Modelling the fate of Listeria monocytogenes during manufacture and ripening of smeared cheese made with pasteurised or raw milk

International Journal of Food Microbiology 145 (2011) S31–S38

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International Journal of Food Microbiology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j f o o d m i c r o

Modelling the fate of Listeria monocytogenes during manufacture and ripening of smeared cheese made with pasteurised or raw milk M.S. Schvartzman a,b, A. Maffre c, F. Tenenhaus-Aziza c, M. Sanaa c,d, F. Butler b, K. Jordan a,⁎ a

Teagasc Food Research Centre, Moorepark, Fermoy, Co. Cork, Ireland Biosystems Engineering, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Ireland National Cross Industry Center for the French Dairy Sector, Paris, France d French Food Safety Agency, Maison-Alfort, France b c

a r t i c l e

i n f o

Article history: Received 18 March 2010 Received in revised form 18 November 2010 Accepted 18 November 2010 Keywords: Listeria monocytogenes Cheese Dynamic conditions Modelling

a b s t r a c t The dynamics of the physicochemical characteristics of foods help to determine the fate of pathogens throughout processing. The aim of this study was to assess the behaviour of Listeria monocytogenes during cheesesmaking and ripening and to model the growth observed under the dynamic conditions of the cheese. A laboratory scale cheese was made in 4 independent replicates from pasteurised or raw cow's milk, artificially contaminated with L. monocytogenes. No growth of L. monocytogenes occurred during raw milk cheese-making, whereas growth did occur in pasteurised milk. During ripening, growth occurred in raw milk cheese, but inactivation occurred in pasteurised milk cheese. The behaviour observed for L. monocytogenes was modelled using a logistic primary model coupled with a secondary cardinal model, taking into account the effect of physicochemical conditions (temperature, pH, water activity and lactate). A novel statistical approach was proposed to assess the optimal growth rate of a microorganism from experiments performed in dynamic conditions. This complex model had an acceptable quality of fit on the experimental data. The estimated optimum growth rates can be used to predict the fate of L. monocytogenes during cheese manufacture in raw or pasteurized milk in different physicochemical conditions. The data obtained contributes to a better understanding of the potential risk that L. monocytogenes presents to cheese producers (growth on the product, if it is contaminated) and consumers (the presence of high numbers) and constitutes a very useful set of data for the completion of chain-based modelling studies. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Listeria monocytogenes is a widely distributed foodborne pathogen that causes listeriosis with a mortality rate of 30% in vulnerable groups like the young, old and the immunocompromised. It has been responsible for several foodborne outbreaks of listeriosis reported in Europe, USA and Japan (Dalton et al., 1997; Makino et al., 2005; Dawson et al., 2006; Swaminathan and Gerner-Smidt, 2007). L. monocytogenes can be present in all foods, particularly milk products (Gaya et al., 1996; Harvey and Gilmour, 1992), animal products (Dillon et al., 1994; Samelis and Metaxopoulos, 1999; Medrala et al., 2003; Soultos et al., 2003; Miettinen and Wirtanen, 2005; Thévenot et al., 2005), ready-to-eat foods (Lianou and Sofos, 2007), and vegetables (Little et al., 2007). Due to the increasing concern about the occurrence of this pathogen in foods and food processing facilities, its persistence and survival in the cheese processing environment (Silva et al., 2003; Chambel et al., 2006) has been the subject of study in recent years. Such contamination of

⁎ Corresponding author. Tel.: + 353 2542451; fax: + 353 2542340. E-mail address: [email protected] (K. Jordan). 0168-1605/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ijfoodmicro.2010.11.032

milk or cheese, due to a lack of hygiene, resistance of some strains to adverse conditions (Mathew and Ryser, 2002) or biofilm formation (Poimenidou et al., 2009), may result from cross contamination at the farm or the processing facility, at the later stages of cheesemaking, whether the milk is pasteurised or not (Aziza et al. 2006). Studies on the growth of L. monocytogenes during cheesemaking are limited. It is thought that the acidification process experienced during cheesemaking can inhibit the growth notably for soft cheese. Alternatively, the milk may serve as an ideal medium for pathogen growth given the temperature and water activity of the process. During the ripening of the cheese, as during manufacture, intrinsic characteristics of the matrix and its physicochemical properties (such as pH, temperature, lactic acid content and water activity) will govern the behaviour of the microorganisms, including pathogens. The particular dynamism of these parameters makes this behaviour very unpredictable. Food safety depends on studies of microbial physiology to increase knowledge on pathogen behaviour. Physiological studies contribute to a better understanding of the behaviour of pathogens, but food predictive microbiology goes a step further by modelling this behaviour by means of mathematical equations and thus by establishing reproducible patterns that can be used to predict microbial

