Scientific Publications - Work Done by Microbiology Reader Bioscreen C

Food Microbiology, 2000, 17, pp. 83-92

Growth of Listeria monocytogenes as a function of dynamic environment at 10°C and accuracy of growth predictions with available models

M. Cheroutre-Vialette* and A. Lebert

ABSTRACT

A combination of a factorial design and two central composite designs was used to assess quantita­tively the effects and interactions of water activity (1-0.95) and pH (5.6-9.5) variations on the growth of Listeria monocytogenes in a meat broth at 10°C. At inoculation or at the beginning of the exponen­tial phase, the cells were exposed to the addition of NaCI and acetic acid or NaCI and NaOH. The effects of abrupt fluctuating conditions on the generation and lag times were analysed using turbidity mea­surements. The data indicated that the cells exposed to osmotic and acid or alkaline variable conditions from the time of inoculation were less affected than cells exposed at the beginning of the exponential phase. In this last case, a lag phase could be induced and the growth recovery was different from those expected in the new environment. Generation time values were estimated by three available predictive models which describe the effects of temperature, salt concentration and pH on L. monocytogenes growth to highlight the potential problems of variable conditions.

INTRODUCTION

The aim of predictive microbiology lies in pre­ dicting the growth kinetics of micro-organisms at a given moment during food processing and so predicting the evolution of food during storage, manufacturing or preservation accidents. One of the great interests of predictive micro­ biology is to optimize the shelf life of a high‑ risk food product by the use of appropriate predictions (Gould 1989). Mathematical models have been developed to simulate the growth of micro-organisms in relation to given environ­mental conditions (Buchanan 1993, Ross and McMeekin 1994) and can be classified by the microbiological event studied, the modelling approach used or the variables considered (Whiting and Buchanan 1994). Most of these models were elaborated from data acquired un­der constant environmental conditions as poly­nomial models, e.g. Food micromodel, Pathogen modeling program, which expressed bacterial growth as a function of factors such as pH, water activity (%NaCl), temperature (McClure et al. 1994, Lebert and Lebert 1997). But envir­onmental factors, such as temperature, Aw, or pH which are often the most important factors governing microbial behaviour in food, may vary extensively during food processing, throughout the complete production and dis­tribution chain of a food product. Indeed, a food product is exposed to environmental var­iations during certain stages in a process, e.g. food cooling, natural product acidification or product brining. In this way, it is important to be able to quantify undesirable micro-organism growths, like Listeria monocytogenes, in envir­onmentally variable conditions.

The purpose of this study was to determine the effects of the combined alkaline-osmotic and acid-osmotic conditions, constant or vari­able during time, on the growth of a L. monocy­togenes strain at low temperature. Considering the temperatures likely to be used for refriger­ated storage (0-10°C) or for working meat pro­duct premises, the temperature of the study was established at 10°C. NaCl, acetic acid and NaOH were used to regulate water activity, acid pH and alkaline pH respectively. As the capacity of Bioscreen C for studying growth kinetics under variable conditions was ever es­tablished (Cheroutre-Vialette et al. 1998), this material was used in this study. Moreover, with the objective to illustrate the potential pro­blems associated with the modelisation of growth under fluctuating pH/Aw, conditions, we proposed to analyse the accuracy of the gen­eration time predictions with available models and characterize these predictions with our ex­perimental data.

Materials and Methods

Strain and media

Listeria monocytogenes 14 (serotype 4b, ob­tained from industrial environment) was used throughout the study. Stock cultures were maintained in TSA agar (Difco, OSI, Maure­pas, France) slopes and stored at 4°C.

For preparation of inocula, cultures on TSA were subcultured in a meat medium (MM) which contained meat peptone (Merck, Nogent sur Marne, France) 10 gl-1, yeast extract (Dif­co) 5 gl-1 and glucose (Prolabo, Fontenay sous bois, France) 5 gl-1. The culture medium for growth studies was a tryptic meat broth (TMB) (Fournaud et al. 1973). It was buffered with a K₂HPO₄-KH₂PO₄ (Merck) 0.1 mol l-1 solution in proportion 1:1 (v/v) to pH 7^.0.

