A Combined Model To Predict the Functionality of the Bacteriocin-Producing Lactobacillus sakei Strain CTC 494

The use of bacteriocin-producing lactic acid bacteria for improvedfood fermentation processes seems promising. However, lack offundamental knowledge about the functionality of bacteriocin-producingstrains under food fermentation conditions hampers their industrialuse. Predictive microbiology or a mathematical estimation ofmicrobial behavior in food ecosystems may help to overcome thisproblem. In this study, a combined model was developed thatwas able to estimate, from a given initial situation of temperature,pH, and nutrient availability, the growth and self-inhibitiondynamics of a bacteriocin-producing Lactobacillus sakei CTC494 culture in (modified) MRS broth. Moreover, the drop in pHinduced by lactic acid production and the bacteriocin activitytoward Listeria as an indicator organism were modeled. Self-inhibitionwas due to the depletion of nutrients as well as to the productionof lactic acid. Lactic acid production resulted in a pH drop,an accumulation of toxic undissociated lactic acid molecules,and a shift in the dissociation degree of the growth-inhibitingbuffer components. The model was validated experimentally.

INTRODUCTION

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Introduction

Predictive microbiology is frequently applied in the area offood microbiology to develop and apply mathematical models tosimulate the responses of undesirable microorganisms to specifiedenvironmental variables (8, 10, 18, 22, 24). In the meat industry,for example, several of these models have been successfullydeveloped in the area of risk assessment and microbial shelf-lifestudies, focusing on the outgrowth, toxin production, or inactivationof harmful microorganisms (1, 19).

Recently, there has also been interest in the modeling of beneficialmicroorganisms deliberately added to food to produce a desiredeffect. For instance, modeling of the functionality of bacteriocin-producinglactic acid bacteria seems promising for the prediction of bacteriocinbioactivity in foods (12). Bacteriocins are, in general, smallpeptides or proteins with an antibacterial mode of action towardsstrains that are closely related to the producer organism, oftenencompassing spoilage bacteria and food-borne pathogens (6).Bacteriocin-producing strains may be applied as starter culturesor cocultures in the food fermentation industry to obtain morecompetitive strains and to reduce the risk of the outgrowthof such undesirable bacteria (5). However, some doubt abouttheir industrial application as novel functional starter culturesremains. Strains that display bacteriocin activity under laboratoryconditions do not necessarily perform well once they are appliedin the food under fermentation conditions (20). Modeling mayhelp to clarify how specific conditions that prevail in thefood environment during fermentation influence the performanceof bacteriocin-producing starters (16).

As an example, Lactobacillus sakei CTC 494 is of particularinterest as a functional bacteriocin-producing starter for sausagefermentation (2, 11). In previous studies, the effects of constanttemperature and pH (13), the presence of salt and nitrite (14),and the availability of complex nutrients (15) on the in vitrofunctionality of L. sakei CTC 494 in (modified) MRS broth wereinvestigated through a modeling approach. Although MRS mediumis not a perfect meat simulation medium (4), it permits thestudy in vitro of the effect of some typical sausage fermentationconditions in a meat peptide environment.

In this study, data from earlier fermentation experiments withL. sakei CTC 494 at constant pH and temperature presented byLeroy and De Vuyst (13) were remodeled using the nutrient depletionmodel of Leroy and De Vuyst (15). The latter model leads tomore accurate fitting of the fermentation data. Moreover, aminimum cell concentration for bacteriocin production [X_B] wasintroduced to take into account induction of bacteriocin production(15). Subsequently, the remodeled biokinetic parameters wereused to develop a combined model that is able to predict theeffect of temperature, initial pH, and initial nutrient concentrationon cell growth, sugar metabolism, and bacteriocin activity ofL. sakei CTC 494 in (modified) MRS broth. These factors willfurther enable the prediction of microbial behavior under typicalsausage fermentation conditions.

