Temperature effect on bacterial growth rate: quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food

Temperature effect on growth rates of Listeria monocytogenes, Salmonella, Escherichia coli, Clostridium perfringens and Bacillus cereus, was studied. Growth rates were obtained in laboratory medium by using a binary dilutions method in which 15 optical density curves were generated to determine one μ value. The temperature was in the range from 2 to 48 °C, depending on the bacterial species. Data were analysed after a square root transformation. No large difference between the strains of a same species was observed, and therefore all the strains of a same species were analysed together with the same secondary model. The variability of the residual error, including both measurements errors and biological strain difference, was homogenous for sub-optimal temperature values. To represent this variability in bacterial kinetic simulation, the 95% confidence interval based on an asymptotic Normal distribution, around the growth rate value was determined. With this modelling approach, the behaviour of bacterial species on food, irrespective of the strain or the laboratory, was described. This growth simulation with confidence limits has several applications, such as to facilitate comparisons between a challenge-test and simulation results, and, to appreciate if the temperature change has or has not a significant effect on a bacterial growth profile, with regard to the uncontrolled factors. The integration of this piece of work in the Sym'Previus software is now in process. Results obtained in five French laboratories will be extended by working on new food and new microbial species and improved by further work on variability estimation.     

Analysis of the lag phase to exponential growth transition by incorporating inoculum characteristics

During the last decade, individual-based modelling (IbM) has proven to be a valuable tool for modelling and studying microbial dynamics. As each individual is considered as an independent entity with its own characteristics, IbM enables the study of microbial dynamics and the inherent variability and heterogeneity. IbM simulations and (single-cell) experimental research form the basis to unravel individual cell characteristics underlying population dynamics. In this study, the IbM framework MICRODIMS, i.e., MICRObial Dynamics Individual-based Model/Simulator, is used to investigate the system dynamics (with respect to the model and the system modelled). First, the impact of the time resolution on the simulation accuracy is discussed. Second, the effect of the inoculum state and size on emerging individual dynamics, such as individual mass, individual age and individual generation time distribution dynamics, is studied. The distributions of individual characteristics are more informative during the lag phase and the transition to the exponential growth phase than during the exponential phase. The first generation time distributions are strongly influenced by the inoculum state. All inocula with a pronounced heterogeneity, except the inocula starting from a uniform distribution, exhibit commonly observed microbial behaviour, like a more spread first generation time distribution compared to following generations and a fast stabilisation of biomass and age distributions.     

食品微生物检验中菌落总数测定的分析

菌落总数是评价食品等许多产品的卫生情况、新鲜程度及污染程度的最常用的指标之一。通过菌落总数的测定可以在一定程度上判断产品卫生情况的优劣,在科学发展和检验技术进步的今天,对检验数据的准确性和可靠性提出了更高的要求。要保证检测结果的准确性和可靠性,真实准确的反映所检样品中细菌污染的程度,检验过程的每一环节都至关重要。     

Modelling of growth, growth/no-growth interface and nonthermal inactivation areas of Listeria in foods

Growth, growth boundary and inactivation models have been extensively developed in predictive microbiology and are commonly applied in food research nowadays. Few studies though report the development of models which encompass all three areas together. A tiered modelling approach, based on the Gamma hypothesis, is proposed here to predict the behaviour of Listeria.Datasets of Listeria spp. behaviour in laboratory media, meat, dairy, seafood products and vegetables were collected from literature, unpublished sources and from the databases ComBase and Sym'Previus. The explanatory factors were temperature, pH, water activity, lactic and sorbic acids. For the growth part, 697 growth kinetic datasets were fitted. The estimated growth rates and 2021 additional growth primary datasets were used to fit the secondary growth models. In a second step, the fitted model was used to predict the growth/no-growth boundary. For the inactivation modelling phase, 535 inactivation curves were used.Gamma models with and without interactions between the explanatory factors were used for the growth and boundary models. The correct prediction percentage (predicted growth when growth is observed + predicted inactivation when inactivation is observed) varied from 62% to 81% for the models without interactions, and from 85% to 87% for the models with interactions. The median error for the predicted population size was less than 0.34 log₁₀(CFU/mL) for all models. The kinetics of inactivation were fitted with modified Weibull primary models and the estimated bacterial resistance was then modelled as a function of the explanatory factors. The error for the predicted microbial population size was less than 0.71 log₁₀(CFU/mL) with a median value of less than 0.21 for all foods.The model enables the quantification of the increase or decrease in the bacterial population for a given formulation or storage condition. It might also be used to optimise a food formulation or storage condition in the case of a targeted increase or decrease of the bacterial population.     