Monitoring of bacteria/growth

An automated turbidimeter (Bioscreen C, Labsystem, Labsystem France SA, Les Ulis, France) was used to follow the growth of L. monocytogenes 14 in the micro-titer plates. Optical density (OD) was read at a wavelength of 600 nm.

Experimental procedure

The strain was incubated on TSA agar slopes for 7 h at 37°C and then transfered to MM that was incubated in a rotary shaker waterbath (Aquatron, Infors, Switzerland) for 20 h at 20°C, by which time the strain had reached the stationary phase. A proportion of the culture was inoculated in various media to give a con­centration of about 3^.10⁷ cfu ml-1, in order to be above the detection threshold of the Bio­screen C. The viable number of bacteria was confirmed by TSA plate counts.

For all experiments, three conditions were studied: standard, limiting and shock condi­tions. Under standard and limiting conditions, bacteria were grown in TMB or in TMB ad­justed to the desired A_R, and pH values (Fig. 1). For the inoculated TMB medium, 300 µl were dispensed in each of six wells and the same vo­lume of non-inoculated medium was dispensed in each of four wells in order to determine the OD of the growth medium and to detect possi­ble contamination. The well numbers were con­sidered as replicates. The shock exposures were arranged as follows: 300 µl of inoculated and 300 µl of non-inoculated TMB were dispensed in each of six and four wells, respectively. An aliquot (100 µl) of concentrated ( x 4) shock so­lutions or standard TMB was added when the OD was near 0^.2, corresponding to the begin­ning of the exponential phase. The osmotic shock was achieved by the addition of NaCl (Prolabo) according to Chirife and Resnik (1984). Acetic acid (Carlo Erba, Nanterre, France) was added to adjust acid pH. The effect of high pH stresses was studied with the presence of NaOH (Prolabo). The time which elapsed before adding the shock solutions to 90 wells was about approximately 10 min. These plates were placed in the Bioscreen C previously adjusted to 10°C.

Figure 1. Experimental plan showing the combination of osmotic and acid stresses using NaCI and acetic acid respectively ((D); and the combination of osmotic and alkaline stresses using NaCI and NaOH respectively (Q). The points of the factorial design are represented by n. The experiment numbers are indicated in italics.

Experimental design

The growth of L. monocytogenes 14 at 10°C in the presence of salt (0-8%), corresponding to Aw value (1-0^.95) and in different values of acid pH (5^.6-7^.0) or alkaline pH (7^.0-9^.5) was stu­died by using an experimental design. This design was a combination of two central com­posite designs and a factorial design (Fig. 1). Each factor was studied at five levels and for each combination, the shock and limiting conditions were studied. At least 50 experi­ments were performed.

Data analysis

Averages of the OD were calculated for the six repetitions of inoculated media and for the four repetitions of non-inoculated media. The data were then analysed using the procedure

1. (ODi)t, the mean OD of the six repetitions of inoculated media;

2. (ODni)t, the mean of the OD of the four repetitions of non-inoculated media;

3. (ΔOD)t=(ODi)t-(ODni)t;

4. log10[(ΔOD)t/(ΔODmin)] where ΔODmin was the lowest ΔOD value above the detection threshold.

As indicated by Cheroutre-Vialette et al. (1998), a modified Gompertz equation (Zwietering et al. 1990) was used to fit the growth curves log10[(ΔOD)t/(ΔODmin)]=f(t). Growth parameters such as A (logarithmic increase of population), LT (lag time) and µ (maximal growth rate) were determined by non-linear regression with STAT-ITCF statistic software (Gouet J.P. and Philippeau G., Institut technique des Céréales et des fourrages, Paris, France). GT (generation time) was derived from µ using the relatio: GT=log10(2)/µ.