MATERIALS AND METHODS

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Materials and Methods

Microorganisms and media.
L. sakei CTC 494, a producer of the bacteriocin sakacin K (11),and Listeria innocua LMG 13568, an indicator for the estimationof sakacin K activity, were maintained as described previously(13). Fermentation experiments were performed in (modified)MRS broth. Modifications of MRS broth involved changes in thetotal amount of the complex nutrient source (CNS), i.e. bacteriologicalpeptone (Oxoid, Basingstoke, United Kingdom), meat extract (LabLemco powder; Oxoid), and yeast extract (Merck, Darmstadt, Germany).

Fermentation experiments and sampling.
Fermentations were carried out in a computer-controlled 15-literlaboratory fermentor (BiostatC, B. Braun Biotech International,Melsungen, Germany) containing 10 liters of (modified) MRS broth.Preparation of the fermentor, building of the inoculum, andon-line control of the fermentation process (temperature, pH,and agitation) were performed as described previously (13).Determinations of biomass concentration [X], total lactic acidconcentration [L], residual glucose concentration [S], and bacteriocinactivity in the cell-free culture supernatant [B] were carriedout as described elsewhere (13). Summarizing, biomass (as celldry mass [CDM]) was determined by gravimetry after membranefiltration, lactic acid and glucose were determined by high-pressureliquid chromatography, and bacteriocin activity was estimatedby a twofold critical dilution method. Optical density at 600nm (Uvikon 923; Kontron Instruments, Milan, Italy) was measuredand calibrated against CDM measurements to obtain additionalCDM data (15).

Experiments on the 100-ml scale were performed to investigatethe effect of temperature and pH and of lactic acid (Merck),acetic acid (Merck), and citric acid (as triammonium citrate;BDM Laboratory Supplies, Poole, United Kingdom) on growth rateinhibition.

Titration of MRS broth.
The pH drop caused by acidification due to growth was predictedby establishing an empirical relationship between the lacticacid concentration and the pH of MRS broth. Lactic acid (Merck)was gradually added to 1 liter of MRS broth with an initialpH of 6.95, and the drop in pH was monitored with a pH meter(pH-526; WTW Measurement Systems, Ft. Myers, Fla.).

Modeling procedure.
In a first step, maximum specific growth rates were determinedon a 10-liter as well as 100-ml scale for different conditionsof temperature and pH and at different concentrations of lactic,acetic, and citric acids. Determinations were based on linearregression of optical density measurements during the exponentialpart of the growth curves. The latter experiments permittedus to estimate the parameters of the inhibition functions (equations6 to 8; see below) using Microsoft Excel (version 97). The concentrationof undissociated acid was calculated from the total acid concentrationwith the equation of Henderson-Hasselbalch.

Next, for each 10-liter fermentation experiment, the equations1, 2, 13, 17, and 19 of the model (see below) were integratedwith the Euler integration technique using Microsoft Excel.The parameters needed for the modeling were estimated by manualadjustment until the best fit was obtained. The lag phase wasmodeled as a Heaviside function, which forces the specific growthrate to zero during the duration of this phase (3).

In this way, a combined predictive model was built from a setof 16 fermentations at constant pH, including the fermentationspresented in previous studies (13, 15). The validation was basedon eight independent fermentations performed under a free pHcourse which were not included in the construction of the model,namely: 20°C, pH₀ 5.5, 1.0 [CNS]; 22°C, pH₀ 5.8, 0.7[CNS]; 25°C, pH₀ 6.5, 1.0 [CNS]; 28°C, pH₀ 6.0, 1.0[CNS]; 28°C, pH₀ 6.0, 2.0 [CNS]; 30°C, pH₀ 6.4, 1.0[CNS]; 31°C, pH₀ 6.2, 2.5 [CNS]; and 35°C, pH₀ 6.0,1.0 [CNS].

