Effect of the substrate's microstructure on the growth of Listeria monocytogenes

Food Research International 64 (2014) 683–691

Contents lists available at ScienceDirect

Food Research International journal homepage: www.elsevier.com/locate/foodres

Effect of the substrate's microstructure on the growth of Listeria monocytogenes Z. Aspridou a, T. Moschakis b, C.G. Biliaderis c, K.P. Koutsoumanis a,⁎ a b c

Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece Laboratory of Dairy Science and Technology, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece Laboratory of Food Chemistry and Biochemistry, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

a r t i c l e

i n f o

Article history: Received 16 April 2014 Received in revised form 16 July 2014 Accepted 23 July 2014 Available online 31 July 2014 Keywords: Microstructure Listeria monocytogenes Gelatin Sodium alginate Model dairy product Growth kinetics Gelation rheology

a b s t r a c t The effect of the microstructure of the medium on the growth of the food-borne pathogen Listeria monocytogenes was studied. The pathogen's growth kinetics was evaluated using liquid substrates and gels formed from different concentrations of sodium alginate (3.0% w/w) and gelatin (0–30.0% w/w). These results were further verified using a model dairy product with solid concentrations varying from 10.0 to 40.0% w/w. The pathogen's growth was faster in the liquid media than in the gels regardless of the gelling agent employed. The substrate's microstructure, apart from altering the growth pattern from planktonic to colonial, resulted in microbial growth suppression; however, each system affected the microorganism's growth in a different way. The suppressing effect of the substrate's microstructure on microbial growth was also dependent on temperature, while the presence of glucose in the solid medium accelerated microbial growth, thus reducing substantially the difference in growth kinetics between the gels and the liquid media. Any increase in the hydrocolloid concentration, which was also reflected in the rheological properties of the structured samples, resulted in a reduction of growth rate and in an increase of the lag phase of the pathogen. Overall, the gelation of the medium was found to exert a stress on the microorganism since the sol–gel transition, when the pathogen was already at the exponential growth phase, resulted in an additional lag phase or a decrease in the growth rate. The relationship between maximum specific growth rate and loss tangent of the gels (tanδ = G″/G′) was explored, pointing to the possible use of a single structural parameter to describe food matrix effects on microbial growth kinetics. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Over the last 30 years, Listeria monocytogenes has become increasingly important as a food-associated pathogen. Although, human listeriosis is not very usual, with the European Union incidence rate being 0.41 cases per 100,000 citizens, the EU case fatality rate was 17.8% among the majority of the confirmed cases. L. monocytogenes infections are also responsible for the highest hospitalization rates (91.6%) among all zoonoses under EU surveillance, while the majority of the cases were domestically acquired (EFSA/ECDC, 2014). Food products constitute different types of ecosystems, depending on the environment and the microorganisms that are harbored (Mossel & Ingram, 1955), and as composite matrices of multiple constituents and phases are characterized by great structural complexity. In order to study microbial growth in such systems and to elucidate how specific microorganisms impact food safety and quality, the way that food microstructure (structure on micro-level) affects the growth of different microorganisms has to be explored (Robins & Wilson, 1994).

⁎ Corresponding author. Tel./fax.: +30 2310991647. E-mail address: [email protected] (K.P. Koutsoumanis).

http://dx.doi.org/10.1016/j.foodres.2014.07.031 0963-9969/© 2014 Elsevier Ltd. All rights reserved.

Microbial growth takes place in the aqueous phase of food products. The majority of food products are solid or semi-solid structured systems and are differentiated from liquid homogeneous media where microbial cell mobility is not constrained, displaying a planktonic growth pattern (Wilson et al., 2002). In many cases, however, structure in the aqueous phase of a food matrix is induced by gelation of different components in the medium. Food microstructure thus becomes a key element in order to control the microbial growth in foods, and thereby affects food safety in many ways, while it determines the method of antimicrobial agents' incorporation in formulated products (Corbo et al., 2009) as well as the redistribution of the added preservatives between the different phases in emulsions (Brocklehurst & Wilson, 2000). The main effect of food microstructure on microbial growth is the fact that cells may become immobilized and their growth pattern turns from planktonic to colonial. Cell immobilization and growth in a structured medium may also cause alteration of cell morphology and physiological responses, affecting the thermal inactivation tolerance of the microorganisms (Mogollón, Marks, Booren, Orta-Ramirez & Ryser, 2009; Prachaiyo & McLandsborough, 2003), susceptibility to antibiotics (Jouenne, Tresse, & Junter, 1994; Junter, Coquet, Vilain, & Jouenne, 2002), protection from physicochemical stresses and cell membrane modification through the gene expression of some specific proteins

