Temperature/growth relationships for psychrotrophic food-spoilage bacteria

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

Predictive modeling and sensitivity analysis to estimate the experimental data of inulinase fermentation by Aspergillus niger grown on sugar beet molasses-based medium optimized using Plackett-Burman Design

The present work aimed to model Aspergillus niger inulinase fermentation performed in the medium using sigmoidal functions, validate the selected models using an independent set of the experimental values, and perform a sensitivity analysis of the selected models. Based on the results, the selected models were Stannard and Fitzhugh models for substrate consumption (R²  = 0.9976 and 0.9974, respectively), Huang model for inulinase production (R²  = 0.9967), Weibull model for invertase-type production (R²  = 0.9963), and modified logistic model for invertase-type activity/inulinase activity ratio (R²  = 0.9292) with high R² values (>0.90). Kinetics predicted by particularly selected models mentioned above fit well with the experimental kinetic results. Besides, validation of the selected models with an independent set of the experimental data indicated that they gave satisfying results with high R² values for consumption and production (R²  > 0.90). Sensitivity analysis of the selected models showed that the yielded R² values (R²  ≥ 0.9775) were in good agreement with those obtained from the selected models. Consequently, A. niger inulinase fermentation was successfully modeled and the selected models were successfully validated with an independent set of the observed data. Besides, the sensitivity analysis also verified the reliability of the selected models. Those models can serve as universal equations to describe the A. niger inulinase fermentation.

Hospital preparedness during epidemics using simulation: the case of COVID-19

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

Influence of Dietary Polar Lipid Supplementation on Memory and Longitudinal Brain Development

Polar lipids, which are found in human milk, serve essential functions within biological membranes, hence their importance in brain development and cognition. Therefore, we aimed to evaluate the longitudinal effects on brain macrostructural and microstructural development and recognition memory of early-life polar lipid supplementation using the translational pig model. Twenty-eight intact (i.e., not castrated) male pigs were provided either a control diet (n = 14) or the control diet supplemented with polar lipids (n = 14) from postnatal day 2 until postnatal week 4. After postnatal week 4, all animals were provided the same nutritionally-adequate diets until postnatal week 24. Pigs underwent magnetic resonance imaging at 8 longitudinal time-points to model brain macrostructural and microstructural developmental trajectories. The novel object recognition task was implemented at postnatal weeks 4 and 8 to evaluate recognition memory. Subtle differences were observed between groups in hippocampal absolute brain volumes and fractional anisotropy, and no differences in myelin water fraction developmental patterns were noted. Behavioral outcomes did not differ in recognition memory, and only minimal differences were observed in exploratory behaviors. Our findings suggest that early-life dietary supplementation of polar lipids has limited effect on brain developmental patterns, object recognition memory, and exploratory behaviors.

Influence of Rearing Environment on Longitudinal Brain Development, Object Recognition Memory, and Exploratory Behaviors in the Domestic Pig (Sus scrofa)

Introduction

Over the last 40 years, the domestic pig has emerged as a prominent preclinical model as this species shares similarities with humans with regard to immunity, gastrointestinal physiology, and neurodevelopment. Artificial rearing of pigs provides a number of advantages over conventional rearing (i.e., true maternal care), including careful control of nutrient intake and environment conditions. Yet there remains a gap in knowledge when comparing brain development between sow-reared and artificially reared domestic pigs. Thus, our research sought to model brain development and assess recognition memory in a longitudinal manner by directly comparing rearing environments.

Methods

Forty-four intact (i.e., not castrated) male pigs were artificially reared or sow-reared from postnatal day 2 until postnatal week 4. After postnatal week 4, all pigs were housed in a group setting within the same environment until postnatal week 24. Magnetic resonance imaging was conducted on pigs at 8 longitudinal time-points to model developmental trajectories of brain macrostructural and microstructural outcomes. Additionally, pigs behavior were tested using the novel object recognition task at postnatal weeks 4 and 8.