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In standard and limiting conditions, the time of inoculation was taken as zero time when considering the growth curve. In shock conditions, the time of the addition of the shock solution was taken as zero time in the calculation of growth parameters.

Model predictions

The predictions of some models, indicated in Table 1, were estimated. The ratio of predicted and observed generation times was calculated for the studied combinations (predicted gen­eration time/observed generation time). One complementary measure, proposed by Ross (1996) as simple index of the performance of models in predictive microbiology, was also evaluated. This value, termed the ^`bias factor, may be interpreted as the average ratio of the predicted and observed values and is defined as follows:

where y predicted is the predicted generation time, y observed is the observed value, and n is the number of observations used in the calcu­lation. Perfect agreement between predictions and observations will lead to a bias factor of 1.

Results

Growth kinetics of L. monocytogenes

Figure 2 gives the growth curves of L. monocy­togenes 14 observed when an alkaline shock (pH 9^.5), an osmotic shock (8%) or the combination of these shocks were applied in exponential phase (shock condition). The abrupt addition of 8% NaCl has a marked effect on the growth recovery of the micro-organism compared to the effect produced by the presence of NaOH to alkaline pH 9^.5. The growth curve asso­ciated to the combined alkaline-osmotic shock solutions differed from those of uncombined shocks suggesting an interactive effect of NaOH and NaCl at 10°C. The effects of acid shock (pH 6^.3), osmotic shock (8% NaCl) and combined acid (pH 6^.3) - osmotic (8%) shocks at 10°C on the exponential phase of L. monocy­togenes 14 growth were shown in Fig. 3. The acid-osmotic variable conditions have particu­larly affected the growth recovery of the strain. Indeed, the organic acid and the solute pre­sented an interactive effect on the growth of micro-organism.

In shock condition, L. monocytogenes 14 responded instantaneously to small changes of pH and A_te,. Small acid and acid-osmotic

variations involved an unusual growth curve as shown by Fig. 3. Indeed, consecutively to these shocks, the growth of micro-organism contin­ued and a rupture of the sigmoid curve was seen before the growth recovery. The Gompertz equation fitting left this phenomenon out of ac­count and considered the growth recovery cor­responding to the generation time values. In exchange, large environmental conditions dur­ing exponential phase (i.e. shock condition) of the micro-organism induced a lag phase before the growth recovery. This lag phase was more pronounced especially as the concentrations of shock solutions were higher, as shown by Fig. 4 in case of alkaline-osmotic shocks. This induced lag period was really observed and cal­culated when a high concentration of NaCl, e.g. 6^.8 and 8%, was added with the organic acid in the culture.

Some growths were particularly affected by the exposure to the combined shock solutions in exponential phase. Indeed, no increase of op­tical density during the experimental period, i.e. 21 days, underlying no increase of popula­tion, was noted for the combination pH 5^.6 and 4 or 8% NaCl.

An adaptation period to the new environ­ment (temperature, pH and water activity)

was observed in limiting condition, i.e. when the shock solutions were present at the begin­ning of the culture. This period reached 12 days if the cells were placed in presence of higher concentrations of NaCl and alkaline pH, 8% and 9^.5, respectively (data not shown). An ex­perimental condition (pH 5^.6, 8% NaCl) caused no increase of optical density during the experimental period.

The generation times calculated with the Gompertz equation revealed that growth re­covery obtained in limiting conditions was dif­ferent to those observed in shock conditions. As indicated by the values indicated in Table 2, the cells were more affected by the exposure to the uncombined or combined shocks in expo­nential phase. Indeed, the shock condition pre­sented a generation time value higher than calculated in limiting condition whatever the environmental variation.

In conclusion, two principal pheno­mena were observed when the bacteria sub­mitted to abrupt change of pH and A_w (i) large environmental variations induced a lag phase following the exposure to shock solutions, and (ii) the growth continued with a generation time value different from that observed before the change.