684

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

(Junter, Coquet, Vilain, & Jouenne, 2002). Cell immobilization and colonial growth also modify the local conditions in the growth environment; e.g., cell metabolism changes the substrate composition around the colony (Wimpenny et al., 1995) and acidic metabolites are accumulated, causing a drop of pH inside and around the colony (Malakar et al., 2002; Walker, Brocklehurst, & Wimpenny, 1997), and creating pH gradients in the growth medium (Walker, Brocklehurst, & Wimpenny, 1997; Wimpenny et al., 1995). In the case of L. monocytogenes, this pH drop is able to stimulate an auto-induced acid tolerance response (Kroll & Patchett, 1992), resulting in reduced susceptibility to added growth inhibitory agents (Wilson et al., 2002), but also providing cross-protection against other environmental stresses as well as increased virulence, possibly attributed to enhanced ability to overcome the harsh in vivo conditions (O'Driscoll, Gahan, & Hill, 1996). Food microstructure and immobilization of the microbial cells influence growth kinetics as they appear to exert additional stresses onto microbial growth (Brocklehurst, Mitchell, & Smith, 1997; Wilson et al., 2002). The microstructure of the growth medium may also influence the effect of the environment including temperature, water activity and pH on L. innocua growth (Antwi et al., 2006). Several studies have indeed shown that immobilized cells exhibit different metabolic activities (Skandamis, Tsigarida, & Nychas, 2000) and a slower growth rate compared to planktonic cells (Robins & Wilson, 1994). Wilson et al. (2002) have reported that cells of Bacillus cereus immobilized in gelatin gel exhibited a reduced growth rate compared to cells grown in broth. The same observations have been reported for Aspergillus carbonarious immobilized in gel where except for the reduced growth rate (estimated as biomass increase), the production of ochratoxin A was also reduced compared to that in liquid medium (Huang, Chapman, Wilson, & Hocking, 2009; Kapetanakou, Ampavi, Yanniotis, Drosinos, & Skandamis, 2011). Another feature that is related to immobilization and growth in a structured medium is a reduction in the growth yield (Brocklehurst, Parker, Gunning, Coleman, & Robins, 1995; Robins & Wilson, 1994). Moreover, Koutsoumanis, Kendall, and Sofos (2004) and Meldrum, Brocklehurst, Wilson, and Wilson (2003) have reported that cell immobilization may lead to narrower growth boundaries, concerning other environmental factors influencing bacterial growth. So far, most available predictive models for microbial growth are based on experimental data from liquid laboratory media and thus do not consider the significant effect of differentiated diffusion rates of nutrients, oxygen and metabolic end products, and of the mechanical constraints that microbial cells may experience due to the microstructure of the substrate (Robins & Wilson, 1994). This constitutes a completeness error, as described by Ross, McMeekin, and Baranyi (1999), and limits the ability of any microbial growth model to accurately predict the safety and quality of structured real food products, leading to fail-safe predictions and thereby causing economical losses. As a result, an extensive amount of research has been carried out recently to study structured systems using (mostly) agar or gelatin as solidifying agents in order to mimic the microstructure of gelled food products like pâté, cheeses or emulsion gels, like sausages (Noriega, Laca, & Díaz, 2008, 2009; Wilson et al., 2002). However, gels made from different gelling agents are differentiated regarding their properties and microstructure. Furthermore, the gel microstructure properties are affected by the concentration of the biopolymer used and by processing and storage conditions. A different microstructure may influence the impact each gel network exerts on microbial cells. To tackle this, diverse microstructures should be studied in order to clarify the influence of microstructure and to identify a universal parameter to describe the effect of substrate's microstructure on the microbial growth kinetics. Sodium alginate and gelatin are typical biopolymers that form self-supporting gels and are used in many food and pharmaceutical products as structuring hydrocolloids. Alginates are polysaccharides produced by brown seaweeds and algae. They are linear copolymers of D-mannuronic acid and L-guluronic

acid. Each chain normally contains continuous blocks of the constituent sugars and regions where the two residues alternate (Smidsrød, 1970). Divalent cation (Ca2+) binding and gel formation are the most important features of alginates. This property is attributed to a specific and strong interchain interaction between stretches of guluronic acid blocks (G-blocks) and the divalent ions leading to the formation of junction zones. Grant, Morris, Rees, Smith and Thom (1973) have proposed the egg-box model to describe this gelling mechanism. Each cross-linking Ca2+ ion interacts with two adjacent G residues as well as with two adjacent residues in the opposing chain; this physical ionotropic interaction connecting alginate chains results in hydrogel formation (Donati & Paoletti, 2009). Gelatin is readily dissolved in water when heated to 40–50 °C and it remains in random coil conformation, unless the solution is cooled below 30 °C where a reverse coil to helix transition takes place. Then the gelatin molecules reassemble to a triple-helix conformation and form a thermoreversible viscoelastic gel network stabilized by extensive interchain H-bonding (Djabourov, Leblond, & Papon, 1988). The aim of the present work was to explore the effect of growth medium microstructure on the growth of the food-borne pathogen L. monocytogenes. Specifically, the main objectives were: i) to study the growth kinetics of L. monocytogenes when inoculated in gels formed by different biopolymers, ii) to evaluate the influence of each gelling system on the growth of the pathogen, and iii) to investigate the factors affecting the influence of microstructure on microbial growth such as the storage temperature and the presence and concentration of glucose in the growth medium. The endmost aim of the study was to investigate structural parameters that could be included in predictive growth models, to take into account the influence of microstructure on microbial proliferation and thus to simulate the environment of real food product matrices. 2. Materials and methods 2.1. Microorganism and inoculum preparation L. monocytogenes FSL R2-500 (serotype 4b), which was isolated from a Mexican type cheese during a listeriosis outbreak (in 2000) in North Carolina, United States, was used. The isolate was kindly provided by Dr. Martin Wiedmann (Cornell University, Ithaca, New York) and deposited in the strain collection of the Laboratory of Food Microbiology and Hygiene of Aristotle University of Thessaloniki. The stock culture was stored frozen (−70 °C) onto Microbank™ porous beads (Pro-Lab Diagnostics, Ontario, Canada), whereas the working culture was stored at 5 °C on Brain Heart Infusion Agar (BHIA, LAB M, Lancashire, United Kingdom) slants and was renewed bimonthly. The inoculum was activated by transferring a loopful from the BHIA slant into 9 mL of Brain Heart Infusion Broth (BHI, LAB M) and incubated at 30 °C for 24 h. Aliquots of the activated culture were transferred into 9 mL of fresh BHI, incubated at 30 °C for 24 h and then the cultures were used for the preparation of the test inoculum. The initial concentration of the inoculum was determined by viable plate counting on BHIA. For the experiments with milk powder, after the activation of the culture, portions of the activated culture were transferred into 9 mL of fresh BHI and incubated at 30 °C. 24 h-cultures were centrifuged at 6000 rpm for 20 min at 4 °C. The harvested cells were then washed with 9 mL of quarter-strength Ringer's solution (Lab M) and centrifuged as described previously. The harvested cells of the washed cultures were resuspended in 9 mL of Ringer's solution. 2.2. Kinetic analysis of microbial growth In all the experiments, the samples were analyzed in appropriate time intervals to obtain the effective kinetic analysis of microbial growth. Two or four independent experiments were conducted at each set of growth conditions.