Semantic annotation of Web data applied to risk in food

A preliminary step to risk in food assessment is the gathering of experimental data. In the framework of the Sym'Previus project (http://www.symprevius.org), a complete data integration system has been designed, grouping data provided by industrial partners and data extracted from papers published in the main scientific journals of the domain. Those data have been classified by means of a predefined vocabulary, called ontology. Our aim is to complement the database with data extracted from the Web. In the framework of the WebContent project (www.webcontent.fr), we have designed a semi-automatic acquisition tool, called @WEB, which retrieves scientific documents from the Web. During the @WEB process, data tables are extracted from the documents and then annotated with the ontology. We focus on the data tables as they contain, in general, a synthesis of data published in the documents. In this paper, we explain how the columns of the data tables are automatically annotated with data types of the ontology and how the relations represented by the table are recognised. We also give the results of our experimentation to assess the quality of such an annotation.     

Analysis of a novel class of predictive microbial growth models and application to coculture growth

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277–294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.     

基于Web数据流技术的网络入侵检测研究

针对传统多遍扫描数据库的挖掘技术构建的入侵检测模型已不能满足Web数据流高速并且无限到达的需要,根据多维频繁模式的特点,提出了一种新的入侵检测模型和一种新型数据结构SW.Tree,并给出了一种基于滑动窗口树的挖掘频繁项集的新型算法AFP.对不同流量数据的实验结果表明该模型有较高的报警率和较低的误报率.     

Modeling the growth of lactic acid bacteria in sliced ham processed by high hydrostatic pressure

The main responsible for the spoilage of cooked cured meat products stored under refrigerated and anaerobic conditions are lactic acid bacteria. The application of high hydrostatic pressure (HHP) reduces the lactic acid bacterial growth extending the product shelf-life and preserving natural taste, texture, color and vitamin content. This work studied the influence of pressure level and holding time on the lactic acid bacterial growth in vacuum-packaged sliced ham. Modified Gompertz and Logistic models were used to fit experimental data obtained from post-treatment microbial counts carried out along the product storage. Samples of sliced vacuum-packaged ham treated by HHP and control samples (non-treated) were stored at 8 °C until the microorganism population reached 10⁷ CFU/g. An experimental planning 2² with triplicate at the central point was designed to determine the influence of pressure level (200, 300, and 400 MPa) and holding time (5, 10, and 15 min) on the product shelf-life. The results have shown that the pressure intensity and the holding time significantly influenced microbial population over the product storage. Shelf-life of ham treated at 400 MPa for 15 min was extended from 19 (control samples) to 85 days.     

Development of a Safety Monitoring and Assurance System for chilled food products

The principles of a novel chill chain management policy, coded Safety Monitoring and Assurance System (SMAS) for the optimisation of the distribution of chilled food products within the chill chain are developed. In this system, a new approach based on actual risk evaluation at important points of the chill chain is used in order to promote products to the next stage of distribution. This evaluation based on product's time–temperature history, variation in product's characteristics (e.g. a_w, pH, etc.), and the use of predictive models for the growth of food pathogens, allows to give priority to products in such a way that risk at consumption time is minimized. The effectiveness of SMAS was evaluated against the First In First Out (FIFO) approach, the current method for food distribution, in a case study on the risk of listeriosis of cooked ham using the Monte Carlo simulation technique. Furthermore, the two approaches were compared for their effect on the quality of the products in terms of remaining shelf life at the time of consumption. The results showed that following the SMAS approach the risk of listerisosis is significantly lower while the spoiled products at the time of consumption are significantly reduced compared to FIFO approach.     