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behaviour over a range of conditions. An initial step before simulating the behaviour of a microbiological agent in specific conditions is to assess the values of the model parameters using mathematical equations. These equations comprise primary and secondary models (which are based on the actual data and mathematical manipulations of the data, respectively) that represent efficient tools to quantify growth kinetics and the effect of environmental parameters on the growth kinetics of foodborne pathogens. At a later stage, predictive models can be used to improve several aspects of food safety such as identification of critical control points in HACCP systems and improvement in Food Safety Objectives for specific products. In order to carry out these modelling exercises, more data on bacterial behaviour in food chains is needed. In this study we aimed to assess the behaviour of L. monocytogenes throughout the complete processing of a laboratory-scale smeared cheese from its manufacture to the end of the ripening, and to model the growth and survival observed as a function of the physicochemical properties measured. Cheeses were made in 4 independent replicates, in order to take into account the variability and the error of the measurements. We fitted a logistic primary growth model (Rosso et al., 1996) coupled with a secondary cardinal growth model (Augustin et al., 2005) on the experimental data collected in dynamic physicochemical conditions to assess the optimum growth rate of L. monocytogenes in pasteurised and raw milk, during cheesemaking and ripening, in the core and on the rind. 2. Materials and methods 2.1. Strain and culture preparation The strain of L. monocytogenes used was strain C5, serotype 4b isolated from cow faeces. The strain was maintained in tryptic soy broth (TSB; Becton Dickinson Co., USA) with 25% vol/vol glycerol at −80 °C. The strain was activated by two consecutive subcultures overnight in TSB at 37 °C in 4 replicates. The overnight culture (from the second subculture) was diluted to achieve an initial inoculation level of approximately 102 colony forming units per millilitre (cfu/ml) of milk. 2.2. Milk Experiments were carried out in pasteurised whole milk and in raw milk. Fresh homogenised pasteurised milk was acquired in a local store on the day of the experiment. The raw milk was obtained from the farm at Moorepark Food Research Centre (Fermoy, County Cork, Ireland). The fat, protein and lactose concentrations of the milks were measured by infrared analysis (Milkoscan 605, Foss Electric, Denmark), and the raw milks were standardised in order to maintain the same fat-to-protein ratio (PFR) as the pasteurised milk. The total background flora and total coliforms were measured by plating the milk on milk plate count agar (Merck KGaA, Darmstadt, Germany) and VRBL Agar (Merck KGaA, Darmstadt, Germany), respectively. Plates were incubated at 30 °C for 3 days and 18 h, respectively. 2.3. Cheesemaking and ripening Milk (2 l) at 30 °C was inoculated with starter culture at 0.02% (F-DVS CHN-19, CHR Hansen, Ireland Ltd; containing Lactococcus lactis subsp. cremoris, L. lactis subsp. lactis, L. lactis subsp. diacetylactis and Leuconostoc mesenteroides subsp. cremoris) and a single strain of L. monocytogenes (at approximately 5×102 cfu/ml). When the pH was at 6.5, double strength animal rennet (diluted 10-fold in sterile distilled water) was added at a rate of 0.18 ml/l. After testing the gelled milk for firmness (~45 min after the addition of the rennet) the curd was cut in cubes and after 10 min the curd–whey mixture was cooked from 30 °C to 36 °C at a rate of +1 °C every 5 min, stirring continuously. The curd was then put

into moulds (top diameter 89 mm, bottom diameter 82 mm and height 83 mm, Moorlands Cheeemakers Limited, UK), turned every 30 min for 3 h and then stored at 4 °C for up to 12 h. Brining was carried out the following day by immersing the cheeses in 23% NaCl solution containing 200 μg/l of Ca2+ for 1 h. The smearing solution, which is used by a local cheese-maker, contained a mixture of bacteria and yeast. Following brining, the cheeses where left to drain for 30 min and manually smeared. After smearing, the cheeses were ripened at 90% humidity and 13 °C for 2 weeks followed by a further two weeks at 8 °C.