Validation parameters mean square error (MSE), correlation coefficient(r²), bias factor, and accuracy factor were calculated as describedelsewhere (21, 25). Briefly, the MSE is a measure of variabilityremaining, mainly due to systematic errors and biological variability.Hence, the lower the MSE, the better the adequacy of the modelto describe the data. On the other hand, the higher the valueof the regression coefficient (0 $<=$ r² $<=$ 1), the better the predictionby the model. The bias factor indicates the structural deviationof a model whereas the accuracy factor indicates how close,on average, predictions are to observations.

Modeling of cell growth. (i) Growth equation.
After the lag phase ${lambda}$ (in hours) has finished and the cells haveadapted to their new environment, the production of biomass[X] (in grams of CDM per liter) as a function of time t (inhours) is generally related to the specific growth rate µ(per hour) as follows:

(1)

By taking into account the specific death rate k_d (per hour),meaningful at low pH values and low complex nutrient availability(results not shown), the concentration of living cells [X_v]may be calculated with the following equation:

(2)

At the onset of the active growth phase, the cell populationincreases exponentially as µ is at its maximal value (µ_max).However, µ decreases considerably as the cell concentrationgets denser. Hence, µ equals the maximum specific growthrate µ_max (per hour) multiplied by a self-inhibition function, ${gamma}$ _i (15):

(3)

In turn, µ_max depends on the initial conditions that prevailin the microbial environment. Hence, the parameter µ_maxmay be defined as

(4)

The value of (µ_max)_opt (per hour) corresponds with thevalue of µ_max obtained in MRS broth under optimal conditionsof temperature and pH and in the absence of inhibitory substancessuch as lactic acid or buffer components. It was presumed thatnutrients were not limiting in the earliest stages of growth.

(ii) Initial growth inhibition.
The inhibition function ${gamma}$ ₀ describes the initial inhibition dueto suboptimal temperature ( ${gamma}$ _T) and suboptimal initial pH ( ${gamma}$ _pH0) conditions. Moreover, inhibition due to the initial presenceof lactic acid ( ${gamma}$ _[L]0) and of acetic acid ( ${gamma}$ _[Ac]0) and citricacid ( ${gamma}$ _[Ci]0), originating from the buffer components of MRSbroth, has to be taken into account. In analogy with the ${gamma}$ concept(25), the inhibition function ${gamma}$ ₀ may be expressed as the combinedresult of the individual ${gamma}$ inhibition functions, presuming thatno interaction effects occur among the individual inhibitoryactions:

(5)

The ${gamma}$ functions that describe the growth effect of temperatureT (in degrees centigrade) and initial acidity pH₀ are cardinalfunctions based on the maximum, minimum, and optimum valuesof the studied factor (23):

(6)

(7)

The inhibition due to the presence of organic acid A, i.e.,lactic (L), acetic (Ac), or citric (Ci) acid, is related tothe concentration of undissociated organic acid ([HA], in gramsper liter) as follows (15):

(8)
withn a fitting exponent and [HA]_max the maximum value of [HA] thatstill allows growth.

(iii) Self-inhibition.
Previously, a model was constructed to simulate self-inhibitionof a growing L. sakei CTC 494 culture under pH-stat conditions(15). In this paper, the self-inhibition model was extendedto take into account the effect of free pH. The self-inhibitionfunction ${gamma}$ _i (see equation 3) accounts for the depletion of sugar( ${gamma}$ _[S]), complex nutrients ( ${gamma}$ _[CNS]), and the production of lacticacid ( ${gamma}$ _{[L]_p}):

(9)

Growth inhibition due to the depletion of sugar ( ${gamma}$ _[S]) is givenby the equation of Monod (15):

(10)
withK_S being the Monod constant for substrate S (in grams per liter)and [S] the residual glucose concentration (in grams per liter).The value of K_S was set to 0.01 g/liter (15).

The inhibition function ${gamma}$ _[CNS] may be described by the followingthree-step function (15):

(11)
with[X₁] and [X₂] being the critical biomass concentrations fornutrient inhibition (in grams of CDM per liter) and I₁ and I₂the inhibition slopes (15). The values of [X₁], [X₂], I₁, andI₂ were independent of the pH but were a function of the temperatureand the initial complex nutrient concentration.

