Modelling growth of Lactobacillus plantarum and shelf life of...

Original article

Modelling growth of Lactobacillus plantarum and shelf life of

vacuum-packaged cooked chopped pork at different temperatures

Francieli Dalcanton,1 Fernando P�erez-Rodr�ıguez,2* Guiomar Denisse Posada-Izquierdo,2

Gl�aucia M. F. de Arag~ao1 & Rosa Mar�ıa Garc�ıa-Gimeno2

1 Department of Chemistry Engineering and Food Engineering, Federal University of Santa Catarina – UFSC, 88040-900 Florian�opolis, SC,

Brazil

2 Departamento de Bromatolog�ıa y Tecnolog�ıa de los Alimentos, Universidad de C�ordoba, Campus Rabanales, Edif. Darwin-Anexo, 14014

C�ordoba, Spain

(Received 6 April 2013; Accepted in revised form 20 June 2013)

Summary Lactobacillus plantarum growth in a vacuum-packaged cooked meat product under different storage

temperatures (4, 10 and 16 °C) and the relation between the microorganism growth and sensory quality

were investigated. The Gompertz model was fitted to experimental counts of L. plantarum showing a good

fitting to growth curves at different temperatures. A root-square secondary model and linear model were

satisfactorily fitted to estimated growth rates (ffiffiffil

p ¼ 0:01 � ðTþ 5:89Þ) and lag times (k ¼ 0:17 � 1l), respec-tively. The sensory attributes (colour, flavour, taste, appearance) were also evaluated due to their impor-

tance to the global quality (Q). The sensory deterioration was detected several days after L. plantarum

reached the stationary phase, that is, 59, 45 and 25 days for 4, 10 and 16 °C, respectively. According to

results, sensory deterioration was related to time when microorganism reached late stationary phase,

phenomenon known as ‘delayed change’.

Keywords Cooked meat product, Lactobacillus plantarum, predictive microbiology, sensory quality, shelf life prediction.

Introduction

The deterioration of food products due to spoilagemicroorganisms is a highly serious problem, whichaffects both the food industry and consumers (Zurera-Cosano et al., 2006). Lactic acid bacteria (LAB) areidentified as a major spoilage population of vacuum-packaged and modified-atmosphere-packaged meatproducts, and other processed products stored underrefrigeration temperatures (Borch et al., 1996; Hugas,1998). Meat and meat products spoilage caused by thegrowth of LAB and biochemical reactions can causediscoloration, texture changes, slime formation,rancidity, development of off-odours and off-flavours.

Many strategies are used by the food industry toguarantee quality and safety of its foods, such as tem-perature control throughout the distribution in the

chill chain. This is very important to avoid rapid anduncontrollable microbial growth, which reduces prod-uct shelf life and may endanger public health (Nychaset al., 2008). Another important control factor is theatmosphere in which foods are packaged. Vacuumpackaging (VP) and modified-atmosphere packaging(MAP) are protective technologies that have beenwidely applied to meat and meat products to extendtheir shelf lives, retarding the development of oxidativeprocesses and microbial growth (Koutsoumanis et al.,2008).The LAB species most often isolated from spoiling

in cooked meat products belong to Lactobacillus genus(Samelis et al., 2000). This genus comprises severalspecies linked to spoilage in vacuum and modified-atmosphere packaging such as Lactobacillus sakei,Lactobacillus plantarum and Lactobacillus curvatus(Chenoll et al., 2007; McMillin, 2008).The objectives of this study were (i) to estimate and

model the growth of Lactobacillus plantarum as aspoilage microorganism on slices of cooked choppedpork packed in vacuum atmosphere under differentstorage temperatures (4, 10 and 16 °C) and (ii) tostudy the effect of L. plantarum growth on the productsensory quality.

*Correspondent: Fax: +34-957 21 20 00; e-mail: [email protected]

This work provides predictive growth models for Lactobacillus plan-tarum, which can be used by food business operators to determineshelf life of similar cooked meat product to that used in this study.In addition, the work provides insight into ‘the delayed change’,which is related to the sensory deterioration in cooked meatproducts.

International Journal of Food Science and Technology 2013

doi:10.1111/ijfs.12252

© 2013 The Authors. International Journal of Food Science and Technology © 2013 Institute of Food Science and Technology

1

Materials and methods

Inoculum preparation

The strain of Lactobacillus plantarum ATCC 8014 wastaken from the Spanish Collection of Strain Types(Valencia, Spain). The strain was maintained at�18 °C in cryovials containing beads and cryopreser-vative (MicrobankTM). Two days before the experi-ment, a bead of the strain was transferred into a tubewith 10 mL of MRS broth (Oxoid Ltd., Hampshire,UK) and incubated at 30 °C for 24 h in 10% CO2

(Crenesys Instrumentation, Madrid, Spain). Then,1 mL of the initial subculture was pipetted into a flaskcontaining 100 mL of Man, Rogosa and Sharpe broth(MRS, Oxoid Ltd.) and incubated for 18 h at 30 °C.After this period, the strain was washed by centrifuga-tion at 2683 g (Jouan C4i; Thermo Electron Corpora-tion, Illkirch, France) for 10 min, and this process wasrepeated twice with the use of a 0.85% saline solution(Panreac, Barcelona, Spain). Then, necessary decimaldilutions were made in 0.85% saline solution, and0.1 mL of the adequate dilution was spread evenly onthe slice using a sterile glass rod to obtain an initialinoculum of about 103 CFU g�1 on the surface ofeach slice of meat product. This level was consideredappropriate to represent a real contamination scenariowhile allowing monitoring the growth of the testmicroorganism.