2.4. Sampling plan, enumeration and biochemical analyses During cheesemaking, samples were taken every hour and analysed for L. monocytogenes, moisture, pH, water activity, temperature and L-lactic acid content. L. monocytogenes counts in milk were estimated by direct plate count enumeration, diluting in maximum recovery diluent (MRD, Oxoid, Basingstoke, U.K.) and spread-plating onto ALOA agar plates (Agosti & Ottaviani Listeria Agar, LAB M Lancashire, U.K), incubated at 37 °C for 24–48 h. For curd and cheese, 1 or 10 g, respectively, were diluted 10-fold in 2% tri-sodium citrate, homogenised on a Stomacher for 4 min, further diluted in MRD, as necessary, and plated on ALOA agar plates (incubated at 37 °C for 24– 48 h). In order that the counts from liquid and solid matrices could be compared, the results were expressed as counts per gram of dry weight (gdw). Moisture and dry matter were measured by standard methods (IDF Standard 4A and IDF Standard 21B for milk). The pH was measured every 10–20 min using the British Standard for pH determination of cheese samples (BS770:5:1976) with an Orion pH meter model 420A. Temperature was measured continuously with a temperature probe. The water activity was measured in an AquaLab Series 3T (Labcell, Hampshire, U.K.), sampling 5 ml of milk, or a portion of curd or cheese as described in the manufacturer's instructions. Total L-lactic acid was measured with an L-Lactic acid kit (Boehringer Mannheim, Darmstadt, Germany). Ripening of the cheese was carried out over a period of 28 days during which samples were taken every 4 days; each cheese was sampled both in the core and on the rind by weighting 1 g from the core or rind (1–2 mm thick) and diluting it 10-fold with 2% tri-sodium citrate in a Stomacher bag. The sample was then homogenised in a Stomacher and diluted for plate count enumeration. L. monocytogenes counts, moisture, dry matter, pH, L-lactic acid and water activity were measured at each sampling point as described above.

2.5. Modelling of the data The data were classified in two groups. The first concerned the cheesemaking and corresponded to times 0 h to 5 h. The second group corresponded to the ripening period from 5 h to 672 h (day 28). The ripening period was in turn subdivided into two sub-groups: core and rind. The data were modelled with primary and secondary models. Primary models describe growth or decay kinetics over time. The main parameter of such a model is the maximum growth rate (μmax) of the bacteria (Perni et al., 2005). Secondary models are used to estimate the effect of the physicochemical parameters on the growth rate of the bacterial populations. It is assumed that when the physicochemical parameters affect the behaviour of the organism, quantification of these effects can be estimated by explaining the maximum growth rate as a function of these parameters. The parameters used were temperature in °C (T), water activity, pH and undissociated lactic acid in g/l− 1 ([AH]). A logistic primary growth model without delay coupled to the secondary cardinal model with gamma concept was adjusted on the sets of experimental data, when growth was observed.

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Eq. (1) describes the primary growth model (Rosso et al., 1996).
 dy YðtÞ ðtÞ = μ max ðtÞ⋅YðtÞ⋅ 1− dt Ymax

μ max = μ opt CM2 ðTÞCM1 ðpHÞSR1 ðaw ÞSRð½AHÞξðT; pH; aw ; ½AHÞ

ð2Þ

where μ opt is the optimum growth rate (in h− 1), aw refers to water activity and [AH] to lactic acid. CMn ðXÞ =

ðXopt −Xmin Þn−1

n

Xopt −Xmin

ðX−Xmax ÞðX−Xmin Þn    h io X−Xopt − Xopt −Xmax ðn−1ÞXopt + Xmin −nX

ð3Þ for X = T or pH, !n ðX−Xmin Þ ; ðXopt −Xmin Þ

SRn ðXÞ =

ð4Þ

for X = aw SRðAHÞ = 1−

½AH ; MICu

ð5Þ

where MICu is the Minimal Inhibitory Concentration of the undissociated acid for L. monocytogenes, fixed to 0.4 g/l (Coroller, 2005). Xmin, Xopt and Xmax, are the cardinal values of L. monocytogenes (Table 1). For all the functions of the secondary model, if the parameter X is not in the interval [Xmin, Xmax] the function is equal to zero. In the same way, the lactic acid module SR is equal to zero if the lactic acid concentration is greater than the MICu value. 8 ψ≤0:5 < 1; ξ = 2ð1−ψÞ; 0:5bψb1 : 0; ψ≥1