Modeling of growth and bacteriocin production by Leuconostoc mesenteroides E131

Leuconostoc mesenteroides E131, isolated from dry fermented sausages, produces an antimicrobial agent, characterized as bacteriocin. The effect of pH and temperature on growth and bacteriocin production, using MRS broth as growth medium, was studied in a fermentor. The pH value at which the best cell growth was observed (6.5) did not coincided with the value at which the maximum bacteriocin activity was attained (5.5). In contrast, the maximum bacteriocin activity was attained at temperature (25 °C) close to the optimum temperature for cell growth (25–30 °C). Notably, the range of pH and temperature for good bacteriocin production was within the range used for sausage fermentation. An empirical model was developed to describe the growth and bacteriocin production in different pH and temperature conditions. The model was able to describe growth and bacteriocin production and it could be used to predict the kinetic parameters of growth and bacteriocin production within the pH and temperature range examined.     

Design of challenge testing experiments to assess the variability of Listeria monocytogenes growth in foods

The assessment of the evolution of micro-organisms naturally contaminating food must take into account the variability of biological factors, food characteristics and storage conditions. A research project involving eight French laboratories was conducted to quantify the variability of growth parameters of Listeria monocytogenes obtained by challenge testing in five food products. The residual variability corresponded to a coefficient of variation (CV) of approximately 20% for the growth rate (μ_max) and 130% for the parameter K = μ_max × lag. The between-batch and between-manufacturer variability of μ_max was very dependent on the food tested and mean CV of approximately 20 and 35% were observed for these two sources of variability, respectively. The initial physiological state variability led to a CV of 100% for the parameter K. It appeared that repeating a limited number of three challenge tests with three different batches (or manufacturers) and with different initial physiological states seems often necessary and adequate to accurately assess the variability of the behavior of L. monocytogenes in a specific food produced by a given manufacturer (or for a more general food designation).     

Time to growth and inactivation of three STEC outbreak strains under conditions relevant for fermented sausages

Published models predicting growth and survival capabilities of shiga-toxin-producing Escherichia coli (STEC) under a_w and lactic acid stress were validated by performing experiments with fermented sausage associated outbreak strains. Strain variation in inactivation and time to growth (TTG) were investigated for strains representing three serotypes (O103, O111, and O157). The TTG and growth boundaries of each strain were compared with predictions of a model for generic acid adapted E. coli and survival with predictions of two inactivation models. In addition, the influence of strain variation on the performance of the inactivation models, in terms of bias and accuracy factors, was illustrated. Strains with induced acid tolerance were used in broths containing 50 or 110 mM total lactic acid. The concentration of undissociated lactic acid (HLac) was adjusted by setting the pH-value, and water activity (0.900 to 0.995 depending on experiment) was adjusted by adding NaCl. The survival capabilities of the outbreak strains were good compared to the model predictions. The average bias factors of inactivation model predictions were within a factor of 2.2 depending on the strain used to validate the model indicating that inactivation rates of outbreak strains were slower than predicted. However, the observed rates were similar to the rates of a previously studied acid tolerant generic E. coli strain. Similarly, the time to growth of two of the strains (O103 and O157) was comparable with model predictions, whereas the growth capability of the third strain (O111) was lower than predicted. These results suggest that the properties of the most tolerant sausage outbreak strains are comparable to tolerant generic E. coli strains, which imply that suitable non-pathogenic E. coli strains are valid surrogates for fermented sausage outbreak strains. The relative sensitivity of strains depended on the environmental parameters and the response evaluated. The strain with the smallest log reduction at 20 °C was O157, whereas it was strain O103 at 8 °C. Under conditions unfavorable for growth, the time to growth was much shorter for strains O103 and O157 than for strain O111, whereas differences between strains were negligible under conditions favorable for growth. Depending on the response variable and the specific application the limitation of not addressing strain variation may lead to biased, fail-dangerous, predictions. Thus, solutions on how to best address strain variation in the development and validation of predictive models are needed.     