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

2.2.1. Growth medium: alginate gels The experiments using sodium alginate aimed to examine and compare the growth behavior of L. monocytogenes in broth and calcium alginate gels (3% w/w): i) under isothermal conditions at 4 and 12 °C; ii) supplemented with 0.2% (w/v) glucose under isothermal conditions at 4 °C; and iii) under structure fluctuating conditions at isothermal conditions (4 °C). In this respect, sodium alginate (Duchefa Biochemicals, Haarlem, the Netherlands) was added to heat sterilized Nutrient Broth (NB, LAB M) at 80 °C. Calcium sulfate dihydrate (Merck, Darmstadt, Germany) was also dissolved in another portion of heat sterilized NB to prepare a 5% (w/v) solution. The sodium alginate solution was cooled to 40 °C and inoculated with a diluted inoculum, resulting in a final inoculum concentration of 103 CFU/mL. Nine parts of sodium alginate solution were aseptically mixed with one part of calcium sulfate solution and thoroughly stirred for 1 min. Aliquots of the mixture were transferred to 35 mm diameter plastic sterile Petri dishes and approximately 20 to 30 dishes were prepared per treatment. The Petri dishes were subsequently covered with parafilm to avoid water evaporation, left for 3–4 min to enhance solidification and then stored in high-precision low temperature incubators (± 0.1 °C) under isothermal conditions (4 or 12 °C depending on the experimentation). The same procedure was also followed in samples containing glucose. The quantity of glucose was added to NB just after heat sterilization. For samples with induced gelation at a certain time, sodium alginate solution was initially inoculated with a dilution of the inoculum, resulting in a final inoculum concentration of 103 CFU/mL and kept under isothermal conditions at 4 °C for growth. After 10 days, the viscous solution was converted into gel by the addition of calcium cations and stored at 4 °C for further monitoring of the growth response.

2.2.2. Growth medium: gelatin gels The experiments using gelatin aimed to examine the growth behavior of L. monocytogenes in: i) gelatin gels (0, 3.4, 15.0, 22.0, 30.0% (w/w)) under isothermal conditions at 4 °C; ii) gelatin gels (30.0% (w/w)) under isothermal conditions at 12 °C; and iii) gelatin gels (30.0% (w/w)) supplemented with 0.2% or 0.5% (w/v) glucose under isothermal conditions at 4 °C. Gelatin (gelatin approximately 225 Bloom from porcine skin, Sigma Aldrich, St. Louis, United States) was added to a heat sterilized ΝΒ at 80 °C. Gelatin solutions were cooled to 40 °C and inoculated with a diluted inoculum, resulting in a final inoculum concentration of 103 CFU/mL. Aliquots of the gelatin solution were transferred to 35 mm diameter plastic sterile Petri dishes and approximately 20 to 30 dishes were prepared per treatment. The samples were stored in high-precision low temperature incubators under isothermal conditions (4 or 12 °C depending on the experimentation).

2.2.3. Growth medium: a model dairy product The growth behavior of L. monocytogenes in gels of low fat milk powder (10.0, 20.0, 40.0% w/w), as a model gelled dairy product, was investigated under isothermal conditions at 4 °C. Low fat milk powder (Arla Foods, Denmark) (10.0, 20.0, 40.0% w/w) was added to heat sterilized distilled water at room temperature. Reconstituted milk was pasteurized at 63 °C for 30 min, then cooled to 35 °C and 300 μL of 10% (w/w) calcium chloride (Merck) solution were added in each portion of 30 g of the milk powder solution. The mixture was inoculated with a diluted inoculum, resulting in a final inoculum concentration of 103 CFU/mL and subsequently 400 μL of 25% (w/w) rennet (Calf Rennet, Renco, New Zealand) solution were added for each 30 g of milk solution. After stirring (~1 min), aliquots of this mixture were transferred to 35 mm diameter plastic sterile Petri dishes and approximately 20 to 30 dishes were prepared per treatment. The samples were stored in highprecision low temperature incubators under isothermal conditions (4 °C).

685

2.3. Microbiological analysis At regular time intervals, a sample was transferred to a sterile plastic stomacher bag (Sterile bags for Bag Mixer 400), and was weighed and then a known volume (dilution factor 1/10) of diluent (Ringer's solution) was added. The sample was subsequently homogenized by mixing for 120 s in a stomacher (Bag Mixer 400, Interscience, France). For the enumeration of L. monocytogenes, 0.1 mL of appropriate serial decimal dilution of the homogenized gel was surface plated on BHIA and incubated at 30 °C for 48 h. Gelatin samples were heated to 40 °C for 1.5 min in a water bath until melting of the gel prior to homogenization. 2.4. Estimation of growth parameters Growth data obtained from the enumeration of L. monocytogenes were fitted using the DMfit Excel Add-In software which is based on the model of Baranyi and Roberts (1994) and the growth parameters (lag time (λ) and maximum specific growth rate (μmax)) were estimated. The model describes the natural logarithm of the number of the cells y(t) = lnx(t) by the equation: yðt Þ ¼ y0 þ μ max Aðt Þ−

! mμ Aðt Þ 1 e max −1 ln 1 þ mðy m e max− y0 Þ

ð2:1Þ

with μmax (1/h) the maximum specific growth rate, ymax the natural logarithm of the maximum cell number, y0 the natural logarithm of the initial cell population, m the curvature parameter to characterize the transition from the exponential phase, and A(t) determines λ which is described by the equation: Αðt Þ ¼ t þ