Figure 4. Evolution of induced lag times (ex­pressed in h) in shock conditions for all combina­tions of alkaline-osmotic shocks applied on the growth of L. monocytogenes 14 at 10°C.

Polynomial models predictions

Table 3 gives the estimation of the predictions of the different models: PMP, FMM and Lm14 (Table 1). The aim of this part was not to com­pare polynomial models, but to show the position of their predictions with regard to variable environmental conditions. The range of predicted values was wide. Considering the limiting condition, the ratio calculated was in

general less than 1^.0, indicating that the pre­dicted value was inferior to those observed. The predictions were more especially conser­vative as the culture conditions were severe, i.e. low pH and high salt concentration. As indicated by Fig. 5(a), few plots were above the identity line indicating that the predicted para­meters were higher than the observed values in limiting conditions. This was observed for growth predictions associated to combined al­kaline pH-osmotic conditions or uncombined osmotic conditions. In cases of growth predic­tions under low pH (pH < 6^.3), the predictions values were in general three or four times be­low the observed values and might be 14 times inferior in presence of low pH and higher salt concentration, i.e. pH 5^.8 and 6^.8% or pH 6^.3 and 8%. Considering the shock condition (Fig. 5(b)), the trend of models to have `fail safe' predictions were confirmed and increased. All the points were below the identity line, representing predictions which were shorter than the observed generation time. These re­sults were confirmed by the bias factors which maybe interpreted as the geometric mean ratio of predicted and observed generation times (Table 3). The bias factors calculated in shock condition showed, on average, 1^.5-fold greater bias than those observed in limiting condition.

Discussion

For several years, it was largely demonstrated that refrigeration in conjunction with other factors, e.g. salt or acid, may severely retard or prevent the growth of micro-organisms (Con­ner et al. 1986, Sorrells and Enigl 1990). As L. monocytogenes is common in the food proces­sing environment (Cox et al.1989) and as a food product is often exposed to environmental var­iations, it is important to evaluate the adapta­tion of this bacteria to variable conditions of environmental factors as pH and Aw at low tem­perature. A previous study, Cheroutre-Vialette et al. (1998) has reported that the generation times calculated for the growths of L. monocy­togenes in shock and limiting conditions under variable pH or Aw conditions at 20°C were sig­nificantly different. Moreover, an additional lag time in shock condition could be observed. The results obtained in the present study car­ried out at low temperature and under com­bined pH-Aw conditions were in agreement with the conclusions established in this pre­vious study.

The aim of this paper was to introduce more advances into the field of predictive microbiol­ogy, by specifying the microbial behavior under dynamic environment of pH and water activity and collecting experimental data. In this way, the use of Bioscreen C allowed to rapidly ex­plore the microbial impact of varying pH and Aw conditions. In recent years, the interest in developing dynamic mathematical models that describe the growth of microorganisms in the presence of environmental factor variations has increased. With the objective to take into account the effects of temperature variations on microbial growth, Baranyi et al. (1993) ob­served that the abrupt transitions could lead to adjustment periods, i.e. microbial cultures, when shifted abruptly from one temperature to another, may exhibit a transient growth rate before assuming the growth rate expected at the new temperature. Our data under higher pH and/or Aw, variations confirmed the pre­sence of adjustment period which was repre­sented by an induced lag phase. When these environmental changes were smaller, an unusual growth curve was observed. This char­acteristic raised some questions about its origin. This could be assigned to the material used, the Bioscreen C. The lateral agitation of the plates in Bioscreen C could have been

insufficient for homogenous distribution of the compound in the medium (Laplace et al. 1993). At the moment of shock (acid or acid-osmotic), a time is needed for the repartition of the shock solutions in the well. A study of Jorgensen et al. (1995) has also reported that the cells of L. monocytogenes incubated in media containing NaCl became rapidly longer than cells grown without salt. Such morphological changes could also lead to variations in the turbidity of the medium.