Results

Throughout the 24-week study, no differences between rearing groups were noted in weekly body weights, average growth and feed intake patterns, or feed efficiency. Whole brain, gray matter, white matter, and cerebrospinal fluid growth patterns also did not differ between pigs assigned to different early-life rearing environments. Moreover, minimal differences in regional absolute volumes and fractional anisotropy developmental trajectories were identified, though artificially reared pigs exhibited higher initial rates of myelination in multiple brain regions compared with sow-reared pigs. Furthermore, behavioral assessment at both PNW 4 and 8 suggested little influence of rearing environment on recognition memory, however, an age-dependent increase in object recognition memory was observed in the sow-reared group.

Conclusion

Our findings suggest that early-life rearing environment influences the rate of development in some brain regions but has little influence on overall brain growth and object recognition memory and exploratory behaviors in the domestic pig. Artificial rearing may promote maturation in certain brain areas but does not appear to elicit long-term effects in outcomes including brain structure or object recognition memory.

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

Effect of Salicylic Acid on the Growth and Chemical Responses of Pectobacterium carotovorum subsp. carotovorum

BACKGROUND AND OBJECTIVE

Salicylic acid is a signal molecule which activates plant defense against plant pathogens such as the soft rot enterobacterium Pectobacterium carotovorum subsp. carotovorum. The objectives of study were to determine bactericidal effects of salicylic acid on the growth of P. carotovorum subsp. carotovorum and secondly, assess chemical responses of P. carotovorum subsp. carotovorum to salicylic acid.

MATERIALS AND METHODS

Pectobacterium carotovorum subsp. carotovorum was grown in lysogeny broth amended with salicylic acid at concentrations of 0, 100, 200, 400, 800 and 1200 mg L-1. The P. carotovorum subsp. carotovorum cultures were incubated at 25°C and sampled at two time points, 0 h (sampled before incubation) and 24 h. Bacterial counts were done at the onset of the incubation (0 h) and after the 24 h incubation. The set which was incubated for 24 h was split into two, one subset was centrifuged and the other was not. From the centrifuged subset the supernatant was recovered and was, together with all the other samples (0 and 24 h not centrifuged), analyzed with1H nuclear magnetic resonance and gas chromatography.

RESULTS

Bacterial counts done before and after incubation showed that the lower concentrations of salicylic acid, 0, 100, 200 and 400 mg L-1, supported the growth of P. carotovorum subsp. carotovorum whereas the higher concentrations of 800 and 1200 mg L-1 inhibited the growth of the bacterium completely. Nuclear magnetic resonance results showed either slight or no differences in the metabolite profiles and gas chromatography showed different responses without a clearly defined pattern among the experimental treatments. However, methanethiol was detected by both nuclear magnetic resonance and gas chromatography in all the treatments and was probably formed as a result of the breakdown of lysogeny broth.

CONCLUSION

From the results obtained it was concluded that salicylic acid promotes the growth of P. carotovorum subsp. carotovorum at lower concentrations of 0-400 mg L-1 but higher concentrations of salicylic acid of 800 and 1200 mg L-1 inhibit bacterial growth. All the tested salicylic acid concentrations (0-1200 mg L-1) cause only slight chemical shifts in the bacterial culture. Methanethiol was detected in all treatments and it is probably formed from the breakdown of lysogeny broth.

Kinetic analysis and mathematical modeling of growth parameters of Lactobacillus plantarum in protein-rich isolates from tomato seed

The aim of the present study was to evaluate the applicability of using protein-rich isolates from tomato seed as a sole source of nutrition for the growth of lactic acid bacteria. Unstructured mathematical and logistic models were proposed to describe growth, pH drop, lactic acid production and nutriment consumption by Lactobacillus plantarum in whole and defatted isolates in order to compare their suitability for the production of a fermented beverage. These media have considerable good quantities of nutriment that allowed the growth of L. plantarum, after which the cell numbers begin to decline. The maximum biomass was observed in defatted isolate (1.42 g L−1) followed by the whole isolate (1.24 g L−1). The lactic acid increased by about 5.5 and 6.5 times respectively in whole and defatted protein isolates. However, significant nutriment consumption occurred during the growth phase as well as stationary phase. A reduction of 61.90% and 95.88% in sugar content, as well as 21.91% and 16.93% reduction in protein content were observed respectively in whole and defatted isolates. In most cases, the proposed models adequately describe the biochemical changes taking place during fermentation and are a promising approach for the formulation of tomato seed-based functional foods.