1 μ max

  t−h0 −μ t −h −μ ln e max þ e 0 þ e max

ð2:2Þ

where h 0 is a dimensionless parameter quantifying the initial physiological state of the cells. From that, the lag time λ (h) can be calculated as h0 / μmax. The fitting of the curves was assessed with the coefficient of determination (R2). The decimal logarithm of the reciprocal of the relative maximum specific growth rate (Relμmax) of the pathogen for each concentration of the three gelling systems at 4 °C was expressed as a function of the loss tangent. The term Relμmax denotes the maximum specific growth   rate μ maxgel of the pathogen for each concentration of the three gelling systems divided by the maximum specific growth rate in the respective   liquid substrate μ maxliq under the same growth conditions. Data were fitted to the biphasic model using the Excel v4 format of the curvefitting program TableCurve 2D (Systat Software Inc., San Jose, CA, United States):  Log

!  1 1 þ Log ð f  expð−k1  tanδÞ þ ð1−f Þ  expð−k2  tanδÞÞ ¼ Log Relμ max Rel μ max0

ð2:3Þ where f and (1 − f) are the fractions of 1/Relμmax values that express different dependencies on tanδ, Relμ max0 is the lowest value that the relative specific growth rate can take when tanδ approaches zero, k1 and k2 are the slopes of the two phases of the curve, and tanδ is the loss tangent of the gels (see Section 2.5). 2.5. Rheological measurements Rheological measurements were carried out to obtain a more quantitative description of the gel properties. These measurements allow for an explicit illustration of gel strength better than polymer concentration,

686

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

regardless of the chemical structure of the polymer used or the gelation conditions (Theys et al., 2008). The rheological measurements of the samples were performed by a rotational Physica MCR 300 rheometer (Physica Messtechnic GmbH, Stuttgart, Germany) using a parallel plate geometry (50 mm diameter) or concentric cylinders. The temperature was regulated by a Paar Physica circulating bath and a controlled peltier system (TEZ 150P/MCR) with an accuracy of ± 0.1 °C. To minimize dehydration, a solvent trap around the measuring geometry was employed. Small deformation oscillatory measurements for evaluation of the viscoelastic properties, G′ (storage modulus), G″ (loss modulus), and tanδ (G″/G′), were performed over the frequency range of 0.1–10 Hz at 4 °C. A frequency sweep test was carried out after incubation of the gels at 4 °C for 24 h. The storage modulus was found to be constant after 24 h up to 30 days of incubation at 4 °C. The linear viscoelastic region was also assessed by strain sweep experiments. Storage modulus is the elastic component of a viscoelastic material, which indicates the amount of energy stored, and loss modulus is the viscous component which indicates the amount of energy dissipated. Loss tangent (tanδ = G″/G′) is the ratio between the viscous and elastic properties; with a tanδ value of 1, the elastic and viscous properties of the material are equal. A small tanδ value means that the material can be described as solid-like, while a high tanδ means that the material behaves like a viscous fluid; when this value is lower than 1, the system is characterized as an elastic gel. For an ideal gel that behaves elastically, the G′ value is also expected to be independent of the frequency and G′≫ G″ (Moschakis, Lazaridou, & Biliaderis, 2012). Generally, if the loss tangent is smaller than 0.1 then the system is characterized as a true gel. 3. Results and discussion 3.1. Effect of gelling agent A series of experiments with liquid substrates (NB) and gels inoculated with L. monocytogenes were performed, aiming at evaluating the growth kinetics of the pathogen in different substrate physical environments. Population changes of L. monocytogenes upon storage at 4 °C in broth as well as calcium alginate (3.0%) and gelatin (3.4%) gels are shown in Fig. 1. As can be seen, the concentration of the pathogen after 8 days of incubation increased 3.5 logs in broth, while the respective population changes were about 1.2 and 2.0 logs in the calcium alginate and gelatin gels. Clearly, the microorganism grew faster in the liquid medium compared to the alginate and gelatin structured systems. The results indicate that bacterial cell immobilization, by the addition of a gelling agent in the liquid substrate of constant chemical composition, 6

imposes an additional stress hurdle on microbial growth. Similar observations have also been reported in several studies, showing slower growth rate and narrower growth boundaries for L. monocytogenes in gelatin (Meldrum, Brocklehurst, Wilson, & Wilson, 2003) and agar gels (Koutsoumanis, Kendall, & Sofos, 2004). A similar behavior was also observed in other bacteria such as Salmonella Typhimurium (Brocklehurst, Mitchell, & Smith, 1997; Skandamis, Tsigarida, & Nychas, 2000; Theys et al., 2008) and L. innocua (Antwi et al., 2006; Lebert, Dussap, & Lebert, 2004) which grew faster in broth than in a gelled substrate. Bacterial cell immobilization suppresses the growth, while colony formation alters local conditions in the growth environment of the microorganism (Robins & Wilson, 1994). Different mechanisms have been proposed to explain the way that the substrate's microstructure affects microbial growth, including nutrient and end-product diffusion, oxygen availability and mechanical hindrance exerted from the immobilization matrix (Koutsoumanis et al., 2004). The primary model (Eq. 2.1) was fitted on growth data to calculate the kinetic parameters, that is, maximum specific growth rate (μmax) and lag phase (λ). As can be seen from the R2 values (Table 1), there is a good agreement between the model and the experimental observations. Each gelling system had a different impact on the pathogen's growth kinetic responses possibly due to its particular microstructure. The μmax and λ values for the alginate gels were higher than those of the gelatin gels. Gelatin gels (3.4%) and calcium alginate gels (3.0%) were characterized by a similar storage modulus (G′) value (~1400 Pa), but they had extremely different loss tangent (tanδ) values (see Table 2). The tanδ value for gelatin gels was less than 0.1, while for the alginate gels was 0.53 at 1 Hz. In general, gelatin gels exhibit great elasticity values and are considered as true gels (Saltzman, Radomsky, Whaley, & Cone, 1994). Based on the growth data, the expanding microcolonies of the pathogen have to manage the same pressure from the local microenvironment, as it is described by the gel strength parameter, but they have a different opportunity to modify this region and gain the necessary space in order to grow, as it is described by tanδ. Thus, it seems that the microbial growth responses are not directly related with a single mechanical parameter, but also other factors of the gel microstructure may play an important role. It is suggested that this significant divergence in loss tangent may account for the different growth behavior of the pathogen in these gel network systems. Apart from that, these gels are differentiated by their porosity. The relatively larger pore size of alginate gels (estimated at 5–500 nm; Smidsrød & Skjåk-Bræk, 1990) and their ‘open’ structure may be attributed to the less extensive cross-linking of the polymer chains, while the smaller pore size of gelatin gels (~ 10 nm; Saltzman, Radomsky, Whaley, & Cone, 1994) may stem from the three helical structures that form extensive junction zones among the protein molecules. Karathanos and Saravacos (1993) have reported that any increase in gel porosity causes an increase in