The experiments of the present study showed also that the recovered growth rate after the change of pH and/or A_w was usually less than

those associated to the new environment. Other investigations of bacterial growth pre­dictions with changes in temperature by Zwie­tering et al. (1994) andVan Impe et al. (1995), or with changes in pH by Rosso (1995) admitted the possible transposition of results obtained from constant conditions to variable condi­tions. Considering our results, such postulate is not suitable to take into account the physio­logical response of micro-organisms.

The results of this study highlighted the pro­blems associated with the variable conditions and the available models established in con­stant conditions. The results obtained with the predictions estimated with the models (FMM, PMP, Lm14) are not surprising taking into con­sideration that models were developed with strains of different origins, in media of various compositions and in different conditions of growth. For example, the pH was adjusted with HCl in PMP or Lm14, lactic acid in FMM. Acetic acid was used for pH regulation in our growth experiments and it was well estab­lished that this organic acid presents a higher inhibitory activity on L. monocytogenes (Sor­rells et al. 1989, Conner et al. 1990, Fernandez et al. 1997). As can be pointed out by this study, the predictions of these models based on data generated under constant conditions can reliably predict growth under fluctuating conditions of pH and A_w. But, a systematic over-prediction was revealed under dynamic environment. Under a dynamic environment, the bias factor showed that the recovered growth rates caused much larger generation times than those under constant conditions. In consequence, the structure of these models seemed to be inappropriate to take into account the microbial response to variable en­vironmental conditions. Moreover, the induced lag phase observed in dynamic environment of pH and A_w could not be integrated by these models. The necessity to determine a new way to model changing pH values, varying A_w or other food paramenters was underlined (Whit­ing and Buchanan 1994, Cheroutre-Vialette et al.1998). Neuronal networks could represent a new approach to take into account the varia­bility of cell response in variable conditions and produce a dynamic model. A previous study (Baucour and Lebert 1997) provided a good prediction of L. monocytogenes growth under variable conditions. Such dynamic models could follow the microbial impact of the different steps associated with the production, distribu­tion and retailing of a food (Buchanan 1993).

Acknowledgements

We thank Dr RC Whiting and Dr RL Buchanan at the US Department of Agriculture-Agricul­tural Research Service for providing the Pathogen Modelling Program.

References

Baranyi, J., Roberts, T. A. and McClure, P. J. (1993) A Non-autonomous Differential Equation to Model Bacterial Growth. Food MicrobioL 10, 43-59.

Baucour, P. and Lebert, A. (1997) Utilisation de reseaux de neurones pour la prediction de la croissance bacterienne. In: 5eme Journees Agroindustrie et Methodes Statistiques, 3-5 Decembre 1997, Association pour la Statistique et ses Utilisations, INRAVersailles, France, pp. 30.1-30.10.

Begot, C., Desnier, I., Daudin, J. D., Labadie, J. C. and Lebert, A. (1996) Recommendations for calculating growth parameters by optical density measurements. J. Microbiol. Methods. 25, 225-232.

Buchanan, R. L. (1993) Predictive food microbiology. Trends Food Sci. Technol. 4, 6-11.

Cheroutre-Vialette, M., Lebert, I., Hebraud, M., Labadie, J. C. and Lebert, A. (1998) Effects of pH or aR, stress on growth of Listeria monocytogenes. Int. J. Food Microbiol. 42, 71-77.

Chirife, J. and Resnik, S. L. (1984) Unsaturated solutions of sodium chloride as reference sources of water activity at various temperatures. J. Food Sci. 49,1486-1488.

Conner, D. E., Brackett, R. E. and Beuchat, L. R. (1986) Effect of temperature, sodium chloride and pH on growth of Listeria monocytogenes in cabbage juice. Appl. Enuiron. Microbiol. 52, 59-63.

Conner, D. C., Scott, V. N. and Bernard, D. T. (1990) Growth, inhibition, and survival of Listeria monocytogenes as affected by acidic conditions. J. Food Project. 53, 65255.