Modeling healthy male white matter and myelin development: 3 through 60months of age

An emerging hypothesis in developmental and behavioral disorders is that they arise from disorganized brain messaging or reduced connectivity. Given the importance of myelin to efficient brain communication, characterization of myelin development in infancy and childhood may provide salient information related to early connectivity deficits. In this work, we investigate regional and whole brain growth trajectories of the myelin water fraction, a quantitative magnetic resonance imaging measure sensitive and specific to myelin content, in data acquired from 122 healthy male children from 3 to 60 months of age. We examine common growth functions to find the most representative model of myelin maturation and subsequently use the best of these models to develop a continuous population-averaged, four-dimensional model of normative myelination. Through comparisons with an independent sample of 63 male children across the same age span, we show that the developed model is representative of this population. This work contributes to understanding the trajectory of myelination in healthy infants and toddlers, furthering our knowledge of early brain development, and provides a model that may be useful for identifying developmental abnormalities.

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Proposes various growth models for modeling MWF trajectories

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Demonstrates modeling of MWF trajectories of 122 male infants under 5 years

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Statistically compares tested models using Bayesian information criterion

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Generates first four-dimensional model of whole-brain myelination of early childhood

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Discusses the utility of developed model

Impact of bottled water storage duration and location on bacteriological quality

An investigation studying the effects of storage duration and location on the persistence of heterotrophic microorganisms in oligotrophic bottled water environments has been completed. One-gallon high-density polyethylene water containers stored for up to 16 weeks at temperatures ranging from 2°C to >49°C in a refrigerator, indoor cabinet, covered porch, and car trunk were evaluated for microbiological quality. Heterotrophic plate counts (HPCs) of up to 4 × 10(3) cfu/mL were detected in containers stored on a porch and car trunk; whereas, HPCs were found not to exceed 400 cfu/mL and 100 cfu/mL for bottles stored in indoor cabinets and refrigerators, respectively. Containers stored on an enclosed porch for up to seven years contained HPC of up to 4 × 10(4) cfu/mL. Logistic and Gompertz growth models predicted microbial growth rates for bottled water stored on a protected porch environment for long (R(2) 0.99) and short-term (R(2) 0.86) durations.

Determination of the growth limits and kinetic behavior of Listeria monocytogenes in a sliced cooked cured meat product: validation of the predictive growth model under constant and dynamic temperature storage conditions

To describe the growth limits of Listeria monocytogenes NCTC10527 in a sliced vacuum-packaged cooked cured meat product, the binary logistic regression model was used to develop an equation to determine the probability of growth or no growth of L. monocytogenes as a function of temperature (from 0 to 10 degrees C) and water activity (from 0.88 to 0.98). Two inoculum concentrations were used (10 and 10(4) CFU g(-1)), and the growth limits for the two inocula were different. The kinetic behavior of L. monocytogenes as a function of temperature (4, 8, 12, and 16 degrees C) on the same meat product at the lower concentration (10 CFU g(-1)) was also studied. The Baranyi model appeared to fit the overall experimental data better than did the modified Gompertz and the modified logistic models. Maximum specific growth rate (micromax), lag phase duration (LPD), and maximum cell concentration (Nmax) derived from the primary model were modeled using the square root function (micromax and LPD) and a second order polynomial (Nmax) (secondary models). The selection of the best model (primary or secondary) was based on some statistical indices (the root mean square error of residuals of the model, the regression coefficient, the F test, the goodness of fit, and the bias and accuracy factor). The developed kinetic behavior model was validated under constant and dynamic temperature storage conditions. This prediction of L. monocytogenes growth provides useful information for improving meat safety and can be used for in-depth inspection of quality assurance systems in the meat industry.