Samples preparation

The products used in this study were taken fromcooked chopped pork stubs preserved in cans with anaverage weight of 2.2 kg (in each package) provided bya Spanish industry (C�ordoba, Spain). Chopped pork isa ready to eat (RTE) product, prepared with pork meat(65%), water, curing ingredients, food-grade salt andsodium nitrite. The mean values of pH and aw of theproduct were provided by the manufacturer, whichcorresponded to 6.2 and 0.971, respectively.

The product was sliced aseptically in a householdslicer with a polished stainless steel blade (DemokaTM,M-381 Zeta Plus Ø, Lleida, Spain) that was disinfectedwith 70% (v/v) ethanol for 10 min prior to its use.The obtained slices, which weighed approximately25 g, were placed in plastic film bags (Sacoliva, Barce-lona, Spain). Then, samples were inoculated with asubculture of Lactobacillus plantarum to obtain almost103 CFU g�1 on the slice surface. Noninoculated sam-ples were used as control. Finally, the samples werevacuum-packaged using a packer (Audionvac 151 HG,Weesp, Holland) and subsequently stored at differenttemperatures (4, 10 and 16 °C).

To verify the effectiveness of the disinfection processof the slicer, Plate Count Agar (PCA, Oxoid Ltd.) and

MRS agar (Oxoid Ltd.), Rodac plates were put in con-tact with three different surface zones of the slicingmachine, including the blade, and incubated at 30 °Cfor 48 h and 30 °C for 48 h in 10% CO2 for PCA andMRS, respectively.

Microbiological and physical-chemical analysis

Prior to the microbiological analysis, the pH was asep-tically measured directly on three different points ineach slice of product using a pH meter (pH/mv-meterdigit 501, Crison, Barcelona, Spain). Then, 25 g ofmeat product (from one slice) was aseptically trans-ferred into a sterile stomacher bag. Next, 225 mL of0.1% peptone solution (Oxoid Ltd.) was added, andthe sample was homogenised for 60 s in a Stomacher400 (Lab Blender, Seward, London, UK). Decimaldilutions were made with 0.85% saline solution, whenneeded, and plated in duplicate. Lactic acid bacteriawere counted using the pour plate method, and MRSagar was incubated at 30 °C for 48 h in an incubatorwith 10% CO2. The plates were examined visually fortypical colonies. Each microbiological determinationwas performed in triplicate (i.e. three samples), andthe results were expressed as the average colony-form-ing units per gram (CFU g�1). The determination ofthe sampling time was dependent on the storage tem-perature and microbial growth evolution.Detection and enumeration of pathogenic and

hygienic indicators microorganisms were performed onanalysed samples. Escherichia coli b-glucuronidasewas enumerated using a most probable number(MPN) method in 9-mL tubes of lauryl tryptose brothsupplemented with MUG (4-methylumbelliferyl-b-D-glucuronide) as established by the AOAC (AOAC,1995). Investigation and enumeration of Listeria spp.and L. monocytogenes and investigation of Salmonellaspp. were performed according to UNE-EN ISO11290-2:2000/A1 and UNE-EN ISO 6579:2003/A1,respectively.

Sensorial analysis and shelf life determination

The evaluation was performed by a trained sensorypanel consisting of 8–10 panellists. The number ofpanellists was considered sufficient for determiningsensory deterioration in accordance with other similarstudies carried out on cooked meat products (Ahmadet al., 2010; Giatrakou et al., 2010; Ntzimani et al.,2010; Clariana et al., 2011). The main organolepticand external attributes corresponded to colour, odour,taste, appearance, slime presence and general accep-tance, which were evaluated in facilities under a stan-dard test room. A glossary was prepared containingterms relating to an intensity scale. The scale followeda numerical representation that considers the severity

© 2013 The Authors

International Journal of Food Science and Technology © 2013 Institute of Food Science and Technology


Modelling L. plantarum growth in cooked meat F. Dalcanton et al.2

of the evaluation using a hedonic scale from 1 to 9(1 = disliked very much; 9 = liked very much) (Peryam& Girardot, 1952, 1957). A hedonic grade of five waschosen to indicate the start of the product deteriora-tion; therefore, samples with values below five wereconsidered unacceptable.

The global quality (Q) was calculated based on themethodology applied by Bruna et al. (2001), using thefollowing Equation 1:

Q ¼ ðColour� f1Þ þ ðAppearance� f2Þ þ ðOdour� f3Þþ ðTaste� f4Þ ð1Þ

where Colour, Appearance, Odour and Taste corre-sponded to mean scores for each attribute obtained bysensory analysis and fi was a value between 0 and 1,with f1 + f2 + f3 + f4 = 1, which accounts for thedegree of importance of the corresponding attribute.