5 Table 1 Parameters of the Baranyi and Roberts model for growth of Listeria monocytogenes at 4 °C in gels of alginate, gelatin and milk powder of various concentrations.

Log (Nt/N0)

4 3 Broth

2

Alginate gel

1

Gelatine gel

0 0 -1

5

10

15

20

Time (days)

Fig. 1. Mean population (Log (Nt/N0) ± standard deviation, n = 4) of Listeria monocytogenes at 4 °C in broth, 3.0% (w/w) calcium alginate gels and 3.4% (w/w) gelatin gels. Nt and N0 represent the mean population of the pathogen at time t and 0, respectively.

Type of system Alginate Gelatin Gelatin Gelatin Gelatin Gelatin Milk Model cheese Model cheese Model cheese a b c

Concentration of polymer % w/w 3.0 0.0 3.4 15.0 22.0 30.0 10.0 10.0 20.0 40.0

μmax (h−1) ± sda

Lag phase (d) ± sdb

R2 (min–max)c

0.049 0.054 0.031 0.030 0.024 0.015 0.038 0.034 0.034 0.033

5.52 2.13 1.86 3.95 3.54 7.25 1.00 3.65 5.05 6.28

0.972–0.992 0.995–0.998 0.984–0.998 0.990–0.998 0.992–0.997 0.965–0.984 0.997–0.997 0.992–0.996 0.988–0.993 0.977–0.993

± ± ± ± ± ± ± ± ± ±

0.003 0.004 0.001 0.002 0.001 0.002 0.001 0.002 0.001 0.001

± ± ± ± ± ± ± ± ± ±

1.05 0.61 0.31 1.29 0.16 0.67 0.15 0.62 0.30 0.32

Mean values (±standard deviation, n = 4) of maximum specific growth rate. Mean values (±standard deviation, n = 4) of lag phase. Coefficient of determination (minimum and maximum values for data fitting, n = 4).

Z. Aspridou et al. / Food Research International 64 (2014) 683–691 Table 2 Storage modulus (G′) and loss tangent (tanδ) values (4 °C, frequency 1 Hz) of different gels with various concentrations of alginate, gelatin and milk powder. Type of system

Concentration of polymer % w/w

G′ (Pa)

tanδ

3.0 3.4 15.0 22.0 30.0 10.0 20.0 40.0

1460 1480 7870 14,500 23,800 49 276 2815

0.526 0.064 0.041 0.035 0.023 0.197 0.201 0.231

Alginate Gelatin Gelatin Gelatin Gelatin Model cheese Model cheese Model cheese

diffusion rate of solutes. It is likely that such a difference in pore size of the gels could affect the diffusion rate of nutrients and metabolites and consequently the microbial growth. 3.2. Effect of gelation induction A solution of sodium alginate (3.0%), inoculated with the pathogen, was initially stored at 4 °C for 10 days, and then gelation was induced with addition of calcium ions. Fig. 2 presents the population evolution of L. monocytogenes under structure fluctuating conditions at 4 °C. A gel microstructure development, indicated in the graph by the black arrow, seems to cause suppression to microbial growth which is reflected by an additional lag phase or a reduced growth rate in the growth curve. These data imply that the substrate's gelation may constitute an additional stress that does not allow the microorganism to continue biosynthesis and growth with the same rate as in the liquid medium, and possibly spends available cellular energy to be adapted to the new environment. Bacterial growth at optimal environmental conditions takes place at a maximum rate which is characteristic for each particular microorganism. Any divergence from the optimal conditions usually results in a decrease of the maximum growth rate and imposes an additional stress for the growth of the microorganism (Moat, Foster, & Spector, 2003). The gel formation seems to cause a physical restraint if the expanding colony is not able to find the necessary space among the structural components of the matrix (Robins & Wilson, 1994) and thus bacterial cells in the periphery of the colony are subjected to an additional stress. Schimel, Balser, and Wallenstein (2007) suggested that cells try to manage this stress through adaptation and acclimation mechanisms. The growth suppression is possibly linked to the induction of protection and response mechanisms which are energy consuming, thus the 6

Log (Nt/N0)

5 4 3 2 1 0 0

5

10

15

20

Time (days) Fig. 2. Listeria monocytogenes population change at 4 °C under structure fluctuating conditions with the sol to gel transition occurring at day 10 (arrow) (3.0% w/w sodium alginate solution converting to 3.0% w/w calcium alginate gel). Each point is a mean value (n = 4) ± standard deviation.