Effects of substrate concentrations on the growth of heterotrophic bacteria and algae in secondary facultative ponds

This paper presents the effect of substrate concentration on the growth of a mixed culture of algae and heterotrophic bacteria in secondary facultative ponds (SFPs) utilizing settled domestic sewage as a sole source of organic carbon. The growth of the mixed culture was studied at the concentrations ranging between 200 and 800 mg COD/l in a series of batch chemostat reactors. From the laboratory data, the specific growth rate (micro) was determined using the modified Gompertz model. The maximum specific growth rate ( micro(max)) and half saturation coefficients (K(s)) were calculated using the Monod kinetic equation. The maximum observed growth rate ( micro(max)) for heterotrophic bacteria was 3.8 day(-1) with K(s) of 200 mg COD/l. The micro(max) for algal biomass based on suspended volatile solids was 2.7 day(-1) with K(s) of 110 mg COD/l. The micro(max) of algae based on the chlorophyll-a was 3.5 day(-1) at K(s) of 50mg COD/l. The observed specific substrate removal by heterotrophic bacteria varied between the concentrations of substrate used and the average value was 0.82 (mg COD/mg biomass). The specific substrate utilization rate in the bioreactors was direct proportional to the specific growth rate. Hence, the determined Monod kinetic parameters are useful for the definition of the operation of SFPs.

Sensitivity analysis of Salmonella enteritidis levels in contaminated shell eggs using a biphasic growth model

Salmonella enteritidis (SE) is a common foodbome pathogen, the transmission of which is primarily associated with the consumption of contaminated Grade A shell eggs. In order to estimate the level of SE present in raw shell eggs, it is necessary to consider the protective effects of the egg albumin, which effectively inhibits SE growth in a time- and temperature-dependent manner. In this study, a SE growth model was produced by combining two mathematical equations that described both the extended lag phase of SE growth (food component) and a SE growth model (pathogen component). This biphasic growth model was then applied to various egg handling scenarios based on the farm-to-table continuum, including in-line and off-line processing facilities with consideration of key events in production, processing, transportation, and storage. Seasonal effects were also studied. Monte Carlo simulation was used to characterize variability in temperature and time parameter values influencing the level of SE to which individuals are exposed. The total level of SE consumed was estimated under best, most likely, and time-temperature abusive handling scenarios. The model estimated that, in most cases, there was no SE growth in contaminated eggs handled under most likely practices, because 10-70% of the yolk membrane remained intact. Under abusive handling scenarios, complete loss of yolk membrane integrity frequently occurred by the time eggs reach the distribution phase, followed by subsequent SE growth, which was often quite rapid. In general, the effect of season and processing method (in-line vs. off-line) was minimal. Further sensitivity analysis demonstrated that the initial SE contamination level significantly influenced the final exposure levels only under no-abuse or mildly abusive conditions. The results of our study suggest that, for maximum reduction of SE exposure level, cooling strategies should not only focus on the on-farm or processing phases, but should emphasize the importance of cooling strategies at the distribution and consumer phases of the farm-to-fork continuum.

Comparison of simple neural networks and nonlinear regression models for descriptive modeling of Lactobacillus helveticus growth in pH-controlled batch cultures

A set of 20 Lactobacillus helveticus growth curves was obtained from pH-controlled batch cultures with different pH setpoints, whey permeate and yeast extract concentrations. To find the best descriptive model of the biomass concentration versus time (y = X(t)) growth curve, fitting results of a large number of models were compared with statistical and approximate methods. Models studied included simple neural networks, reparameterized Logistic, Gompertz, Richards, Schnute, Weibull, and Morgan-Mercier-Flodin models, Amrane-Prigent model, and four new models based on autonomous growth functions. Simple neural networks with only four weights were good descriptive models of the growth curves and fitting qualities were similar to those of the best existing four-parameter models, such as the Logistic model. However, meaningful parameters had to be calculated numerically and use of simple neural networks yielded no distinctive advantages over other models. A new five-parameter model, based on an autonomous growth function, yielded the best fitting results, even when the number of model parameters was accounted for in the comparisons. However, the maximum specific growth rate was not always well estimated. Therefore the five-parameter Richards model was chosen as the best descriptive model of the growth curve.