The values for fi were calculated based on the opin-ion of the panellists by ranking attributes from 1 to 4according to its degree of importance. The higher thevalue is, the higher the importance. The ranking valuesassigned by panellists for each attribute were pooledto calculate the relative weight of each attributeexpressed as per unit according to the followingequation:

fi ¼

P10

j¼1

Rij

100

where Rij (between 1 and 4) is the ranking assigned byeach j panellist (j = 1��10) for the attribute fi, withi = 1��4.

The following values were obtained for fi: 0.37 forcolour, 0.33 for appearance, 0.10 for odour and 0.20for taste. The degree of importance was also confirmedby analysis of the correlation between the values ofthe different attributes and general acceptance. Alinear regression was performed on the Q values calcu-lated during storage for each temperature. To estimatethe shelf life at the different temperatures, the deterio-ration limit (Q = 5) was determined by interpolationfrom the regression equation obtained for Q.

Modelling of growth data

Gompertz model (Gibson et al., 1987) was fitted forL. plantarum growth data obtained in chopped (log N)using the DMFit program (Excel Add-In) (Baranyi &Roberts, 1994) to estimate the kinetic parameters. TheGompertz model is represented by Equation 2.

logN ¼ N0 þ A exp � exp �Bðt�MÞ½ �f g ð2Þwhere log N is the decimal logarithm of microbialcounts (CFU g�1) at time t, N0 is the asymptotic log

count as time decreases indefinitely (approximatelyequivalent to the log of the initial bacteria counts)(CFU g�1), A is the log count increment as timeincreases indefinitely (CFU g�1), B is the relative growthrate at time M (h�1), and M is the time required toreach the maximum growth rate (h).From these parameters, the following derived

parameters were obtained: the maximum growth rate(l = B.A/e (h�1)), with e = 2.7182, the lag phase dura-tion (k = M�(1/B) (h)) and the maximum populationdensity (MPD = N0 + A (log(CFU g�1))).Maximum growth rates (l) were used to derive a

secondary model describing the relationship betweengrowth rate and temperature. The applied secondarymodel corresponded to the square-root modeldescribed by Ratkowsky et al. (1982):

ffiffiffil

p ¼ b � ðT� TminÞ ð3Þwhere b and Tmin are regression parameters. The latteris considered the conceptual minimum temperature formicrobial growth.The lag time (k) was modelled based on the relation-

ship between lag time and h0 (measure of the physio-logical state of the cells) proposed by Baranyi &Roberts (1994) and described in Equation 4 (Baranyi,2002):

k ¼ h0 � 1l ð4Þ

Statistical analysis

Descriptive statistics of microbiological data andchecklist scores were applied with Microsoft Excel(Microsoft, Redmond, WA, USA). In addition, Pear-son correlation coefficient (r) and regression analysiswere performed by means of SPSS 8.0 software (SPSSInc. Chicago, IL, USA).The goodness of fit of the Gompertz model and the

square-root model was assessed using the correlationcoefficient (R2) and mean square error (MSE) (Equa-tion 5) (Sutherland et al., 1994). The smaller the MSEvalue, the better the model fitted the data.

MSE ¼P

Valueobserved � Valuepredicted� �2

n� pð5Þ

where n is the number of experimental points and p isthe number of model parameters.

Result and discussion

Microbial growth

The Lactobacillus plantarum initial counts on the inoc-ulated samples were approximately 3.5 log CFU g�1.

© 2013 The Authors



Modelling L. plantarum growth in cooked meat F. Dalcanton et al. 3

At the end of the storage time, inoculated samplesreached 5.25 log10 CFU g�1, 7.61 log CFU g�1 and8.09 log CFU g�1 at 4, 10 and 16 °C, respectively.Analysis of control samples indicated that LAB countsat the beginning of the experiment (i.e. day 0) werebelow the limit of detection (<1 log CFU g�1) at alltested temperatures and its subsequent growth pre-sented maximum population levels below 4 logCFU g�1 for 16 °C and lower than 3 log CFU g�1 for10 and 4 °C. These data allowed us to confirm thatgrowth curves for inoculated samples obtained inMRS at the different temperatures corresponded toL. plantarum growth, and not to endogenous flora.Microbiological analysis on samples indicated, in addi-tion, that no pathogenic species were detected, and nosamples were positive for E. coli.

Lactobacillus plantarum growth data on slices ofvacuum-packaged cooked chopped pork at differenttemperatures (4, 10 and 16 °C) were fitted with theGompertz model (Fig. 1). The obtained growthparameters (k, l and MPD), along with fitting-good-ness indices for the fitted model, are shown in Table 1.