i

φðiÞ 2∏ ½1−φð jÞ j≠i

Xopt −X Xopt −Xmin

Mean

Tmin (°C) Topt (°C) Tmax (°C) pHmin pHopt pHmax aw min aw opt

− 1.72 37 45.5 4.71 7.1 9.61 0.913 0.997

were spread over time and weak enough to subsequently favour an immediate adaptation of L. monocytogenes. Thus, the bacteria were considered in its exponential phase during this period. The objective of the modelling exercise was to assess the optimum growth rate μopt of a non linear first order differential equation with non constant coefficients. As no analytical solution was available for this type of dynamic system, the numerical resolution was achieved by using the Model procedure of the SAS software (SAS Institute Inc., Cary, NC, USA. V9.1), which allows fitting this type of complex timedependent model on data collected in dynamic conditions. The equation bias problem was solved using the full information maximum likelihood (FIML) estimation method (Amemiya 1977, SAS Institute Inc., 2004). For each replicate, the population size at each given sampling time was provided to the Model procedure of SAS. Eqs. (1)–(5), and additional functions describing the evolution of each physicochemical parameter over time were also provided. These functions were obtained by linear interpolation between the measured data (results not shown). The goodness of fit of the model on the data was measured with the R-squared value. To quantify the impact of lactic acid, the model was also adjusted without the lactic acid component. A normal distribution was adjusted to the optimum growth rates obtained for each replicate from a same group/sub-group. Simulations of the behaviour of L. monocytogenes were then performed using observed physicochemical parameters and the corresponding assessed optimum growth rate. Predictions were compared graphically to the experimental data for some replicates. 3. Results

The percentages of fat, protein and lactose for the milks used, together with the total counts and coliform bacteria counts can be seen in Table 2. The PFR of the milks was 0.90 and 0.93 for pasteurised and raw milk, respectively. The total bacterial counts in both milk types were within the acceptable ranges (Council Directive 92/46/EEC). 3.2. Cheesemaking

where φð X Þ =

Parameters

3.1. Milk quality ð6Þ

where ψ= ∑

Table 1 Cardinal temperatures (Tmin, Topt, and Tmax), cardinal pHs (pHmin, pHopt, and pHmax) and cardinal aw values (aw min, aw opt, and aw max) of L. monocytogenes estimated in liquid microbiological media, used for the analyses of the data (Augustin et al, 2005). T: temperature, and aw: water activity.

ð1Þ

where μmax(t) is the maximum growth rate (in h− 1), Y(t) the bacterial population at time t, expressed in log cfu/gdw and Ymax is a parameter that represents the hypothetical maximum population of L. monocytogenes in milk or cheese. It was fixed to 108 cfu/gdw. The cardinal secondary growth model assessing the maximum growth rate as a function of physicochemical parameters was adapted from Augustin et al., 2005. The same notations were kept. The maximum growth rate (Eq. (2)) was expressed as the multiplicative effect between the optimum growth rate μopt (the growth rate experienced at optimum conditions) and the modular functions of the different physicochemical parameters. The modular functions of the temperature, pH, and water activity are given in Eqs. (3) and (4). The modular function for L-lactic acid (Le Marc et al., 2001) is given in Eq. (5). The interaction terms between the physicochemical parameters are given by Eq. (6).

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!3 ; X = T; pH or aw and φð X Þ = 1−SRðXÞ; X = ½AH

The model was fitted where environmental conditions allowed the growth of L. monocytogenes (Xmin b X b Xmax for X = T, pH or aw and [AH] b MICu). Variations in pH, from one sampling point to another

The water activity of the milk, curd and cheese during the cheesemaking was 0.998–0.999 in both milk types, which is considered to be optimal for L. monocytogenes. The temperature ranged from 10 °C to 36 °C although the majority of the time the temperature was 30 °C. The pH decreased during this period on average from 6.73 (±0.02) to 6.2 (±0.08) in pasteurised milk and from 6.69 (±0.01) to 6.34 (±0.12) in raw milk. The L-lactic acid was 0.0008 g/l in pasteurised milk and 0.001 g/l in raw milk. At the end of the cheesemaking

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Table 2 Compositional analysis and bacterial quality of the milk used in the study.