687

remaining amount for biosynthesis and cellular division is restricted (McMeekin, Hill, Wagner, Dahl, & Ross, 2010). This is manifested by a reduced growth rate or even an additional lag phase if the exerted stress is rather severe. A possible means that bacterial cells use to confront the gelled environment is related to their ability for local destruction of the microstructure via induction and activation of hydrolytic enzymes. Studies having been conducted with immobilized cells of Escherichia coli showed an increased enzyme activity as a result of cell immobilization (Junter et al., 2002). Sodium alginate could be thus hydrolyzed by some microorganisms to smaller size components that could serve as carbon sources, e.g. alginate lyases degrade sodium alginate by breaking 1,4 glycosidic linkages. These enzymes have been isolated from Azotobacter vinelandii and Pseudomonas aeruginosa (Draget, Smidsrød, & Skjåk-Bræk, 2005) thus there is a possibility that L. monocytogenes might possess them, too. It is known that this pathogen can use mannose, one of the monomers of alginate chains in the form of mannuronic acid, as a carbon source. Furthermore, Shumi, Hossain and Anwar (2004) demonstrated that L. monocytogenes produces proteases that hydrolyze gelatin and skimmed milk caseins. This procedure of induced synthesis-activation of hydrolytic enzymes could be beneficial for bacterial cells in terms of available space for growth or enhanced diffusion of nutrients through the increase in the pore size of the gel. However, it was found that microbial growth rates were similar in broth and in sodium alginate solutions (results not shown), and therefore, the possibility that the microorganism could use sodium alginate just for a carbon source within the time frame of the experiments is rather low. Nevertheless, it should be noted that even though extensive degradation of sodium alginate does not take place by the growing culture, a minor fraction of broken glycosidic linkages can greatly affect the chain connectivity, and thus the gel network integrity. 3.3. Effect of temperature Fig. 3a shows the evolution of L. monocytogenes population at 4 and 12 °C both in broth and calcium alginate gels. It can be observed that the effect of structure on microbial growth is largely influenced by temperature. The effect of the substrate's microstructure is quite pronounced at the lower temperature, but it seems to lessen when the temperature was increased. Specifically, at 4 °C the pathogen's concentration in the gel was approximately 2 logs lower compared to broth after 8 days of incubation, while at 12 °C the population in broth was about 0.5 log units higher compared to that of gel throughout the incubation period. Similar findings were obtained regarding the influence of temperature on the gelatin system (30.0% w/w). The temperature rise caused a decrease in the extent of suppression induced by the substrate's microstructure (Fig. 3b). L. monocytogenes concentration was at maximum of about 4 logs higher in the broth than in the gelatin gel at 4 °C and about 2 logs higher at 12 °C. It is also important to note that due to the high concentration (30.0% w/w) of gelatin used for network formation, the effect of microstructure was so profound that the temperature increase (from 4 °C to 12 °C) was not enough to eliminate the difference in the pathogen's growth responses between broth and gel. The fact that the pathogen's growth suppression was not considerably high when the temperature increased towards the optimum value for the microorganism's growth indicates that the effect of substrate's microstructure is substantially significant under additional stress conditions which further influence the microbial growth kinetics according to the hurdle theory. Several research reports are in agreement with these findings. Wilson et al. (2002) have noted that the additional stress caused by bacterial cell immobilization was more intense under less favorable environmental conditions. Similar observations have been demonstrated for other factors that can influence the effect in structured environments such as pH (Meldrum et al., 2003; Robins & Wilson, 1994). Different mechanisms have been proposed to explain

688

A

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

6 5

Log (Nt/N0)

4

3

Broth at 4 °C Alginate gel at 4 °C

2

Broth at 12 °C Alginate gel at 12 °C

1 0 0

4

8 Time (days)

-1

B

12

16

6 5

Log (Nt/N0)

4 3 2

Broth at 4 °C

metabolic exhaustion (Leistner, 2000). Such energy depletion can also result in reduced growth rates. To the best of our knowledge, most of the available studies concerning structured environments have been conducted with substrates containing glucose and rather limited information is available regarding substrate's microstructure effect in the presence/absence of glucose. Changes in L. monocytogenes populations in broth and in calcium alginate gels with or without glucose are presented in Fig. 4a. It can be observed that the addition of glucose affected pathogen's growth in gels. In the absence of glucose, the effect of the microstructure was found to be about 2 log-unit difference in population increase on day 6 at 4 °C, while in the glucose containing medium this difference was only 0.5 logs. Therefore, it may be suggested that the enhancement of the pathogen growth is due to the glucose addition in the medium. Similar results were obtained for the gelatin system. As can be seen from Fig. 4b, the pathogen's concentration in broth was 4 logs higher compared to gels without glucose, while it was 3 logs higher when 0.2% (w/v) glucose was added. With a further increase of glucose concentration to 0.5% (w/v), the differences in microbial population between the liquid substrate and the gelled media became smaller, implying that the diffusion of glucose plays an important role on the bacterial growth. Obviously, there is a different response to the addition of glucose between the two systems. In the case of calcium alginate gels the addition of 0.2% (w/v) glucose was sufficient to almost eliminate the structure effect on the microorganisms, while for the gelatin system an effect was

Gelatine gel at 4 °C

1

Broth at 12 °C Gelatine gel at 12 °C

0 0

5

-1

10

15

20

25

A

30

6 5

Time (days)

Log (Nt/N0)

4 Fig. 3. a) Mean population (Log (Nt/N0) ± standard deviation, n = 2) of Listeria monocytogenes at 4 °C (open symbols) and 12 °C (solid symbols) in broth and 3.0% (w/w) calcium alginate gels. b) Mean population (Log (Nt/N0) ± standard deviation, n = 2) of L. monocytogenes at 4 °C (open symbols) and 12 °C (solid symbols) in broth and 30.0% (w/w) gelatin gels.

3 Broth Alginate gel

2

Broth + 0.2% Glu

3.4. Effect of glucose A phenomenon that is possibly related with the differences in growth between planktonic and immobilized cells is nutrient diffusion. Bacterial colonies consume nutrients from their local microenvironment and then obtain substances from the surroundings (Pirt, 1967). Nutrient supply from the solid medium will continue to take place by molecular diffusion. Molecular diffusion of both substrate molecules and metabolites in solid media can lead to formation of concentration gradients around the expanding colonies affecting their growth behavior (Brocklehurst et al., 1997; Malakar et al., 2002; Walker et al., 1997; Wimpenny et al., 1995). For example, local accumulation of endproducts (particularly organic acids) can delay the colony growth. On the other hand, depending on the metabolic requirements of a microorganism, diffusional limitations for nutrient supplies may occur and can lead to a significant reduction of the cell energy production and

Alginate gel + 0.2% Glu

0 0

4

8

-1

B

12

16

Time (days) Broth

6

Broth +0.2% Glu Gelatin gel

5

Gelatin gel +0.2% Glu

Log (Nt/N0)

the combined effect of temperature and substrate's microstructure. A temperature rise causes a chain loosening effect in the network microstructure and an increase in the diffusion coefficient of solutes (Øyaas, Storrø, Svendsen, & Levine, 1995), possibly resulting in a greater nutrient supply to the growing colony. Nutrient abundance may be thus responsible for the differences in growth rate between broth and gels. Furthermore, temperature rise can lead to reduced polymer chain rigidity and increased molecular mobility (Ferry, 1948) and this may have an impact on reducing the mechanical restraint from the gel network on the bacterial cells.