Figure 1 shows that the behaviour of L. plantarumat different temperatures was well described by theGompertz model, which was confirmed through thehigh correlation coefficients (R2) and small values ofMSE (Table 1). The growth curve determined at 10 °Cshowed the best fit, with R2 = 0.9931 andMSE = 0.0033, followed by the curve determined at16 °C, while the lowest correlation coefficient wasobserved for the growth curve determined at 4 °C(0.9511), despite its small MSE (0.0208). Our resultsconfirmed the Gompertz model was a suitable modelto describe L. plantarum growth in cooked meatproduct. The Gompertz equation has been successfullyapplied to represent growth of LAB and other spoilagemicroorganisms (e.g. Pseudomonas spp.) in meat

products, such as sliced ham (Slongo et al., 2009), sau-sage (Cayr�e et al., 2005), beef (Giannuzzi et al., 1998),among others.The temperature increase led to the decrease in the

lag phase duration (k), along with the increase in themaximum specific growth rate (l) and in the maximumpopulation (MPD). The counts variability (i.e.standard deviation) observed for some growth data at4 °C could be the consequence of a major variabilityof cells to adapt to more stringent conditions (i.e. lowtemperatures). Nevertheless, bacteria behaviourshowed large differences between 4 °C and 10 °C,despite the refrigeration temperature suggested for theconservation of meat products being in this range. Thelag phase at 4 °C was approximately 25 days(591.23 h), while at 10 °C, this parameter decreaseddramatically to 4 days (95.35 h). When growth was atabuse temperature (i.e. 16 °C), there was no lag phase,as shown in Fig. 1 and Table 1. A large difference alsooccurred on the l parameter, rising from 0.007 (h�1)to 0.029 (h�1) when the temperature increased from 4to 10 °C, respectively, meaning an increase by aboutfour times (Table 1).

Table 1 Lactobacillus plantarum growth parameters and statistical

indices obtained by fitting the Gompertz model to the growth curves

determined at different temperatures for vacuum-packaged cooked

chopped pork

T (°C)

Growth parameters* Statistical indices

k l MPD R2 MSE

4 591.23 0.007 5.25 0.9511 0.0208

10 95.35 0.029 7.61 0.9931 0.0033

16 0 0.047 8.09 0.9907 0.0221

*k (h), l (h�1) and MPD (log10 CFU g�1).

0

1

2

3

4

5

6

7

8

9

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72

Log

N (C

FU g

–1)

Time (days)

Figure 1 Lactobacillus plantarum growth

curves fitted by Gompertz model in slices of

vacuum-packaged cooked chopped pork at

different storage temperatures. The lines rep-

resent the Gompertz model fitted to experi-

mental data (symbols). (●) 4 °C, (▲) 10 °Cand (♦) 16 °C. Bars represent the standard

deviation.

© 2013 The Authors




The square-root model described by Ratkowskyet al. (1982) was satisfactorily fitted to square-rootgrowth rates as shown by the coefficient of determina-tion (R2) and the standard error (SE) that were equalto 0.95 and 0.004, respectively. The value estimatedfor the regression parameters b and Tmin correspondedto 0.01 and �5.89, respectively. Therefore, the expres-sion of the fitted secondary model remained asfollows:

ffiffiffil

p ¼ 0:01 � ðTþ 5:89Þ ð6ÞIn the case of the model for lag time (k), the fitted

model (Equation 7) showed a good fitting to lag datawithR2 = 0.95, where h0 = 0.17.

k ¼ 0:17 � 1l

ð7Þ

To the best of our knowledge, no models forL. plantarum growth in cooked meat have been pub-lished so far. However, there are growth models forL. plantarum developed in culture broth (Cuppers &Smelt, 1993; Zwietering et al., 1994). For instance,growth prediction at 10 °C (0.025 h�1) from the broth-based growth model for L. plantarum developed byCuppers & Smelt (1993) was similar to that observedat the same temperature in our experiment, whilegrowth rate was markedly higher in the broth-basedmodel at 16 °C. As expected, maximum growth ratesderived from the L. plantarum growth curves, in ourstudy, were different from the predictions from broth-based models developed for other Lactobacillus spp.For instance, the growth model for Lactobacillus curva-tus from Wijtzes et al. (2001) predicted a lower growthrate at 10 °C (0.014 h�1), while at 16 °C, the predictedgrowth rate was slightly higher (0.061 h�1) than thatobtained in our experiment at the same temperature.The same pattern was obtained for the predictionderived from the growth model for Lactobacillus sakereported by Devlieghere et al. (2000).

In this study, the stationary phase was reached afteralmost 8, 12 and 36 days at 16 °C, 10 and 4 °C,respectively. As it can be seen in Table 1, at 4 °C themaximum population did not reach 7 log CFU g�1,which is usually considered as the microbiologicalcriterion to establish shelf life in refrigerated meatproducts (Koutsoumanis et al., 2008; Slongo et al.,2009). The count reached 5.25 log10 CFU g�1 in70 days at this lower temperature. At 10 and 16 °C,maximum populations were 7.6 log CFU g�1 and8.09 log CFU g�1, achieving 7 log CFU g�1 in 10 and4 days of storage, respectively.

