Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri

Implications of salt and sodium reduction on microbial food safety

Excess sodium consumption has been cited as a primary cause of hypertension and cardiovascular diseases. Salt (sodium chloride) is considered the main source of sodium in the human diet, and it is estimated that processed foods and restaurant foods contribute 80% of the daily intake of sodium in most of the Western world. However, ample research demonstrates the efficacy of sodium chloride against pathogenic and spoilage microorganisms in a variety of food systems. Notable examples of the utility and necessity of sodium chloride include the inhibition of growth and toxin production by Clostridium botulinum in processed meats and cheeses. Other sodium salts contributing to the overall sodium consumption are also very important in the prevention of spoilage and/or growth of microorganisms in foods. For example, sodium lactate and sodium diacetate are widely used in conjunction with sodium chloride to prevent the growth of Listeria monocytogenes and lactic acid bacteria in ready-to-eat meats. These and other examples underscore the necessity of sodium salts, particularly sodium chloride, for the production of safe, wholesome foods. Key literature on the antimicrobial properties of sodium chloride in foods is reviewed here to address the impact of salt and sodium reduction or replacement on microbiological food safety and quality.

Estimation of Storage Time of Yogurt with Artificial Neural Network Modeling

Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.

Effects of culture conditions on the growth and auto-aggregation ability of vaginal Lactobacillus johnsonii CRL 1294

AIMS

To evaluate the effects of different physico-chemical factors on the growth and auto-aggregating ability of vaginal Lactobacillus johnsonii CRL 1294.

METHODS AND RESULTS

L. johnsonii CRL 1294 was cultivated in different culture media, initial pH and temperature of incubation. The growth parameters were estimated by the Gompertz model, being optimal (higher final biomass and growth rate, and shorter lag phase) at an initial pH of 6.5 and at a temperature of 37 degrees C, both in LAPTg and MRS. The auto-aggregation ability, which was assessed by a model of exponential association, was evidenced in all the growth phases, being higher at pH 5 or 6.5.

CONCLUSIONS

The growth of L. johnsonii CRL 1294 was affected in different way by all the physico-chemical factors tested. However, the auto-aggregation ability increased mainly at low initial pH of growth media.

SIGNIFICANCE AND IMPACT OF THE STUDY

The auto-aggregation ability under different culture conditions of a vaginal Lactobacillus strain was systematically and statistically evaluated for the first time. The higher cellular aggregation evidenced at low pH could be a fundamental characteristic in the acidic vaginal environment to promote the protective role of lactobacilli.

Model for the combined effects of temperature, pH and sodium chloride concentration on survival of Shigella flexneri strain 5348 under aerobic conditions

Shigella is recognized as a major foodborne pathogen; however, relatively few studies have been reported on its growth and survival characteristics, particularly under conditions relevant to food. A fractional factorial design was used to measure the effects and interactions of temperature (4-37 degrees C), pH (2-6) and NaCl (0.5-9%) on survival kinetics of Shigella flexneri strain 5348 in BHI broth. Stationary-phase cells were inoculated into sterile media to give initial populations of 6-7 log(10) CFU/ml and bacterial populations were determined periodically by aerobic plate counts. A total of 267 cultures, representing 83 variable combinations of temperature, pH and NaCl concentration, were analyzed. Survivor curves were fitted from plate count data by means of a two-phase linear model to determine lag times and slopes of the curves, from which decimal reduction times (D-values) and times to a 4-log10 inactivation (t 4D) were calculated. Second order response surface models in terms of temperature, initial pH and NaCl concentration were obtained for the inactivation kinetics parameters of S. flexneri using regression analysis. The use of log10 transformation of the inactivation kinetics parameters yielded models with R2 values of >0.8. These models can provide an estimate of Shigella inactivation. The data obtained suggest that Shigella is resistant to acid and salt and that low pH foods stored at low temperatures may serve as vehicles for gastrointestinal illness.

ComBase: a common database on microbial responses to food environments

The advancement of predictive microbiology relies on available data that describe the behavior of microorganisms in different environmental matrices. For such information to be useful to the predictive microbiology research community, data must be organized in a manner that permits efficient access and data retrieval. Here, we describe a database protocol that encompasses observations of bacterial responses to food environments, resulting in a database (ComBase) for predictive microbiology purposes. The data included in ComBase were obtained from cooperating research institutes and from the literature and are publicly available via the Internet.