Degradation of pectic compounds during pasteurised vegetable juice spoilage by Chryseomonas luteola: a predictive microbiology approach

Predictive modelling consists in describing effects of environmental factors on microbial growth parameters. With food spoilage bacteria, this approach must be extended to both growth and food damage characterisation. In order to study the incidence of storage temperature on vegetable damage, using predictive microbiology tools, kinetics of pectic compound degradation were studied. Chryseomonas luteola has been chosen because of its ability to grow on post-harvested vegetables. Experiments were performed at refrigerated temperatures (0-10 degrees C) with low initial bacterial charges (10(1)-10(3.5) cfu/ml). Microbial specific growth rate (mu), stability phase before pectic degradation (Sp) and alteration percentage (Ap) were chosen as reference parameters. Then, sub-optimal temperature effects on these three parameters were estimated using modified Ratkowsky model. Results obtained in synthetic medium were compared with data observed in endive juice to appreciate the alteration of vegetable during post-harvest storage.

Validation of a model describing the effects of temperature and water activity on the growth of psychrotrophic pseudomonads

The reliability of the predictive model for the growth of psychrotrophic pseudomonads was evaluated both under controlled laboratory conditions and in industry conditions using various milks, milk-based products, meat and meat products. The validation process involved monitoring the growth of pseudomonads at various temperatures and comparing the observed generation times to those predicted by the model using bias and accuracy factors. For fluctuating temperatures bias and accuracy factors were used to compare the predicted and observed times to reach various population densities. The psychrotrophic pseudomonad model was shown to predict accurately the growth of pseudomonads in the products tested. In some instances knowledge of the lag phase duration was required to maximise the performance of the model.

Response Surface Models To Describe the Effects of Temperature, pH, and Ethanol Concentration on Growth Kinetics and Fermentation End Products of a Pectinatus sp

Growth curve data which had been fitted by use of the Gompertz and logistic functions have permitted the development of mathematical models to describe the growth of a Pectinatus sp. by several variables, namely, temperature, pH, and ethanol concentration. The activation energy of this microorganism was lower at 26 to 35(deg)C than at 15 to 22(deg)C. On the basis of the Arrhenius law, growth rate, maximum population density, and cell yield models have been developed by introducing the different activation energy (E(infa)) values. According to the model, optimal conditions were 35(deg)C, pH 6.5, and 0% (vol/vol) ethanol for the growth rate. For cell density and cell yield, optimal conditions were 32(deg)C, pH 6.0, and 1% (vol/vol) ethanol. No growth was observed for ethanol concentrations above 8% and pH values below 4.0. Other equations have also been made to describe the major end products fermented during fermentation by a Pectinatus sp. The synthesis of propionate and acetate is maximal at 28(deg)C at pHs of 5.5 and 6.25, respectively. This model completes the model suggested by Membre and Tholozan (J. Appl. Bacteriol. 77:456-460, 1994), which includes only one variable, i.e., the glucose concentration.

Predictive modelling: applications in the dairy industry

Predictive modelling has been used in the dairy industry for determining the keeping quality of raw milk and pasteurized products. More recently, predictive equations describing growth and toxin production for a number of bacteria of concern to dairy microbiologists have been developed. A more mathematical approach is also being adopted for determining effective pasteurization conditions for organisms present in milk.