Cayr�e et al. (2003) investigated the effect of threestorage temperatures on LAB growth in vacuum-pack-aged cooked meat emulsions. The periods needed toreach the stationary phases (>7 log CFU g�1) were 8,14 and 25 days, while the growth rates were 0.048,

0.027 and 0.017 h�1 at 15, 8 and 0 °C, respectively.The l parameter and times elapsed until the stationaryphases for the higher temperatures are in agreementwith the current study, but the behaviour at the lowesttemperature was not similar between studies, as LABin samples at 0 °C reached levels >7 log CFU g�1, inaddition to showing a shorter lag phase and highergrowth. The difference observed between studies canbe due to distinct LAB species composition in theanalysed products and the interaction of differentintrinsic and extrinsic factors, specific for each prod-uct, such as different concentration of nutrients, salt,preservatives, pH, water activity, temperature, packag-ing conditions, among others. Samelis et al. (2000)determined the selective effect of the product type andthe packaging conditions (vacuum or air) on the typesof LAB. When sliced, vacuum-packed cooked ham,turkey breast fillet, smoked pork loin, bacon, parizaand mortadella from the same processing environmentand day of manufacture were stored at 4 °C, both thegrowth rate and the composition of the spoilage floradiffered significantly among the products.

Physico-chemical analysis

The pH value of the meat product was also monitoredduring the storage. Fig. 2 shows the correlationbetween pH mean values and L. plantarum counts forslices of vacuum-packaged cooked chopped pork foreach storage temperature. The mean initial pH valueof all samples was 6.27 (�0.01). During storage of thismeat product at three different temperatures, the pHvalues remained nearly constant until L. plantarumreached a value of 7 log10 CFU g�1. At this point, thepH values of the samples started to decrease. At theend of storage, the pH value reached 5.70 (�0.04) and6.06 (�0.05) at 16 and 10 °C, respectively, and 6.2(�0.03) at 4 °C, which practically did not change with

5.5

5.7

5.9

6.1

6.3

6.5

3 4 5 6 7 8 9

pH

Log N (CFU g–1)

Figure 2 Behaviour of pH values during the growth of Lactobacil-

lus plantarum (log CFU g�1) in slices of vacuum-packaged cooked

chopped pork at different storage temperatures: 4 °C (D), 10 °C (□)and 16 °C (◊). Bars show the standard deviation.

© 2013 The Authors




respect to the initial value. Spoilage lactic acid bacteriaproduce mostly lactic and acetic acid during logarith-mic growth and especially at the stationary phase,producing a pH reduction in the product (Korkealaet al., 1987). These results are in agreement with thosereported by Kreyenschmidt et al. (2010), who investi-gated the shelf life of sliced cooked ham based on theLAB growth stored under different temperature condi-tions (2–15 °C). These authors reported that duringstorage, the pH value remained invariable until LABreached a value of 7.5 log CFU g�1.

Sensory analysis

Although nonmicrobiological chemical changes mayalso affect the sensorial quality of cooked meat prod-ucts, this factor is only significant at very low tempera-tures, when bacterial growth is completely inhibited ator below �3 °C (Korkeala et al., 1987). Likewise,some authors have indicated that meat spoilage is neg-ligible compared with the microbial action of themicrobial flora (Nychas & Tassou, 1997; Tsigarida &Nychas, 2001). As nonmicrobiological chemicalchanges in cooked meat (i.e. colour and lipid oxida-tion) are mostly related to the content of residual O2

(Nannerup et al., 2004) and samples in our study werepackaged in vacuum conditions, the detected sensorialchanges were assumed to be mainly derived frommicrobiological activity.

Table 2 shows scores during storage for the differentsensorial attributes evaluated in this study. In our study,slime formation was not detected by panellists; hence,this attribute was discarded for further analysis. Themajor spoilage characteristics associated with L. planta-rum are acidity increase and flavour changes (Vermeu-

len et al., 2007). The correlation analysis demonstratedthat the most sensitive attributes to deterioration werecolour and odour for samples at 4 °C (r = �0.97), and10 and 16 °C (r = �0.96), respectively, which experi-enced a significant reduction during storage.Correlation analysis of attributes used for Q (odour,

colour, taste and appearance) confirmed that theseattributes were strongly correlated to the generalacceptance (r = 0.76–0.99) (data not shown). A regres-sion analysis was performed on global quality scores(Q) and storage times (t), resulting in a decreasingstraight line (i.e. Q(t) = �m∙t + n) for all assayed tem-peratures, with R2 of 0.891, 0.761 and 0.802 at 4, 10and 16 °C, respectively, as shown in Fig. 3. The slopesof these straight lines (m) decreased as temperatureincreased, being �0.054, �0.066 and �0.111 for sam-ples at 4, 10 and 16 °C, respectively. From thestraight-line equations, by utilising the establisheddeterioration threshold (i.e. 5), shelf life could be esti-mated based on the sensory quality of samples. Shelflife estimations corresponded to 59, 45 and 25 daysfor 4, 10 and 16 °C, respectively. These estimationsindicated that the shelf life was on the late stationaryphase of the growth curves, that is, 32, 33 and 15 daysafter the maximum population density was reached,respectively. Results demonstrated that sensorial alter-ation was less intense than other reported cases(Vermeulen et al., 2007). Indeed, high microbial countsmay in some instances not cause noticeable spoilage(Borch et al., 1996). Sensory changes produced bylactic acid bacteria appear after the bacteria reach thestationary growth phase (Korkeala & Bjorkroth,1997). Korkeala et al. (1989) concluded that the prob-ability that the count of 7 log Lactobacillus spp/gcooked meat product would cause overt spoilage is