Pasteurised Raw

Fat (%)

Protein (%)

Lactose (%)

Total counts (cfu/ml)

Coliforms (cfu/ml)

3.72 3.65

3.36 3.41

4.71 4.82

35 6 × 103

0 45

(approximately 5 h), the levels of L-lactic acid were 0.16 g/l (±0.03) in pasteurised milk cheeses and 0.07 g/l (±0.007) in raw milk cheeses (data not shown). Pasteurised milk was found to favour the growth of L. monocytogenes during cheesemaking. Initial average numbers in pasteurised milk were 3.42 log cfu/gdw (±0.16 log cfu/gdw, 330 cfu/ml). Growth was observed with no lag phase and within the first 5 h of cheesemaking there was a 2.02 log increase (±0.14 log cfu/gdw, Fig. 1A). In raw milk, the initial average concentration of L. monocytogenes was of 3.56 log cfu/gdw (± 0.04 log cfu/gdw, 430 cfu/ml) (Fig. 1B). Considering growth to have occurred when greater than a 0.5 log increase in numbers was observed (this criteria has been used in other studies: Koutsoumanis and Sofos, 2005; Skandamis et al., 2007) there was no growth of L. monocytogenes during cheesemaking with raw milk.

3.3. Ripening During the ripening period, cheeses made with pasteurised milk showed a decrease in numbers of L. monocytogenes that was more marked on the rind (Fig. 2B). In raw milk, for both core and rind, there was a 2 log cfu/gdw increase during the first 4 days (100 h), followed by a survival/decline period that lasted until the end of the ripening (Fig. 2). During this period the pH dropped to 4.7 and 5.0 in pasteurised and raw milk cheeses, respectively, in a period of 6–9 days. In the following 20 days of ripening the pH increased in all cases. The increase was higher in the rind of both milk types (Fig. 3). The water activity reached minimum levels of ~0.78 on the rind of both cheese types (0.75 in raw milk cheese and 0.79 in pasteurised milk cheese). The water activity was lower at the end of ripening on the rind than in

the core (pasteurised: 0.815 ± 0.05; raw: 0.892 ± 0.01). L-lactic acid levels were higher in pasteurised milk, having reached a maximum of 0.35 g/l after 6–9 days of ripening. Following that, the L-lactic acid concentration decreased to ca. 0.22 g/l. In raw milk, L-lactic acid levels went from 0.1 g/l to 0.2 g/l in the 28 days of ripening. Table 3 summarises the time, temperature, pH and water activity at each step of the cheese manufacture.

3.4. Modelling results The following hypothetical example shows how the model was applied for a given set of environmental conditions: let T = 13 °C, pH = 5.59, aw = 0.972 and [AH] = 0.0256 g/l. According to Eqs. (3)–(5) in these environmental conditions, the modular functions CM2(T) = 0.213, CM1(pH) = 0.608, SR1(aw) = 0.708 and SR([AH]) = 0.995. The value of the function Ψ from Eq. (6) is equal to 0.36 and the interaction term ξ is equal to 1, which means that there is no interaction between parameters in this example. Finally, the maximum growth rate can then be calculated using Eq. (2): μ max = μ opt × 0.213×0.608× 0.708×0.995=μopt×0.091, which means that, in these conditions, the growth of L. monocytogenes is eleven times slower than at optimal conditions. The assessment of the μopt was completed for the periods where subsequent growth was observed. When growth was not observed (during cheesemaking with raw milk and during ripening with pasteurized milk), the model could not be fitted and no optimal growth rate was assessed, even if the environmental conditions theoretically allowed growth according to the model (see discussion section). Regarding the experimental results presented above, the optimum growth rates were assessed for the ripening period of the cheese made from raw milk and for the cheesemaking phase of the cheese made from pasteurized milk. For each case, the optimum growth rate was assessed for each replicate and a normal distribution was adjusted on the results.

A 6.0

log10 cfu/gdw

A log10 cfu/gdw

6

5

4

5.5 5.0 4.5 4.0 3.5 3.0 5

100

195

290

385

480

575

670

480

575

670

hours 3 0.0

1.0

2.0

3.0

4.0

5.0

hours

B 6.0

log10 cfu/gdw

B log10 cfu/gdw

6

5

4

5.5 5.0 4.5 4.0 3.5 3.0 5

3

100

195

290

385

hours 0

1

2

3

4

5

hours Fig. 1. Behaviour of L. monocytogenes during cheesemaking in pasteurised (A) and raw (B) milk. Four replicates are represented (interpolation has a visual interest only).