The inhibition due to lactic acid production was the resultof the toxic effect of the undissociated lactic acid moleculesproduced, the drop of the pH, and the consequent effect on thedissociation degree of the weak acids:

(12)

Modeling of lactic acid production.
The residual sugar concentration may be calculated from thebiomass production with the equation of Pirt (13, 14):

(13)
with Y_X/S being the cell yield coefficient (ingrams of CDM per gram of glucose) and m_S the cell maintenancecoefficient (in grams of glucose per gram of CDM per hour).For L. sakei CTC 494, Y_X/S and m_S were shown to be dependenton the temperature and pH (13). In analogy with the ${gamma}$ concept(see above), the latter parameters may be described as follows:

(14)

(15)
where (Y_X/S)_optand (m_S)_opt are the maximum values of Y_X/S and m_S, respectively,that can be obtained for L. sakei CTC 494 in MRS broth. Thefunctions ${alpha}$ _pH and ß_pH are isomorphic with equation7, and the function ${alpha}$ _T is isomorphic with equation 6. The requiredminimum and maximum values of temperature and pH are set equalto the limits for cell growth as determined with equations 6and 7, respectively. The function ß_T, which describeshow the maintenance coefficient increases with increasing temperaturestowards a plateau value, is given by

(16)
wherey and z (per degree centigrade) are two fitting coefficients.

The total lactic acid concentration (in grams per liter) maybe calculated from sugar consumption as follows:

(17)
with Y_L/S being the yield coefficient for theproduction of lactic acid (in grams of lactic acid per gramof glucose). The value of Y_L/S is not significantly influencedby the environment and equals 0.99 g of lactic acid per g offermentable sugar.

Modeling of pH drop.
After experimentally establishing the relationship between lacticacid concentration and pH, the pH drop in MRS broth inducedby lactic acid production may be modeled with the followingequation:

(18)
where a₁ and a₂ arefit parameters (in liters per gram) and [L_th] is the theoreticallactic acid concentration needed to obtain a well-defined fermentationpH, starting from an initial pH_i of 6.9 (Fig. 1). In practice,this [L_th] is equal to the hypothetical lactic acid concentration[L₀] needed to reduce the pH from pH_i to the initial pH of thefermentation (pH₀) plus the amount of lactic acid produced byL. sakei CTC 494 during the fermentation ([L]).

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FIG. 1. Titration of MRS broth with externally added lactic acid. [L₀] is the hypothetical lactic acid concentration needed to obtain an initial pH₀, [L] is the amount of lactic acid produced by L. sakei CTC 494, and [L_th] is the theoretical lactic acid concentration needed to obtain a well-defined fermentation pH. Symbols represent experimental data. The solid line is according to the model (equation 18).

Modeling of bacteriocin activity.
The bacteriocin activity in the cell-free culture supernatant[B], expressed in mega-arbitrary units (MAU) per liter, is givenby

(19)
where k_B is the specific bacteriocinproduction (in MAU per gram of CDM), k_inact is the apparentbacteriocin inactivation rate (in liters per gram of CDM perhour), and [X_B] is the minimum biomass concentration for bacteriocinproduction, below which the value of k_B was equal to zero (ingrams of CDM per liter).

In a previous study, k_B was shown to be dependent on the temperatureand pH (13). Interestingly, a ceiling value, (k_B)_max, of thisparameter was noticed. Moreover, the complex nutrients concentrationhad a considerable influence on k_B (15). Hence, k_B was relatedto the temperature, pH, and complex nutrient concentration asfollows:

(20)
with f(pH) being a functionof the pH, ${delta}$ _T an inhibition function for suboptimal temperatures,and ${delta}$ _[CNS] an inhibition function for suboptimal complex nutrientavailability. ${delta}$ _T is isomorphic with equation 6 and ${delta}$ _[CNS] is afractal function of the complex nutrient concentration (seeResults).

