different incubation temperatures was tested. These growth data were fitted into the Gompertz model, Logistic model, and Baranyi model in order to compare the goodness-of-fit of the 3 primary models using several factors such as coefficient of determination (R2), the standard deviation (Sy.x), and the Akaike’s information criterion (AIC). The Gompertz model showed the best statistical fit. Hence, growth parameters such as specific growth rate (SGR) and lag time (LT) obtained from the Gompertz model were used to construct the secondary models. Further, developed models were evaluated by bias factor (Bf) and accuracy factor (Af). For the SGR, the Bf value was 0.993 and Af value was 1.156 which indicated conservative predictions. While for LT, a clear deviation was observed between predictions and observations (Bf=0.635 and Af=1.592). The results, however, were also considered acceptable after comparing with previous publications. [ABSTRACT FROM AUTHOR]

Predictive microbiology has been developed utilizing accurate and versatile mathematical models to predict microbial behavior during food processing instead of using traditional microbiological methods. Generally, mathematical models are classified into three types: primary, secondary, and tertiary models. Secondary model deals with the response of parameters appearing in primary modeling approaches as functions of one or more environmental conditions like temperature, pH, etc. Most of them have little or no microbiological basis, which makes interpretation of some model parameters difficult and sometimes their performance is not stable. This paper reviews the performance and development of secondary models and discusses their strengths and weaknesses, which may help future model development and application. [ABSTRACT FROM AUTHOR]

Abstract: Several factorial models extending the famous Bigelow model to describe the influence of the heating and recovery pH and a w conditions on bacterial heat resistance have been developed. These models can be associated in an overall multifactorial model describing the influences of heating and recovery conditions on D values. For Bacillus cereus strain ADQP 407 the model parameters characterising the environmental factor influences (pH, Temperature, a w) were evaluated. Determination of bacterial heat resistance in cream chocolate have been realised to validate these parameter values and to evaluate the level of the influence of food texture or different compounds not taken account of in the model. [Copyright &y& Elsevier]

• Cardinal values can be estimated by meta-analysing literature growth data. • The meta-regression models built provide an insight on sources of variability. • The cardinal values obtained can be used as reference values for risk assessments. Since Listeria monocytogenes has a high case-fatality rate, substantial research has been devoted to estimate its growth rate under different conditions of temperature, pH and water activity (a w). In this study, published findings on L. monocytogenes growth in broth were extracted and unified by constructing meta-regression models based on cardinal models for (i) temperature (CM[T]), (ii) temperature and pH (CM[T][pH]), and (iii) temperature, pH and a w (CM[T][pH][a w ]). After assessing all the sources retrieved between 1988 until 2017, forty-nine primary studies were considered appropriate for inclusion. Apart from the modelling variables, study characteristics such as: type of broth (BHI, TSB, TPB), reading method (colony-forming-units, CFU; or binary-dilution optical density methods, OD), inoculum concentration and strain serotype, were also extracted. Meta-regressions based on CM[T] and CM[T][pH] were fitted on subsets of the 2009 growth rate measures and revealed that type of broth and reading method significantly modulated the cardinal parameter estimates. In the most parsimonious CM[T][pH][a w ] meta-regression model, whereby the variability due to type of broth was extracted in a nested random-effects structure, the optimum growth rate μ opt of L. monocytogenes was found to be lower when measured as CFU (0.947 log CFU/h; SE = 0.094 log CFU/h) than when measured as OD (1.289 log CFU/h; SE = 0.092 log CFU/h). Such a model produced the following cardinal estimates: T min = -1.273 °C (SE = 0.179 °C), T opt = 37.26 °C (SE = 0.688 °C), T max = 45.12 °C (SE = 0.013 °C), pH min = 4.303 (SE = 0.014), pH opt = 7.085 (SE = 0.080), pH max = 9.483 (SE = 0.080), a w min = 0.894 (SE = 0.002) and a w opt = 0.995 (SE = 0.001). Integrating the outcomes from numerous L. monocytogenes growth experiments, this meta-analysis has estimated pooled cardinal parameters that can be used as reference values in quantitative risk assessment studies. [ABSTRACT FROM AUTHOR]