Fig. 2. Behaviour of L. monocytogenes during ripening in the core and on the rind of pasteurised (○) and raw (●) milk cheeses during ripening. A = core; and B = rind. Averages are represented with Standard Deviation as error bars (interpolation has a visual interest only).

M.S. Schvartzman et al. / International Journal of Food Microbiology 145 (2011) S31–S38

A - core

B - Core

7.0

1.000

6.5

0.950

6.0

0.900

aw

pH

S35

0.850

5.5 0.800 5.0

0.750 0.700

4.5 5

100

195

290

385

480

575

5

670

100

195

290

hours

A - rind

480

575

670

B - rind

7.0

1.000

6.5

0.950 0.900

aw

6.0

pH

385

hours

5.5

0.850 0.800

5.0

0.750

4.5

0.700 5

100

195

290

385

480

575

5

670

100

195

hours

290

385

480

575

670

hours

C - core lactic acid (g/l)

0.4 0.3 0.2 0.1 0.0 5

100

195

290

385

480

575

670

hours

C - rind lactic acid (g/l)

0.4 0.3 0.2 0.1 0.0 5

100

195

290

385

480

575

670

hours Fig. 3. pH (A), water activity, aw (B) and L-lactic acid (C) change with time in the core and rind of pasteurised (○) and raw (●) milk cheeses during ripening. Averages are represented with Standard deviation as error bars (interpolations have a visual interest only).

Optimum growth rates during cheesemaking of cheese made with pasteurised milk and the associated R2 values are given in Tables 4A and 4B. The average and the standard deviations of the normal distribution were 1.20 ± 0.01, with the lactic acid and 1.19 ± 0.01 without lactic acid, respectively. According to the Student test, the optimal growth rates with and without lactic acid were not significantly different. The model predictions compared with the observed data are shown in Fig. 4. The optimum growth rate assessed during the ripening period for the core of the cheese made from raw milk was assessed using the

experimental data of the first 216 h of ripening since the water activity after the 216 h was not suitable for growth (for 3 of the 4 replicates). For the rind, the first 120 h were used for the same reasons. Results of optimum growth rates and associated R² values are given in Tables 5A and 5B. For the core, the average and the standard deviations of the normal distribution were, respectively 0.18± 0.003 using the lactic acid component, and 1.18 ± 0.003 without. For the rind, these values were 0.17± 0.002 using the lactic acid component and 0.16 ± 0.002 without (not significantly different with the Student test). The predictions of the model are compared to observations in Fig. 5.

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Table 3 Approximate time, temperature, moisture and pH at which each step of the cheesemaking and ripening process took place. Time (h)

Step

Temperature (°C)

Moisture (%)

pH

0 0.30 3 3.45 4 4.4 4.4–8 8–360 360–700 360–700

Milk incubation Starter addition Rennet addition Cutting of the gel Start cooking Moulding Turning Ripening Ripening — pasteurised Ripening — raw

10 20 30 30 30–36 36 36–22 13 8 8

88 88 71 67 65 62 62–60 43 23–90 18–85

7–6.8 7–6.8 6.55 6.45 6.45 6.3 6.3–5.2 5.2–5.0 6.2–4.7 6.7–5

4. Discussion In this study, we observed that L. monocytogenes grew during the manufacture of pasteurised milk cheese, whereas during the manufacture of raw milk cheese, no growth of the pathogen occurred. Most studies reporting growth of L. monocytogenes during cheesemaking are inaccurate since on the same graph they plot numbers in milk as cfu/ml and in cheese as cfu/g (Ryser and Marth, 1987a; Ryser and Marth, 1987b; Papageorgiou and Marth, 1989; Hicks and Lund, 1991; Morgan et al., 2001; Millet et al., 2006). Thus, the approximately 10-fold concentration during curd formation is not accounted for. This results in an apparent 1-log increase in numbers that is actually due to concentration of the cells and not to growth. In this study, we have used the innovative approach of expressing all numbers in the same units (CFU/g dry weight) and therefore counts from milk and cheese can be directly compared on the same graph, and any increase observed is a real increase in numbers. In contrast to cheesemaking, during ripening L. monocytogenes grew in raw milk cheese but was inactivated in pasteurised milk cheese. In line with these observations, Mathew and Ryser (2002) observed that heat-injured cells of L. monocytogenes were recovered with higher rates in heat-treated or pasteurised milk than in raw milk. In another study (Feresu and Nyati, 1990), E. coli was found to grow to higher numbers in pasteurised fermented milk than in raw milk. Morgan et al. (2001) also found that L. monocytogenes survived the ripening of soft lactic cheese made with raw milk, while Margolles et al. (1997) found that L. monocytogenes was inactivated during the manufacture, ripening and storage of an artisanal acid-coagulated cheese made with pasteurised milk. The secondary growth model failed to predict stagnation during cheesemaking with raw milk and inactivation during ripening with pasteurized milk, which suggests that other environmental parameters not accounted for in this model are influencing the behaviour of L. monocytogenes under these conditions. This behavioural diversity between raw and pasteurised milk cheeses could be accounted for by the background flora of raw milk, or a combination of other limiting environmental factors not accounted for, such as acetic or other organic acids produced by the background flora. During cheesemaking, both water activity and temperature were optimal for the growth of