分组加料柔性控制技术在制丝生产中的应用

分组加料是对同一牌号不同组别的烟叶针对性地施加不同料液的一种工艺方法。为了解决传统模式下不同料液切换时导致断流频次增加的问题,提出了分组加料的柔性控制技术,采用叶组烟包信息与对应料液信息在生产线上的同步传递、数据模拟、集成处理、中央控制方式,通过控制加料系统不同阀岛的开启与关闭来更替料液切换,从而实现在不断流情况下将料液精准地施加到目标等级叶组烟包上。     

Towards a novel class of predictive microbial growth models

Food safety and quality are influenced by the presence (and possible proliferation) of pathogenic and spoilage microorganisms during the life cycle of the product (i.e., from the raw ingredients at the start of the production process until the moment of consumption). In order to simulate and predict microbial evolution in foods, mathematical models are developed in the field of predictive microbiology. In general, microbial growth is a self-limiting process, principally due to either (i) the exhaustion of one of the essential nutrients, and/or (ii) the accumulation of toxic products that inhibit growth. Nowadays, most mathematical models used in predictive microbiology do not explicitly incorporate this basic microbial knowledge. In this paper, a novel class of microbial growth models is proposed. In contrast with the currently used logistic type models, e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277–294], the novel model class explicitly incorporates nutrient exhaustion and/or metabolic waste product effects. As such, this novel model prototype constitutes an elementary building block to be extended in a natural way towards, e.g., microbial interactions in co-cultures (mediated by metabolic products) and microbial growth in structured foods (influenced by, e.g., local substrate concentrations). While under certain conditions the mathematical equivalence with classical logistic type models is clear and results in equal fitting capacities and parameter estimation quality (see Poschet et al. [Poschet, F., Vereecken, K.M., Geeraerd, A.H., Nicolaï, B.M., Van Impe, J.F., 2004. Analysis of a novel class of predictive microbial growth models and application to co-culture growth. International Journal of Food Microbiology, this issue] for a more elaborated analysis in this respect), the biological interpretability and extendability represent the main added value.     

Response surface model for prediction of growth parameters from spores of Clostridium sporogenes under different experimental conditions

Clostridium sporogenes is considered to be a non-toxingenic equivalent of proteolytic Clostridium botulinum, and it also causes food spoilage. The effects of temperature (16.6-33.4 degrees C), pH value (5.2-6.8) and concentration of sodium chloride (0.6-7.4%) on the growth parameters of C. sporogenes spores were investigated. The growth curves generated within different conditions were fitted using Baranyi function. Two growth parameters (growth rate, GR; lag-time, LT) of the growth curves under combined effects of temperature, pH and sodium chloride were modeled using a quadratic polynomial equation of response surface (RS) model. Mathematical evaluation demonstrated that the standard error of prediction (%SEP) obtained by RS model was 1.033% for GR and was 0.166% for LT for model establishing. The %SEP for model validation were 43.717% and 5.895% for GR and LT, respectively. The root-mean-squares error (RMSE) was in acceptable range which was less than 0.1 for GR and was less than 8.0 for LT. Both the bias factor (B(f)) and accuracy factor (A(f)) approached 1.0, which were within acceptable range. Therefore, RS model provides a useful and accurate method for predicting the growth parameters of C. sporogenes spores, and could be applied to ensure food safety with respect to proteolytic C. botulinum control.     

Contribution of Fourier transform infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat spoilage