Table 2 Sensory attributes mean for vacuum-packaged cooked chopped pork at different storage temperatures (4, 10 and 16 °C)

Temperature (°C)

Storage time (day)

0 5 12 19 26 33 37 40 47 58 68 75

Colour 4 9.0 8.0 – 6.9 6.8 – – 6.2 – – – 4.3

10 9.0 7.7 – 6.7 6.1 6.3 – 5.3 5.9 5.7 3.3 –

16 9.0 6.1 6.4 5.8 5.2 – 4.9 – 3.2 – – –

Appearance 4 9.0 7.2 – 6.0 7.3 – – 6.1 – – – 4.4

10 9.0 7.2 – 6.6 5.9 5.7 – 5.6 5.1 5.3 4.3 –

16 9.0 5.8 5.7 5.6 5.3 – 4.6 – 4.7 – – –

Odour 4 9.0 7.9 – 6.4 6.4 – – 5.7 – – – 4.5

10 9.0 7.6 – 6.6 6.0 5.5 – 4.9 5.0 4.6 4.4 –

16 9.0 6.2 6.0 5.3 4.9 – 3.8 – 3.8 – – –

Taste 4 9.0 7.7 – 6.3 6.7 – – 5.6 – – – 4.3

10 9.0 7.3 – 6.1 – 6.2 – 5.2 – 6.4 – –

16 9.0 6.3 6.4 5.3 – – – – – – – –

General acceptance 4 9.0 7.4 – 5.7 6.3 – – 5.7 – – – 3.6

10 9.0 7.2 – 6.1 5.4 5.6 – 5.1 5.0 5.0 3.9 –

16 9.0 5.3 6.1 5.2 4.8 – 4.6 – 4.4 – – –

–: No sensory analysis was performed.

© 2013 The Authors




about 10%. The time interval elapsed from themoment bacteria reached 7 log CFU g�1 to theappearance of evident spoilage was 19 and 30 days at4 and 2 °C, respectively. In the current study, the esti-mated times elapsed between counts of 7 log CFU g�1

and the established deterioration threshold were 35and 21 days at 10 and 16 °C, respectively. At 4 °C,

maximum levels were always below 7 log10 CFU g�1,although the estimated sensory deterioration (byextrapolation) was situated after 59 days. Rodr�ıguez-P�erez et al. (2003) determined the shelf life of vacuum-packed sliced cooked chicken breast based on sensoryand microbial changes as a function of temperature(2.3, 6.5, 10, 13.5 and 17.7 °C). At 13.5 and 17.7 °C,the mean shelf life estimated microbiologically wasshorter than that estimated using sensory methods.This difference, which amounted to at least 8 days,was due to the so-called delayed change, reported inprevious experiments with cooked meat products. Inthe present study, the delay in the sensory changes wasobserved for the three studied temperatures. Accordingto these results, it may be stated that spoilage is devel-oped slowly and begins only when the maximumdensity population of spoilage flora, L. plantarum inour work, has been reached (Mataragas et al., 2006).

Conclusions

This work obtained adequate secondary models forpredicting growth rate (l) and lag time (k) of L. plan-tarum as a function of temperature (4–16 °C) invacuum-packaged cooked chopped pork. However,sensory alteration appeared much later than time whenthe stationary phase was reached, and it was notrelated to the maximum numbers of bacteria reached.The results allow for the conclusions that L. plantarumgrowth is strongly influenced by storage temperature,even under refrigeration conditions, and that abusetemperatures can cause a drastic reduction in productshelf life. However, sensory changes were not severe;hence, environmental conditions should be furtherexamined with other strains to further define the con-ditions in which spoilage by L. plantarum can occur.

Acknowledgments

The authors gratefully acknowledge the Capes Foun-dation (Brazil), for the financial support of FrancieliDalcanton for her Ph.D. studies and both the Depart-ment of Food Science and Technology of the Univer-sity of C�ordoba and the Junta de Andalucia/ProjectP08-CTS 3620, for the financial support to carry outthis research.

References

Ahmad, S., Rizawi, J.A. & Srivastava, P.K. (2010). Effect of soyprotein isolate incorporation on quality characteristics and shelf-life of buffalo meat emulsion sausage. Journal of Food Science andTechnology, 47, 290–294.

AOAC- Association of Official Analytical Chemists International.(1995). Official Methods of Analysis of AOAC International, 16thedn. Gaithersburg, MD: AOAC International.

Baranyi, J. (2002). Stochastic modelling of bacterial lag phase. Inter-national Journal of Food Microbiology, 73, 203–206.