Validation of bacterial growth inhibition models based on molecular properties of organic acids

Organic acids occur naturally in foods and have been used in many food products as preservatives because they inhibit the growth of most microorganisms. The acids commonly found in foods differ greatly in both their structure and inhibitory effects for different bacteria. A way to represent relationships between different acids was previously described in which principal components analysis (PCA) was applied to 11 physical and chemical properties of 17 organic acids, to arrive at principal properties. These were used for development of regression models that related the minimum inhibitory concentrations (MICs) of organic acids to their principal properties. Separate MIC models were constructed for six different bacteria. The objective of the present study was to test the predictive capabilities of the organism models using different organic acids from the ones used to construct the original models. MIC predictions were made for three acids for each of the six bacteria for which models were previously constructed. MIC determinations for these acids were then carried out and compared with the predictions; these were in good agreement, thus validating the models. The new data were combined with that obtained previously to produce similar, but slightly stronger models. These had R(2) values between 0.861 and 0.992.

The effect of NaCl on survival of Shigella flexneri in broth as affected by temperature and pH

Shigella, a major foodborne pathogen, survives well in salt-containing environments. However, systematic data are scarce. We studied the behavior of Shigella flexneri 5348 in brain heart infusion broth (pH 4 to 6) containing 0.5 to 8% NaCl. Stationary-phase cells were inoculated into sterile media at initial concentrations of 6 to 7 log10 CFU/ml and incubated at 12 to 37 degrees C. Bacterial population sizes were determined periodically by plate counts. Survivor curves were derived from plate count data by using a two-phase linear model to determine lag times and slopes of the curves, from which decimal reduction times (D-values) and times to a 4-log10 inactivation (T4D) were calculated. In media of pH 6, the bacteria grew in the presence of < or = 6% NaCl at 19 and 37 degrees C and in the presence of < or = 7% NaCl at 28 degrees C. In media of pH 5, growth was observed in the presence of < or = 2, < or = 4, < or = 4, and 0.5% NaCl at 37, 28, 19, and 12 degrees C, respectively. Growth did not occur and bacterial populations gradually declined in media of pH 4. While NaCl had a major effect on growth, bacterial survival was affected to a lesser extent. Lag times decreased with increasing NaCl levels; however, the effect on D-values and T4D values was less pronounced. The average T4D values for media of pH 4 containing 0.5 to 6% NaCl were 4, 13, 23, and 61 days at 37, 28, 19, and 12 degrees C, respectively. These results show that S. flexneri is salt tolerant and suggest that salty foods may serve as vehicles for infection with this bacterium.

Debating the biological reality of modelling preservation

Predictive food microbiology is a rapidly developing science and has made great advances. The aim is to debate a number of issues in modelling preservation: (1) inoculum and prehistory effects on lag times and process susceptibility; (2) mechanistic vs. empirical modelling; and (3) concluding remarks (the Species concept, methodology and biovariability). Increasing the awareness in these issues may bridge the gap between the complex reality in food microbial physiology and the application potential of predictive models. The challenge of bringing integrated preservation or risk analysis further and developing ways to truly model and link biological susceptibility distributions from raw ingredients via process survival to outgrowth probabilities in the final product remains.

Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food

The application of Artificial Neural Networks (ANN) in predictive microbiology is presented in this paper. This technique was used to build up a predictive model of the joint effect of NaCl concentration, pH level and storage temperature on kinetic parameters of the growth curve of Lactobacillus plantarum using ANN and Response Surface Model (RSM). Sigmoid functions were fitted to the data and kinetic parameters were estimated and used to build the models in which the independent variables were the factors mentioned above (NaCl, pH, temperature), and in some models, the values of the optical densities (OD) vs. time of the growth curve were also included in order to improve the error of estimation. The determination of the proper size of an ANN was the first step of the estimation. This study shows the usefulness of an ANN pruning methodology. The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity. The best architecture has been sought using genetic algorithms (GA) in conjunction with pruning algorithms and regularization methods in which the initial distribution of the parameters (weights) of the network is not uniform. The ANN model has been compared with the response surface model by means of the Standard Error of Prediction (SEP). The best values obtained were 14.04% of SEP for the growth rate and 14.84% for the lag estimation by the best ANN model, which were much better than those obtained by the RSM, 35.63% and 39.30%, respectively. These were very promising results that, in our opinion, open up an extremely important field of research.