The parameter k_inact was a function of temperature and pH only(13):

(21)
where K₀ (in liters pergram of CDM per hour), k₁ (per degree centigrade), and k₂ arefitting coefficients.

RESULTS

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Results

Estimation of model parameters. (i) Modeling of cell growth.
The value of (µ_max)_opt for L. sakei CTC 494 correspondingwith optimal growth was estimated at 0.94 h^-1 (equation 4).The ${gamma}$ functions for temperature, pH, and lactic acid inhibitionare displayed in Fig. 2. The values of the parameters neededin equations 6 to 8 are summarized in Table 1. From the growthinhibition studies in function of the organic acid concentrations,it was found that the most toxic acid, in its undissociatedform, was citric acid ([HCi]_max = 0.4 g per liter), followedby lactic acid ([HL]_max = 2.8 g per liter), and acetic acid([HAc]_max = 7.1 g per liter).

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FIG. 2. Influence of temperature (a), pH (b), and undissociated lactic acid concentration (c) on growth inhibition of L. sakei CTC 494 in MRS broth. Solid symbols indicate experiments on the 10-liter scale, and open symbols indicate experiments on the 100-ml scale. Error bars indicate standard deviations. Solid lines are according to the model (equations 6 to 8).

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TABLE 1. Kinetic data on the parameters for cell growth and sugar consumption (µ_max, Y_X/S, and m_S) concerning the influence of pH, temperature, and the presence of undissociated organic acids for L. sakei CTC 494 in modified MRS broth

The effect of complex nutrient availability and temperatureon the nutrient depletion kinetics was quantified as follows:

(22)
where [CNS] represents the concentration ofcomplex nutrient source, expressed as the factor by which theconcentration of the complex nutrient source of standard MRSbroth was multiplied, and a_T is a dimensionless temperaturecorrection factor. Based on the individual fitting of the valuesof [X₁], [X₂], I₁, and I₂ of the pH-stat fermentations for differenttemperatures in the range from 20 to 37°C, the latter wasempirically modeled as a linear function:

(23)

Based on the entire set of pH-stat fermentations, the specificdeath rate k_d (per hour) was expressed as an empirical functionof the pH and the complex nutrient concentration [CNS]:

(24)

(ii) Modeling of lactic acid production.
The biokinetic parameters Y_X/S and m_S obtained in equation 13were reestimated according to the nutrient depletion model ofLeroy and De Vuyst (15). The values of (Y_X/S)_opt and (m_S)_optwere set at 0.46 g of CDM (g of glucose)^-1 and 0.95 g of glucose(g of CDM)^-1 h^-1, respectively. An overview of the coefficientsneeded to calculate m_S and Y_X/S is given in Table 1.

(iii) Modeling of pH drop.
An experimental relationship between lactic acid concentrationand pH was established, indicating that 12 g of lactic acidper liter was required to achieve a final pH of 4.0. The fitparameters a₁ and a₂ for the modeling of the pH drop were estimatedexperimentally at 0.45 and 0.18 liter g^-1, respectively (Fig.1).

(iv) Modeling of bacteriocin activity.
The specific bacteriocin production k_B was related to the temperature,pH, and complex nutrient concentration according to equation20. The function f(pH), estimated for pH-stat experiments atdifferent pH values (pH 4.5 to 6.5) but at the same temperature(30°C), displayed a discontinuity at pH 5.37 (13):

(25)

The ceiling value of (k_B)_max was estimated at 2.6 MAU (g ofCDM)^-1. The minimum, optimum, and maximum temperature valuesfor bacteriocin production were estimated to be 3, 24, and 32°C,respectively. ${delta}$ _[CNS] is the normalized inhibition function forthe description of the influence of the complex nutrient sourceon the specific bacteriocin production. The latter factor wasdetermined at 25°C and a controlled pH of 5.5 as follows(Fig. 2):

(26)

The parameter k_inact was a function of temperature and pH only,the kinetic coefficients K₀, k₁, and k₂ needed to calculatek_inact (see equation 21) were estimated at 4 x 10^-7 liters (gof CDM)^-1 h^-1, 0.115°C^-1, and 1.427, respectively.