Q = –0.054(time) + 8.209R2 = 0.891

0

1

2

3

4

5

6

7

8

9

10

Glo

bal q

ualit

y (Q

)

Time (days)

Q = –0.066(time) + 7.966R2 = 0.761

0

1

2

3

4

5

6

7

8

9

10

Glo

bal q

ualit

y (Q

)

Time (days)

Q = –0.111 (time) + 7.877R2 = 0.802

0

1

2

3

4

5

6

7

8

9

10

0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

0 5 10 15 20 25 30 35 40 45 50

Glo

bal q

ualit

y (Q

)

Time (days)

(a)

(b)

(c)

Figure 3 Linear regression of the sensorial global quality (Q) over

time (days), at storage temperatures of 4 °C (a), 10 °C (b) and

16 °C (c).

© 2013 The Authors




Baranyi, J. & Roberts, T.A. (1994). A dynamic approach to predict-ing bacterial growth in food. International Journal of Food Microbi-ology, 23, 277–294.

Borch, E., Kant-Muemansb, M.L. & Blixt, Y. (1996). Bacterial spoil-age of meat products and cured meat. International Journal of FoodMicrobiology, 33, 103–120.

Bruna, J.M., Ord�o~nez, J.A., Fern�andez, M., Herranz, B. & Hoz, L.(2001). Microbial and physico-chemical changes during the ripen-ing of dry fermented sausages superficially inoculated with orhaving added an intracellular cell-free extract of Penicillium aurant-iogriseum. Meat Science, 59, 87–96.

Cayr�e, M.E., Vignolo, G. & Garro, O. (2003). Modeling lactic acidbacteria growth in vacuum-packaged cooked meat emulsion storedat three temperatures. Food Microbiology, 20, 561–566.

Cayr�e, M.E., Garro, O. & Vignolo, G. (2005). Effect of storagetemperature and gas permeability of packaging film on the growthof lactic acid bacteria and Brochothrix thermosphacta in cookedmeat emulsions. Food Microbiology, 22, 505–512.

Chenoll, E., Maci�an, M.C., Elizaqu�ıvel, P. & Aznar, R. (2007). Lac-tic acid bacteria associated with vacuum-packed cooked meatproduct spoilage: population analysis by rDNA-based methods.Journal of Applied Microbiology, 102, 498–508.

Clariana, M., Guerrero, L., S�arraga, C., D�ıaz, I., Valero, A. &Garc�ıa-Regueiro, J.A. (2011). Influence of high pressure applica-tion on the nutritional, sensory and microbiological characteristicsof sliced skin vacuum packed dry-cured ham. Effects along thestorage period. Innovative Food Science and Emerging Technologies,12, 456–465.

Cuppers, H.G.A.M. & Smelt, J.P.P.M. (1993). Time to turbiditymeasurement as a tool for modeling spoilage by Lactobacillus.Journal of Industrial Microbiology, 12, 168–171.

Devlieghere, F., Geeraerd, A.H., Versyck, K.J., Bernaert, H., VanImpe, J.F. & Debevere, J. (2000). Shelf life of modified atmospherepacked cooked meat products: addition of Na-lactate as a fourthshelf life determinative factor in a model and product validation.International Journal of Food Microbiology, 58, 93–106.

Giannuzzi, L., Pinotti, A. & Zaritzky, N. (1998). Mathematical mod-eling of microbial growth in packaged refrigerated beef at differenttemperature. International Journal of Food Microbiology, 39, 101–110.

Giatrakou, V., Ntzimani, A. & Savvaidis, I.N. (2010). Combinedchitosan-thyme treatments with modified atmosphere packaging ona ready-to-cook poultry product. Journal of Food Protection, 73,663–669.

Gibson, A.M., Bratchell, N. & Roberts, T.A. (1987). The effect ofsodium chloride and temperature on rate and extent of growth ofClostridium botulinum type A in pasteurized pork slurry. Journal ofApplied Bacteriology, 62, 479–490.

Hugas, M. (1998). Bacteriocinogenic lactic bacteria for the biopreser-vation of meat and meat products. Meat Science, 49, 139–150.

Korkeala, H. & Bjorkroth, K.J. (1997). Spoilage and contaminationof vacuum-packaged cooked sausages: a review. Journal of FoodProtection, 60, 724–731.

Korkeala, H., Lindroth, S., Ahvenainen, R. & Alanko, T. (1987).Interrelationship between microbial numbers and other parametersin the spoilage of vacuum-packed cooked ring sausage. Interna-tional Journal of Food Microbiology, 5, 311–321.

Korkeala, H., Alanko, T., M€akel€a, P. & Lindroth, S. (1989). Shelf-life of vacuum-packed cooked ring sausages at different chill tem-peratures. International Journal of Food Microbiology, 9, 237–247.

Koutsoumanis, K.P., Stamatiou, A.P., Drosinos, E.H. & Nychas,G.J.E. (2008). Control of spoilage microorganisms in minced porkby a self-developed modified atmosphere induced by the respira-tory activity of meat microflora. Food Microbiology, 25, 915–921.