Estimation of vaginal probiotic lactobacilli growth parameters with the application of the Gompertz model

Lactobacilli are widely described as probiotic microorganisms used to restore the ecological balance of different animal or human tracts. For their use as probiotics, bacteria must show certain characteristics or properties related to the ability of adherence to mucosae or epithelia or show inhibition against pathogenic microorganisms. It is of primary interest to obtain the highest biomass and viability of the selected microorganisms. In this report, the growth of seven vaginal lactobacilli strains in four different growth media and at several inoculum percentages was compared, and the values of growth parameters (lag phase time, maximum growth rate, maximum optical density) were obtained by applying the Gompertz model to the experimental data. The application and estimation of this model is discussed, and the evaluation of the growth parameters is analyzed to compare the growth conditions of lactobacilli. Thus, these results in lab experiments provide a basis for testing different culture conditions to determine the best conditions in which to grow the probiotic lactobacilli for technological applications.

The effect of temperature and low pH on survival of Shigella flexneri in broth

The survival characteristics of Shigella fiexneri strain 5348 were determined in brain heart infusion broth as a function of low pH (2 to 5) and temperature (4 to 37 degrees C). Stationary-phase cells were inoculated into sterile media to give initial populations of 6 to 7 log10 CFU/ml. Bacterial populations were determined periodically by aerobic plate counts. Survivor curves were fitted from plate count data using a two-phase linear model to derive lag times and slopes of the curves, from which D-values and times to a 4-D (99.99%) inactivation (T4D) were calculated. In general, survival increased as temperature decreased and as pH increased. Bacterial populations reached undetectable levels (<1.3 log10 CFU/ml) at 37, 28, 19, 12, and 4 degrees C in media adjusted to pH 4 after 5, 15, 23, 85, and 85 days, respectively, and in media adjusted to pH 3 after 1, 7, 9, 16, and 29 days, respectively. In media adjusted to pH 2, bacterial populations were stable for 2 to 12 h at temperatures of 19 degrees C or lower and reached undetectable levels after 1 to 3 days, while at 28 and 37 degrees C, the bacteria were undetectable after 8 and 2 h, respectively. In media adjusted to pH 5, bacterial levels decreased only 0.5 to 1.5 log10 CFU/ml after 75 days at 4 degrees C and decreased to undetectable levels after 135 days at 12 degrees C, while growth occurred at higher temperatures. These results indicate that S. flexneri is acid resistant and that acidic foods may serve as vehicles for infection.

Microbial growth modelling with artificial neural networks

There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

Artificial neural networks: fundamentals, computing, design, and application

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.

Modeling the inhibitory effects of organic acids on bacteria

The inhibitory effect of acids on microbial growth has long been used to preserve foods from spoilage. While much of the effect can be accounted for by pH, it is well known that different organic acids vary considerably in their inhibitory effects. Because organic acids are not members of a homologous series, but vary in the numbers of carboxy groups, hydroxy groups and carbon-carbon double bonds in the molecule, it has typically not been possible to predict the magnitude, or in some cases even the direction, of the change in inhibitory effect upon substituting one acid for another or to predict the net result in food systems containing more than one acid. The objective of this investigation was to attempt to construct a mathematical model that would enable such prediction as a function of the physical and chemical properties of organic acids. Principal Components Analysis (PCA) was applied to 11 properties for each of 17 acids commonly found in food systems; this resulted in four significant principal components (PCs), presumably representing fundamental properties of the acids and indicating each acid's location along each of these four scales. These properties correspond to polar groups, the number of double bonds, molecular size, and solubility in non-polar solvents. Minimum inhibitory concentrations (MICs) for each of eight acids for six test microorganisms were determined at pH 5.25. The MICs for each organism were modeled as a function of the four PCs using partial least squares (PLS) regression. This produced models with high correlations for five of the bacteria (R2 = 0.856, 0.941, 0.968, 0.968 and 0.970) and one with a slightly lower value (R2 = 0.785). Acid susceptible organisms (Bacillus cereus, Bacillus subtilis, and Alicyclobacillus) exhibited a similar response pattern. There appeared to be two separate response patterns for acid resistant organisms; one was exhibited by the two lactobacilli studied and the other by E. coli. Predicting the inhibitory effects of the organic acids as a function of their chemical and physical properties is clearly possible.