Validation of the model.
A series of fermentations were performed to test the validityof the proposed model (Fig. 3). In a first step, simulationswere made to predict cell growth, sugar consumption, lacticacid production, pH drop, and bacteriocin activity for eightdifferent sets of fermentation conditions (i.e., different conditionsof temperature, initial pH, and complex nutrient concentration).Next, the fermentations were performed experimentally to monitorthe relevant experimental data.

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FIG. 3. Prediction of cell growth ( ${circ}$ , or • when calculated from OD measurements), glucose consumption ( ${diamond}$ ), lactic acid production ( ${diamondsuit}$ ), pH drop ( ${triangleup}$ ), and bacteriocin activity ( ${square}$ ) by L. sakei CTC 494 in (modified) MRS broth at 20°C, pH₀ 5.5, 1.0 [CNS] (a); 22°C, pH₀ 5.8, 0.7 [CNS] (b); 25°C, pH₀ 6.5, 1.0 [CNS] (c); 28°C, pH₀ 6.0, 1.0 [CNS] (d); 28°C, pH₀ 6.0, 2.0 [CNS] (e); 30°C, pH₀ 6.4, 1.0 [CNS] (f); 31°C, pH₀ 6.2, 2.5 [CNS] (g); and 35°C, pH₀ 6.0, 1.0 [CNS] (h). Symbols represent experimental data; solid lines are predicted by the model. dag, decagram.

The performance of the predictive model was estimated by comparingthe observed experimental data with the predicted ones (Fig.4). In Table 2, a validation overview is shown for the totaldata set as well as for the data of the validation experimentsonly. The regression coefficient for the entire data set washigh for µ_max (0.974) and [X]_max (0.957), but somewhatlower for [B]_max (0.920). The bias factor was close to 1 forµ_max, [X]_max, and [B]_max, indicating that the observedvalues were, on an average, not systematically lower or higherthan the predicted values. The accuracy factors for µ_max,[X]_max, and [B]_max indicated that the observed and predictedvalues for the entire data set differed, on average, by 5, 10,and 17%, respectively. The low accuracy of bacteriocin activitypredictions was inherent to the twofold dilution method thatwas used to measure bacteriocin activity levels. At higher activitylevels, uncertainty about the bacteriocin activity increased.

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FIG. 4. Correlation between experimental and predictive values of µ_max, the maximum biomass concentration [X]_max, and the maximum bacteriocin activity [B]_max. Circles represent the entire data set, and squares represent the validation experiments only.

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TABLE 2. Validation data for µ_max, the maximum biomass concentration [X]_max, and the maximum bacteriocin activity in the cell-free culture supernatant [B]_max of the entire data set and of the validation data set only

DISCUSSION

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Discussion

Predictive microbiology is a field of increasing importance.Predictive models help us to understand when foods become spoiledor what risk is associated with certain food products for outbreaksof food-borne pathogens or food poisoning. Alternatively, predictivemodels will be indispensable to estimate the beneficial effects,such as bacteriocin production, exopolysaccharide formation,aroma development, and probiotic effects, of deliberately addedstarter cultures or cocultures in both fermented foods (e.g.,cheese and sausage) and nonfermented food products (e.g., modified-atmosphere-packagedcooked meat products). This will be of particular importancein view of the food-health relationship (natural and healthyproducts, functional foods, etc.) put forward by both the consumerand the food processor.

Yet, information on the prediction of beneficial propertiesof desirable and healthy microbes in food ecosystems is scarce.Moreover, studies that report on beneficial microbes frequentlymake use of empirical models based on polynomial regressionand surface response methodologies (7, 8). The main disadvantagesare the difficult physiological interpretation of the parametersused, the nondynamic and nonindependent behavior of the mathematicalrelationships established, and the nonmechanistic behavior ofthe equations used.