The aim of this work was to investigate the feasibility of Fourier transform infrared (FTIR) spectroscopy to quantify biochemical changes occurring in fresh minced pork meat in the attempt to monitor spoilage. For this reason, partial least squares (PLS) models were constructed to correlate spectral data from FTIR with minced pork meat spoilage during aerobic storage of meat samples at different storage temperatures (0, 5, 10, and 15 °C). Spectral data were collected from the surface of meat samples in parallel with microbiological analysis to enumerate the population of total viable counts, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria and Enterobacteriaceae. Qualitative interpretation of spectral data was based on sensory evaluation, using a three point hedonic scale, discriminating meat samples in three quality classes, namely fresh, semi-fresh and spoiled. The purpose of the developed models was to classify minced pork samples in the respective quality class, and also to correlate the population dynamics of the microbial association with FTIR spectra. The obtained results demonstrated good performance in classifying meat samples in one of the three pre-defined sensory classes. The overall correct classification rate for the three sensory classes was 94.0% and 88.1% during model calibration and validation, respectively. Furthermore, PLS regression models were also employed to provide quantitative estimations of microbial counts during meat storage. The performance was based on graphical plots and statistical indices (bias factor, accuracy factor, standard error of calibration, standard error of prediction, and correlation coefficient). The values of the bias factor were close to unity for all microbial groups indicating no systematic bias of the models. Moreover, the calculated values of the accuracy factor showed that the average deviation between predictions and observations was 7.5% and 7.9% for total viable counts and Pseudomonas spp. and 10.7% and 11.3% for lactic acid bacteria and B. thermosphacta. Finally, correlations above 0.80 between observed and estimated counts were observed for both training and test data sets.     

Prediction of Listeria spp. growth as affected by various levels of chemicals, pH, temperature and storage time in a model broth

The effects of concentration of NaCl (0.5 to 12.5%), methyl paraben (0.0 to 0.2%), sodium propionate (0.3%), sodium benzoate (0.1%), potassium sorbate (0.3%), pH (>5.9) temperature (4 to 30°C), storage time (up to 58 d) and inoculum (>10⁵ to >10⁻² per ml) on the log₁₀ probability percentage of one cell of Listeria spp. to initiate growth in a broth system were evaluated in a factorial design study. At pH 5.96 and temperature ranging from 4 to 30°C the concentrations of sodium propionate, potassium sorbate, and sodium benzoate examined allowed growth of L. monocytogenes with lag phases at 4°C of 18, 27 and 21 days, respectively. For 0.1 and 0.2% methyl paraben growth of all Listeria spp. was initiated at 8°C and 30°C, respectively. At pH 6, concentration of 12% NaCl supported the growth of L. monocytogenes at 8 to 30°C, whereas 12.5% inhibited all Listeria species. Four regression equations were derived relating probability of growth initiation to temperature, concentrations of NaCl and preservatives storage time, and Listeria species specific effects. From these equations, the number of cells needed for growth initiation can be calculated. The impact of this type of quantitative study and its possible application on the development of microbial standards for foods is discussed.     

Effect of periodic fluctuation in the osmotic environment on the adaptation of Salmonella

The growth of Salmonella from different osmotic histories was studied in low water activity conditions. Cell cultures were successively diluted and grown in batch, in minimal medium, without and then with added NaCl, several times and from different inoculum levels. The viable count curves obtained in low water activity conditions showed an initial decline after which the culture either died out or recovered and entered the exponential phase. After the first batch with NaCl added, the subsequent curves at low water activity showed progressively smaller initial decline and shorter lag time as the number of transfers from high to low water activity conditions increased. The observed curves were analyzed by F-tests applying an extension of the model of Baranyi and Roberts (1994). The results suggest that periodic, systematic "training" can improve the adaptation capability of the organism without resulting in higher specific growth rate.     

Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries

Predictive microbiology is considered in the context of the conference theme "chance, innovation and challenge", together with the impact of quantitative approaches on food microbiology, generally. The contents of four prominent texts on predictive microbiology are analysed and the major contributions of two meat microbiologists, Drs. T.A. Roberts and C.O. Gill, to the early development of predictive microbiology are highlighted. These provide a segue into R&D trends in predictive microbiology, including the Refrigeration Index, an example of science-based, outcome-focussed food safety regulation. Rapid advances in technologies and systems for application of predictive models are indicated and measures to judge the impact of predictive microbiology are suggested in terms of research outputs and outcomes. The penultimate section considers the future of predictive microbiology and advances that will become possible when data on population responses are combined with data derived from physiological and molecular studies in a systems biology approach. Whilst the emphasis is on science and technology for food safety management, it is suggested that decreases in foodborne illness will also arise from minimising human error by changing the food safety culture.