Predictive microbiology

Predictive microbiology is based upon the premise that the responses of populations of microorganisms to environmental factors are reproducible, and that by considering environments in terms of identifiable dominating constraints it is possible, from past observations, to predict the responses of those microorganisms. Proponents claim that predictive microbiology offers many benefits to the practice of food microbiology, and there is growing interest internationally. This review considers the origins, benefits and approaches to predictive microbiology and critically considers limitations and potential solutions. It is suggested that the traditional delineation between kinetic and probabilistic models is artificial, and that the two approaches represent the opposite ends of a spectrum of modelling needs. It is concluded: that despite the complexity of many food systems predictive modelling can be successfully applied; that strategies based on predictive models can simplify problems and allow useful predictions and analyses to be made; that the full potential of the technique has not yet been realised; and that "predictive microbiology" may be seen as providing a rational framework for understanding the microbial ecology of food.

A substrate-mediated assay of bacterial proton efflux/influx to predict the degree of spoilage of beef mince stored at chill temperatures

A method was developed to predict spoilage of minced meat at chill temperatures, based on the difference in proton efflux from and influx into bacterial cells. This difference depends on the number of organisms present, the available glucose in the meat sample and the ability of the organisms to metabolize amino acids. The proton efflux/influx of a meat filtrate containing bacteria was measured at 25 degrees C with a pH/ion meter in the presence of peptone with or without glucose. There was a noticeable rate of change of mV h-1 of the meat filtrate prior to the organoleptic detection of spoilage which may be used semi-predictively to determine the remaining shelf-life of meat at different storage temperatures. The method could be investigated further, encompassing type and relative numbers of organisms, incubation temperature, meat type and composition (i.e. available glucose) to produce a spoilage prediction model. The method does not require sophisticated equipment, only a standard pH/ion meter, is cheap, needing only peptone and glucose, is relatively simple, and takes less than 2 h to perform.

Modeling the growth of Enterococcus faecium in bologna sausage

A study to set up mathematical models which allow the prediction of Enterococcus faecium growth in bologna sausage (mortadella) was carried out. Growth curves were obtained at different temperatures (5, 6, 12, 15, 25, 32, 35, 37, 42, 46, 50, 52, and 55 degrees C). The Gompertz and logistic models, modified by Zwietering, were found to fit with the representation of experimental curves. The variations of the parameters A (i.e., the asymptotic value reached by the relative population during the stationary growth phase), mu m (i.e., the maximum specific growth rate during the exponential growth phase), and lambda (i.e., the lag time) with temperature were then modeled. The variation of A with temperature can be described by an empirical polynomial model, whereas the variation of mu m and lambda can be described by the Ratkowsky model modified by Zwietering and the Adair model, respectively. Data processing of these models has shown that the minimum growth temperature for E. faecium is 0.1 degrees C, the maximum growth temperature is 53.4 degrees C, and the optimal growth temperature is 42 to 45 degrees C.

Modeling of bacterial growth as a function of temperature

The temperature of chilled foods is a very important variable for microbial safety in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the lag time, specific growth rate, and asymptote (growth yield) as a function of temperature. The objective of this research was to determine the suitability and usefulness of different models, either available from the literature or newly developed. The models were compared by using an F test, by which the lack of fit of the models was compared with the measuring error. From the results, a hyperbolic model was selected for the description of the lag time as a function of temperature. Modified forms of the Ratkowsky model were selected as the most suitable model for both the growth rate and the asymptote as a function of temperature. The selected models could be used to predict experimentally determined numbers of organisms as a function of temperature and time.

The effect of incubation temperature and inoculum size on growth of salmonellae in minced beef

Growth rates and lag times of Salmonella organisms were determined in minced beef at temperatures between 10 and 35 degrees C. A mixture of serotypes containing antibiotic-resistance markers was used to allow small numbers of salmonellae to be counted in the presence of an excess of competing organisms. Maximum growth rates and lag times of salmonellae were unaffected by inoculum size. The effect of temperature on growth rate could be described by the simple square root relationship of Ratkowsky et al. (J. Bacteriol. 149, 1-5, 1982).