Cox, L. J., Kleiss, T., Cordier, J. L., Cordellana, C., Konkel, P., Pedrazzini, C., Beumer, R. and Siebenga, A. (1989) Listeria spp. in food processing, nonfood and domestic environments. Food Microbiol. 6, 49-61.

Fernandez, P. S., George, S. M., Sills, C. C. and Peck, M. W. (1997) Predictive model of the effect of CO2i pH, temperature and NaCI on the growth of Listeria monocytogenes. Int. J. Food Microbiol. 37, 3715.

Fournaud, J., Sale, P. and Valin, C. (1973) Conservation de laviande bovine sous emballage plastique, sous vide ou en atmosphere contr&lee. Aspects biochimiques et microbiologiques. XIM Reunion europeenne des chereheurs en uiande. Paris-1, 291-313.

Gould, G. W. (1989) Predictive mathematical modelling of microbial growth and survival in foods. Food Sci. Technol. Ibday. 3, 89-92.

Jorgensen, F., Stephens, P. J. and Knochel, S. (1995) The effect of osmotic shock and subsequent adaptation on the thermotolerance and cell morphology of Listeria monocytogenes. J. Appl. Bacteriol. 79, 274-281.

Laplace, C., Desnier, I., Daudin, J-D., Lebert, A. and Labadie, J-C. (1993) Mesure de la croissance bacterienne par densite optique en vue de sa modelisation. Analyse critique sur un exemple:

Listeria monocytogenes. In: Flashs sur les microorganismes pathogens dans les aliments. Societe Franraise de Microbiologie, Colloque Paris, pp. 275-283.

Lebert, I. and Lebert, A. (1997) Microbiologie Predictive: theorie et applications industrielles. Microbiol. Aliments Nutr.15,157-172.

Lebert, I., Begot, C. and Lebert, A. (1998) Development of two Listeria monocytogenes growth models in a meat broth and their application to beef meat. Food Microbiol. 15, 499-509.

McClure, P. J , Blackburn, C. D., Cole, M. B., Curtis, P. S., Jones, J. E., Legan, J. D., Ogden, I. D., Peck, M.W., Roberts, T. A., Sutherland, J. P. and Walker, S. J. (1994) Modelling the Growth, Survival and Death of Microorganisms in Foods: The UK Food Micromodel Approach. Int. J. Food Microbiol. 23, 265-275.

Ross, T. (1996) Indices for performance evaluation of predictive models in food microbiology. J. Appl. Bact. 81, 501-508.

Ross, T. and McMeekin, T. A. (1994) Predictive microbiology. Int. J. Food Microbiol. 23, 241-264.

Rosso, L. (1995) Modelisation et Microbiologie preuisionnelle. Elaboration d'un nouuel outil pour l'agroalimentaire. These de Doctorat, Universite Claude Bernard, Lyon.

Sorrells, K. M., Enigl, D. C. and Hatfield, J. R. (1989) Effect of pH, acidulant, time and temperature on growth and survival of Listeria monocytogenes. J. Food Protect. 52, 571-573.

Sorrells, K. M. and Enigl, D. C. (1990) Effect of pH, acidulant, sodium chloride and temperature on the growth of Listeria monocytogenes. J. Food. Saf. 11, 31-37.

Van Impe, J. F., Nicola!, B. M., Schellekens, M., Martens, T. and De Baerdemaeker, J. (1995) Predictive microbiology in a dynamic environment: a system theory approach. Int. J. Food Microbiol. 25, 227-249.

Whiting, R. C. and Buchanan, R. L. (1994) Microbial modelling. Food Technol. 48,113-120.

Zwietering, M. H., Jongenburger, I., Rombouts, F. M. and Van't Riet, K. (1990) Modeling of the bacterial growth curve. Appl. Env. Microbiol. 56, 1875-1881.

Zwietering, M. H., Dewit, J. C., Cuppers, H. G. A. M. and Van't Riet, K. (1994) Modeling of Bacterial Growth with Shifts in Temperature. Appl. Enuiron. Microbiol. 60, 204-213.

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