1

Gelatin gel +0.5% Glu

4 3 2 1 0 0 -1

2

4

6

8 10 12 Time (days)

14

16

18

20

Fig. 4. a) Mean population (Log (Nt/N0) ± standard deviation, n = 2) of Listeria monocytogenes at 4 °C with 0.2% (w/v) glucose (solid symbols) or without glucose (open symbols) in broth and 3.0% (w/w) calcium alginate gels. b) Mean population (Log (Nt/N0) ± standard deviation, n = 2) of L. monocytogenes at 4 °C with 0.2% or 0.5% (w/v) glucose (solid symbols) or without glucose (open symbols) in broth and 30.0% (w/w) gelatin gels.

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

observed even when 0.5% (w/v) glucose had been added. This different behavior could be attributed to the pore size of the gels. The gelatin gel network is characterized by a smaller pore size which besides the nature of the junction zones is also related to the higher polymer concentration. In calcium alginate gels, possibly due to the larger pore size, diffusion of glucose was facilitated and thus the polymer microstructure effect is diminished with a small addition of glucose. However, it must be noted that the two gel systems consisted of different polymer concentration and hence rheological properties. Another possible mechanism could be the plasticizing effect of such a small molecule, like glucose, that could increase free volume in the polymer network structure and thus enhance molecular mobility. However, the rheological measurements of alginate and gelatin gels with glucose added showed that the G′ values were practically unaffected by the presence of glucose at such low concentration levels. Therefore, it is believed that diffusion of glucose is more important than any modification of the network mechanical strength in controlling the pathogen growth in these composite gels. 3.5. Effect of solid concentration 3.5.1. Gelatin system The evolution of population of L. monocytogenes was also studied in gelatin gels, varying in polymer concentrations, at 4 °C (Fig. 5). After 16 days of incubation, the concentration of the pathogen increased by about 5.0 logs in broth and 4.3, 3.6, 3.1, 1.2 log-units in 3.4, 15.0, 22.0, 30.0% gelatin gels, respectively. It is evident from Fig. 5 that the growth suppression exerted by the solid substrate increased with increasing gelatin concentration. It is also worth pointing out that the addition of gelatin did not affect the water activity values of the medium in the concentration range examined (results not shown). The primary model (Εq. (2.1)) was fitted on the growth data to calculate the kinetic parameters (μmax and λ). As can be seen from the R2 values (Table 1) there is a good agreement between the model and the experimental observations for all gelatin concentrations. The μmax values were found to decrease with increasing gelatin concentration; i.e. μmax in 3.4% gelatin gels decreased by 42% compared to that in broth, while this reduction was 45%, 56%, and 72% for the systems made from 15.0%, 22.0%, and 30.0% (w/w) gelatin, respectively. As can be seen from Table 1, the decrease in μmax was initially steep when the gelatin concentration increased from 0 to 3.4% which was more or less expected since immobilization of the cells was initiated and the growth pattern turned from planktonic to colonial. This could be attributed to a possible increase in the physical constraint exerted on the bacterial culture by the immobilization matrix. In a similar study using gelatin to induce gelation of the medium, Theys et al. (2008) demonstrated that the increase in gelatin

689

concentration resulted in a decrease of S. Typhimurium growth rate. Antwi et al. (2006) also investigated the effect of gelatin concentration on the growth of L. innocua and found that the μmax values decreased when the polymer concentration increased until 5% (w/v), while within the range of 5% to 20% (w/v) no significant change in the growth rate was observed. A further increase in gelatin concentration caused an additional decrease in μmax which remained constant at 30% (w/v) gelatin concentration. These findings are in agreement with those of Brocklehurst et al. (1997) for S. Typhimurium. These studies imply that the amount of gelatin or any enhancement of the gel strength is not directly related with the growth rate. On the other hand, the data of the present work demonstrated that there is a continuous decline in the μmax with increasing gelatin concentration. The observed discrepancy between these studies could be attributed either to the different microorganism studied or to the experimental conditions such as the presence of glucose and any polymer modification before the gelation. Different underlying mechanisms could be responsible for the continuous decline in the microorganisms' growth rate with increasing gelatin concentration in the gels. The increase in polymer concentration may influence the diffusion rate of substances, the pore size of the gel, and the mechanical hindrance exerted by the structural components of the network structure. At first, it should be mentioned that several researchers have reported that in agar gels the diffusion rate of low molecular weight (MW) substances, such as glucose and low MW proteins, is practically unaffected by polymer concentration (Diaz, Wolf, Kostaropoulos, & Spiess, 1993; Stecchini et al., 1998). They also concluded that the diffusion does depend on the pore size of the gel and the molecular weight of the diffusing substances. This is in accordance with the findings of Smidsrød and Skjåk-Bræk (1990) concerning alginate gels. However, these observations were made by experiments conducted either with agar or alginate gels which are characterized by different microstructure, while other authors reported that diffusion of low molecular weight substances like glucose in gelatin gels was affected by varying the polymer concentration (Friedman & Kraemer, 1930; Hendrickx, Ooms, Engels, Pottelbergh, & Tobback, 1987). Theys et al. (2008) also concluded that oxygen limitations (decrease in the oxygen diffusion rate) may account for the drop in μmax of S. Typhimurium. This was not supported by the findings of the current study, since oxygen was not found to be a limiting factor for L. monocytogenes growth. Moreover, it has been demonstrated that the colony size and morphology of L. innocua were affected by increasing gelatin concentration (Antwi et al., 2006; Mitchell & Wimpenny, 1997; Stecchini et al., 1998) indicating an increase in the microstructure effects with a reduction in pore size and an increase in polymer strand thickness of the gel. The lag phase duration was also found to be affected by gelatin