Table 4B Estimated μopt values for 4 smeared cheese replicates in pasteurised milk during cheesemaking, for the model without lactic acid. The goodness of fit is indicated by the R-square value (R²). Replicate

μopt

R2

1 2 3 4

1.24 1.18 1.05 1.28

0.91 0.85 0.79 0.88

L. monocytogenes. The difference between the two types of milk concerned mainly pH and lactic acid concentration. In pasteurised milk cheese the final lactic acid concentration was 0.16 g/l while in raw milk cheese it was 0.07 g/l. Thus, growth and lactic acid production by the starter culture was probably inhibited by the 3-log higher counts for background flora in the raw milk, parameter not included in the secondary growth model. During ripening, the decrease in lactic acid observed during the last days of ripening in pasteurised milk cheeses could be accounted for by the development of bacteria present in the surface-smear, known to use L-lactate as a source of energy (Mounier et al., 2007). Available MICu values for undissociated lactic acid calculated for L. monocytogenes vary from 0.72 g/l (Le Marc et al., 2001) to about 2 g/l (Tienungoon et al., 2000) in broth. In this study, the undissociated lactic acid concentrations in both cheeses were below this range of MICu and the optimal growth rate assessed in both cheesemaking and ripening was not significantly different when using lactic acid or not in the model. However, a synergistic effect of higher lactic acid and other inhibitory conditions in the cheese (like other acids, or environmental adaptation), not accounted for in the secondary growth model, could have contributed to this different behaviour between raw milk cheese and pasteurised milk cheese. This suggests that additional experiments should be performed to identify parameters involved in this phenomenon. The optimum growth rate of L. monocytogenes during cheesemaking with pasteurised milk cheese had an average value of 1.20 h− 1. During ripening, there was an unavoidable loss of moisture on the rind of the cheeses, and a subsequent increase in salt concentration produced the lowering of the water activity values. The minimum water activity that supports growth of L. monocytogenes is 0.913 (Augustin et al., 2005). L. monocytogenes grew during approximately the first 150 and 216 h of ripening in the core and rind, respectively, in raw milk cheeses. The populations then either remained constant or decreased. Incorporating lactic acid data to the ripening model, the optimum growth rate could be assessed with average values of 0.18 h− 1 and 0.16 h− 1, for the core and the rind, respectively. These values were quite realistic since the literature reported an optimum growth rate of 0.21 h− 1 (Augustin et al., 2005) for L. monocytogenes in milk products. This varying growth rate from cheesemaking to ripening may be explained by the different matrix type, since liquid media allow a better diffusion of nutrients than solid media where bacteria can

Table 4A Estimated μopt values for 4 smeared cheese replicates in pasteurised milk during cheesemaking, for the model with lactic acid. The goodness of fit is indicated by the R-square value (R²). Replicate

μopt

R2

1 2 3 4

1.25 1.19 1.06 1.29

0.91 0.86 0.79 0.87

log cfu/gdw

6 5 4 3 0

1

2

3

4

5

hours Fig. 4. Predictions of the model versus observed data of L. monocytogenes growth during cheesemaking of pasteurised milk cheeses. Four replicates are represented with vertical error bars and predicted growth (—).

M.S. Schvartzman et al. / International Journal of Food Microbiology 145 (2011) S31–S38

Core

Rind

Replicate

μopt

R2

μopt

R2

1 2 3 4

0.12 0.15 0.22 0.22

0.21 0.97 0.45 0.89

0.14 0.14 0.14 0.22

0.94 0.98 0.90 0.74

6.0

log cfu/gdw

Table 5A Estimated μopt values for 4 smeared cheese replicates in raw milk during ripening, for the model with lactic acid. The goodness of fit is indicated by the R-square value (R²).