The modeling approach presented in this study permitted to obtaina reliable simulation of the functional behavior of L. sakeiCTC 494 as a potential novel sausage starter culture under selectedconditions of pH, temperature, and complex nutrient availability.The model for cell growth is significantly more complex thanclassical growth models such as the logistic equation or theGompertz equation (26). However, it offers considerable advantagesover classical growth models, such as the ability to describegrowth under free pH and a more mechanistic insight into theeffect of the environment on growth inhibition. The combinedmodel makes a clear distinction between the individual effectsof each environmental factor that has been included in the modeland is able to quantify these effects separately. Moreover,as has been demonstrated previously with the nutrient depletionmodel (15), a closer agreement with the experimental data wasobserved when using this extended nutrient depletion model,compared to fitting with the logistic model used by Leroy andDe Vuyst (13, 14). As a result, some shifts in parameterizationwere observed. For instance, the modeled minimum, optimum, andmaximum values of pH and temperature for cell growth were slightlydifferent from the ones found by Leroy and De Vuyst (13). Thiswas partially due to the extension of the data set but mainlyto the substitution of the original logistic growth equationwith the nutrient depletion model, which resulted in a recalculationof the µ_max values (15). They were, however, still inagreement with the physiologically determined growth limits(i.e., 3.9 < pH < 8.6 and 0 < T < 44°C).

The predictive performance of the model was evaluated. The predictionsfor the values of µ_max were excellent, whereas the predictionsfor [X]_max and [B]_max were still satisfactory. Moreover, thepredicted lines of cell growth, sugar consumption, lactic acidproduction, pH drop, and bacteriocin activity were in good agreementwith the experimental data.

The validated predictive model permits us to simulate bacterialbehavior under a defined set of environmental conditions. Inthis way, the effect of changes in fermentation technology maybe studied. For example, a temperature between 20 and 30°Cis required for good bacteriocin production by L. sakei CTC494. At higher temperatures, bacteriocin activity levels woulddecrease considerably. In contrast, cell growth and acidificationare faster at temperatures above 30°C.

Despite the fact that a fermentor is a very different environmentthan a real sausage, it is assumed that bacterial growth andbacteriocin activity in a sausage take place in the water phaseor at least near the surface of the meat particles. Also, afermentor is a powerful device to study the kinetics of microbialbehavior, making use of a simulation medium. Although MRS brothis distinct from a real food environment, the information obtainedin this paper may be very useful for the selection of suitablestarter cultures for a particular fermentation process and isa first step in the optimization of food fermentation processesand technology as well. Some other factors that will have tobe taken into account are the presence of spices in the sausagebatter, interactions with the background microflora, diffusionlimitations in the meat matrix, substrate and oxygen gradients,and bacteriocin activity losses due to adsorption to fat andmeat particles.

ACKNOWLEDGMENTS

We acknowledge financial support from the Research Council ofthe Vrije Universiteit Brussel, the Fund for Scientific Research—Flanders(FWO), the Institute for the Promotion of Innovation by Scienceand Technology in Flanders (IWT), in particular the STWW project"Functionality of novel starter cultures in traditional fermentationprocesses," and the European Commission (grant FAIR-CT97-3227).F.L. was supported by a grant from the IWT (Ph.D. bursary) andthe FWO (postdoctoral fellowship). L. sakei CTC 494 was kindlyprovided by M. Hugas (Institut de Recerca i TecnologíaAgroalimentáries, Centre de Tecnología de la Carn,Monells, Spain).

FOOTNOTES

* Corresponding author. Mailing address: Research Group of Industrial Microbiology, Fermentation Technology and Downstream Processing, Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium. Phone: 32 2 6293245. Fax: 32 2 6292720. E-mail: ldvuyst{at}vub.ac.be.

REFERENCES

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