Relation between the generation time and the lag time of bacterial growth kinetics

In predictive microbiology, the relation between the lag time (Lag) and the generation time (Tg) is commonly assumed to be proportional, as long as the pre-incubation environmental conditions remain constant. This relation was statistically examined in nine published datasets. For every dataset, it was roughly proportional. However, a more advanced study showed that the ratio Lag/Tg was not totally independent of the environmental conditions. In particular, a significant negative effect of the pH on this ratio was observed in five of the nine datasets. For modeling the environmental dependence of microbial growth parameters, some authors independently deal with Lag and Tg. Other authors only model the environmental dependence of Tg, assuming Lag/Tg to be constant. These two modeling methods were statistically compared for the nine datasets under study. Results differed from one dataset to another. For some, the model developed with a constant ratio Lag/Tg sufficed to describe the data, whereas for the others, an independent modeling of Lag and Tg was more satisfactory.

Revised model for aerobic growth of Shigella flexneri to extend the validity of predictions at temperatures between 10 and 19 degrees C

Although Shigella is a major foodborne pathogen, its growth in foods has received little attention. Growth of S. flexneri 5348 inoculated into commercially available sterile foods (canned broths, meat, fish, UHT milk, baby foods) was studied at 10 to 37 degrees C. S. flexneri was enumerated by surface-plating on Tryptic Soy Agar and growth curves were fitted by means of the Gompertz equation. Observed growth kinetics values and values calculated using a previously developed response surface model compared favorably for growth at 19 to 37 degrees C, but not at < 19 degrees C. To refine the model, additional data were collected for growth at 10 to 19 degrees C. A total of 844 tests in BHI broth, representing 197 variable combinations of temperature (10-37 degrees C), pH (5.0-7.5), NaCl (0.5-5.0%) and NaNO2 (0-1000 ppm) was used for the revised model. The revised model, developed in BHI, gave significantly better agreement of calculated growth kinetics values with those observed in foods at 10 to 19 degrees C.

Computational neural networks for predictive microbiology. II. Application to microbial growth

The growth of a specific microorganism on a certain food is influenced by a number of environmental factors such as temperature, pH, and salt concentration. Methods that delineate the history of the growth of microorganisms are always subject to a considerable debate and scrutiny in the field of predictive microbiology. Regardless of its types, a growth model (e.g., modified Gompertz model) contains several parameters that vary depending on the microorganisms/food combination and the associated prevailing environmental conditions. The growth model parameters for a set of operating conditions are commonly determined from expressions developed via multiple linear regressions. In the present study, a substitute for the nonlinear regression-based equations is developed using computational neural networks. Computational neural networks are applied herein on experimental data pertaining to the anaerobic growth of Shigella flexneri. Results have indicated that predictions by neural networks offer better agreement with experimental data as compared to predictions obtained via corresponding regression equations.

Growth of Shigella flexneri in foods: comparison of observed and predicted growth kinetics parameters

Shigella causes foodborne gastrointestinal illness; however, little information is available on its ability to grow in foods. Commercially available sterile foods (UHT milk, beef broth, chicken broth, vegetable broth, meats, vegetables) were inoculated with S. flexneri 5348 and incubated at 12, 15, 19, 28 or 37 degrees C. Growth curves were fitted from plate count data by the Gompertz equation and exponential growth rates, generation times, lag times and maximum population densities were derived. The observed kinetics values, expressed as T1000 (time, h, required for a 3 log increase in bacterial population), were compared with values calculated using published growth models. Observed and calculated values compared favorably for growth at 19-37 degrees C. S. flexneri grew well in milk at 15-37 degrees C but growth at 12 degrees C was variable. The bacteria readily grew in most foods, even at 12 degrees C; but died off in carrots at 19 and 28 degrees C. Factors other than those used in the growth model may influence bacterial growth in specific foods.