S37

5.5 5.0 4.5 4.0 3.5 3.0

5

105

205

305

405

505

605

hours

5. Conclusions Raw milk cheeses had 3-logs more background microflora than pasteurised milk cheeses, limiting the development of starter lactic acid

6.0 5.5

log cfu/gdw

exhaust the resources in their surrounding area (Antwi et al., 2007, Schvartzman et al., 2010). In terms of modelling, this study confirms the importance of challenge tests, necessary to improve qualitative knowledge on the behaviour of a microorganism (growth, inactivation, and stagnation) in the product at a given processing steps (cheese making and ripening). Common environmental parameters are sometimes sufficient to predict the behaviour, but sometimes other parameters, not yet accounted in the models, make the observed behaviour different from the model predictions. Thus, when studying a product, challenge tests are an important preliminary step if no qualitative knowledge on the behaviour of a microorganism in the product is available in the scientific literature. In the present study, while the model predicted growth, no growth actually occurred, indicating that parameters other than the modelled parameters of temperature, pH, water activity and L-lactic acid, were influencing the behaviour of L. monocytogenes during cheese making with raw milk and ripening with pasteurized milk. Where the model could be fitted to the data, the logistical primary model estimated the optimum growth rate with an R2 value above 0.80 in 75% of the estimates. When developing models in food systems, the data gathered on growth of L. monocytogenes yielded higher variability in the counts of bacteria than data gathered in laboratory media. This is due to the accumulated error of the sampling method or to the high heterogeneity encountered in food systems, and this is therefore reflected in the lower fit of the model. Nevertheless, the simulations drawn in the plots against the observed growth, show that the general trends of the behaviour of L. monocytogenes are well represented by the model predictions. In all the cheesemaking cases, the final concentrations of L. monocytogenes were accurately predicted (Fig. 4). For the ripening period, the simulation growth model was carried out on the whole ripening phase whereas the optimum growth rate was assessed using only the period were growth was observed. The quality of the predictions depended on the period of the ripening for which predictions were done: for the first period, where physico-chemical parameters allow growth, predictions fitted the observations well; for the second period, the model predicts a growth rate equal to 0 and consequently a stable population, whereas the observed data show that populations either remained stable or slightly decreased.

5.0 4.5 4.0 3.5 3.0 5

105

205

305

405

505

605

hours Fig. 5. Predictions of the model versus observed data of L. monocytogenes growth during ripening of raw milk cheeses. Two replicates are represented with error bars; (●) observed growth in the core, (○) observed growth on the rind, (—) predicted growth.

bacteria (and L. monocytogenes) and therefore inducing higher pH values throughout the cheese manufacturing and ripening. The results suggest that the logistic primary model can be used to predict the growth of L. monocytogenes in cheesemaking and ripening conditions where milk was contaminated with low numbers of L. monocytogenes, as environmental conditions leading to growth will allow accurate growth predictions. The model had its own limitations as inactivation could not be predicted, further work should be focussed on developing models capable of predicting both growth and inactivation. The general results indicate that as the cheese enters the market place and the relative risk from cheeses made from pasteurized milk are almost 100-fold less than those made from raw milk, if contaminated with L. monocytogenes. The aim of these models in food safety involves development of HACCP plans, evaluation of the safety of steps in the food manufacturing process, for example in production, distribution, storage and food handling and finally, evaluation of food safety objectives. Risk management in this sense can benefit from specific models on bacterial kinetics to implement control measures in order to protect the microbiological quality of foods, important for both food safety and product quality. Acknowledgments The work was supported by the European Union funded Integrated Project BIOTRACER (contract 036272) under the 6th RTD Framework. Maria Sol Schvartzman was in receipt of a Teagasc Walsh Fellowship.

References Table 5B Estimated μopt values for 4 smeared cheese replicates in raw milk during ripening, for the model without lactic acid. The goodness of fit is indicated by the R-square value (R²). Core

Rind 2

Replicate

μopt

R

μopt

R2

1 2 3 4

0.11 0.15 0.21 0.22

0.18 0.97 0.43 0.87

0.13 0.14 0.13 0.22

0.94 0.98 0.89 0.76

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