Kreyenschmidt, J., Hubner, A., Beierle, E., Chonsch, L., Scherer, A.& Petersen, B. (2010). Determination of the shelf life of slicedcooked ham based on the growth of lactic acid bacteria in differentsteps of the chain. Journal of Applied Microbiology, 108, 510–520.

Mataragas, M., Drosinos, E.H., Vaidanis, A. & Metaxopoulos, I.(2006). Development of a predictive model for spoilage of cookedcured meat products and its validation under constant anddynamic temperature storage conditions. Journal of Food Science,71, 157–167.

McMillin, K.W. (2008). Where is MAP Going? A review and futurepotential of modified atmosphere packaging for meat. MeatScience, 80, 43–65.

Nannerup, L.D., Jakobsen, M., van den Berg, F., Stoumann, J.J.,Møller, J.K.S. & Bertelsen, G. (2004). Optimising colour quality ofmodified atmosphere packed sliced meat products by control ofcritical packaging parameters. Meat Science, 68, 577–585.

Ntzimani, A.G., Giatrakou, V.I. & Savvaidis, I.N. (2010). Combinednatural antimicrobial treatments (EDTA, lysozyme, rosemary andoregano oil) on semi cooked coated chicken meat stored in vacuumpackages at 4 °C: microbiological and sensorial evaluation. Innova-tive Food Science and Emerging Technologies, 11, 187–196.

Nychas, G.J.E. & Tassou, C.C. (1997). Spoilage processes and prote-olysis in chicken as detected by HPLC. Journal of the Science ofFood and Agriculture, 74, 199–208.

Nychas, G.J.E., Skandamis, P.N., Tassou, C.C. & Koutsoumanis,K.P. (2008). Meat spoilage during distribution. Meat Science, 78,77–89.

Peryam, D.R. & Girardot, N.F. (1952). Advanced taste-test method.Food Engineering, 24, 58–61.

Peryam, D.R. & Girardot, N.F. (1957). Hedonic scale method ofmeasuring food preferences. Food Technology, 11, 9–14.

Ratkowsky, D.A., Olley, J., McMeekin, T.A. & Ball, A. (1982).Relationship between temperature and growth rate of bacterial cul-tures. Journal of Bacteriology, 149, 1–5.

Rodr�ıguez-P�erez, M.R., Zurera-Cosano, G., Garc�ıa-Gimeno, R.M.,Barco-Alcal�a, E. & Castillejo-Rodr�ıguez, A.M. (2003). Sensory andmicrobiological quality evaluation of vacuum-packed sliced cookedchicken breast: shelf-life estimation. Journal of Food Quality, 26,105–122.

Samelis, J., Kakouri, A. & Rementzis, J. (2000). Selective effect ofthe product type and the packaging conditions on the species oflactic acid bacteria dominating the spoilage microbial associationof cooked meats at 4 °C. Food Microbiology, 17, 329–340.

Slongo, A.P., Rosenthal, A., Camargo, L.M.Q., Deliza, R., Mathias,S.P. & Arag~ao, G.M.F. (2009). Modeling the growth of lactic acidbacteria in sliced ham processed by high hydrostatic pressure.LWT- Food Science Technology, 42, 303–306.

Sutherland, J.P., Bayliss, A.J. & Roberts, T.A. (1994). Predictivemodeling of growth of Staphylococcus aureus: the effects of temper-ature, pH and sodium chloride. International Journal of FoodMicrobiology, 21, 217–236.

Tsigarida, E. & Nychas, G.J.E. (2001). Ecophysiological attributesof a Lactobacillus sp. and a Pseudomonas sp. on sterile beef filletsin relation to storage temperature and film permeability. Journal ofApplied Microbiology, 90, 696–705.

Vermeulen, A., Devlieghere, F., Bernaerts, K., Van Impe, J. & Debe-vere, J. (2007). Growth/no growth models describing the influenceof pH, lactic and acetic acid on lactic acid bacteria developed todetermine the stability of acidified sauces. International Journal ofFood Microbiology, 119, 258–269.

Wijtzes, T., Rombouts, F.M., Kant-Muermans, M.L.T., van‘t Riet,K. & Zwietering, M.H. (2001). Development and validation of acombined temperature, water activity, pH model for bacterialgrowth rate of Lactobacillus curvatus. International Journal of FoodMicrobiology, 63, 57–64.

Zurera-Cosano, G., Garc�ıa-Gimeno, R.M., Rodr�ıguez-P�erez, R. &Herv�as-Mart�ınez, C. (2006). Performance of response surface modelfor prediction of Leuconostoc mesenteroides growth parametersunder different experimental conditions. Food Control, 17, 429–438.

Zwietering, M.H., de Wit, J.C., Cuppers, H.G.A.M. & van′t Riet,K.. (1994). Modeling of bacterial growth with shifts in tempera-ture. Applied and Environmental Microbiology, 60, 204–213.

© 2013 The Authors




Modelling growth of Lactobacillus plantarum and shelf life of...

Documents

Transcript of Modelling growth of Lactobacillus plantarum and shelf life of...