6 6

5

Broth

5

15.0% 22.0%

Log (Nt/N0)

4

30.0%

3

Log (Nt/N0)

3.4%

4

3

Milk Gel 10.0% Gel 20.0%

2

2

Gel 40.0%

1 1

0

0

0 0

-1

5

10

15

20

25

30

5

10

15

20

Time (days)

Time (days)

Fig. 5. Mean population (Log (Nt/N0) ± standard deviation, n = 4) of Listeria monocytogenes at 4 °C in gels of various concentrations of gelatin.

Fig. 6. Mean population (Log (Nt/N0) ± standard deviation, n = 4) of Listeria monocytogenes at 4 °C in milk (10.0% w/w solids) and gels of various concentrations of skimmed milk powder.

690

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

1.00 0.80 0.60 0.40 0.20 0.00 0.00 Fig. 7. Mechanical spectra (4 °C, deformation 0.5%) of alginate (3.0% w/w), gelatin (3.4% w/w) and skimmed milk powder (10.0% w/w) gels.

concentration (Table 1). Generally, with an increase in gelatin concentration there was an increase in lag phase. 3.5.2. Model dairy product The model dairy product used mimics the structure of a real soft cheese, with the difference being that it is produced from skimmed milk powder. The effect of milk powder concentration on L. monocytogenes growth at 4 °C is illustrated in Fig. 6. In comparison to the pathogen's growth in liquid milk with 10.0% (w/w) solids, the microbial growth was suppressed in the model cheese gel containing the same solid level. It can be also seen that the higher the milk powder concentration in the milk gels, the greater the microbial growth suppression (Fig. 6). The primary model was fitted to the data and the growth parameters are given in Table 1. As can be seen the μmax decreased by 10% in the model cheese with 10.0% (w/w) solids, compared to the reconstituted milk with the same concentration of milk solids, while the decrease was 12 and 14% for the model cheese samples with 20.0 and 40.0% (w/w) solids, respectively. Here it should be also mentioned that the addition of the milk powder did not affect the water activity value of the medium. The lag phase duration was also found to be affected by the milk powder concentration. Specifically, the increase in solid concentration in the milk gels caused an increase in the lag phase duration. 3.6. Rheological characterization of model systems Rheological measurements have been carried out in an attempt to find a universal mechanical parameter that can describe the effect of microstructure regardless of the gelling agent used. The bacterial responses to structured systems are expected to vary depending on their type and composition (Meldrum et al., 2003). In this study, all comparisons made in mechanical properties among samples were carried out using dynamic oscillatory tests at a constant frequency of 1 Hz. Fig. 7 shows the frequency sweep (G′, G″) for alginate (3.0% w/w), gelatin (3.4% w/w) and model cheese (10.0% w/w milk powder) gels at 4 °C. It can be seen that the G′ of gelatin gels was practically unaffected by frequency, while for alginate and model cheese gels there was a slight frequency dependence. Gelatin gels are true gels exhibiting a typical elastic response, with G′ and G″ being constant over the frequency range examined and a significant difference between G′ and G ″ values; i.e. the tanδ was constant over the entire frequency range

Table 3 Estimated values and statistics for the parameters of the biphasic model describing the effect of tanδ on the Log (1/Relμmax). Parameter f k1 k2 Log (1/Rel μ max0 )

Estimated value ± standard error 0.886 53.696 0.233 0.929

± ± ± ±

0.027 7.780 0.199 0.085

R2 0.932

0.10

0.20

0.30

0.40

0.50

0.60

Fig. 8. Relationship of the loss tangent (tanδ) with the relative maximum specific growth rate (Relμmax) of Listeria monocytogenes (Eq. 2.3) in gels of different concentrations of alginate, gelatin and milk powder at 4 °C (solid line represents fitted values, symbols represent observed values).

tested. The G′ and tanδ (1 Hz) values at 4 °C for all the gels are summarized in Table 2. It is obvious that the G′ increased and tanδ decreased with increasing gelatin concentration. For the model dairy product, the G′ increased with increasing solid concentration, whereas there was a concomitant slight increase in tanδ. The rheological parameters of the gels (G′ and tanδ) were used to explore any relations between the gel microstructure and the pathogen's growth rate (μmax), regardless of the type and polymer concentration of the system. The μmax was inversely correlated with the storage modulus (correlation coefficient, r = −0.8, p b 0.05). The relative maximum specific growth rate of the alginate, gelatin and milk powder gels, as a whole group, was related with the loss tangent; a good relationship between the logarithm of the reciprocal of the Relμmax with tanδ was thus observed. The relative μmax was employed in order to alleviate the influence of the different chemical compositions of broth and milk solution on the microbial growth. The estimated parameters of the model are presented in Table 3. From Fig. 8, it can be seen that with increasing tanδ, (i.e., decrease in elasticity) there is an increase in relative μmax, which finally tends to be 1. It can be observed that for true gels (tanδ b 0.1) even with a small change in the loss tangent value, there is a pronounced response in the relative maximum specific growth rate. The highest relative maximum specific growth rate of the pathogen was observed in alginate gels characterized by the highest tanδ value 0.53 and on the other hand the lowest relative μmax was observed in gelatin gels (30.0% w/w) with tanδ 0.02. The difference in tanδ values between alginate gels (3.0% w/w) and gelatin gels (3.4% w/w) is reflected on the difference in the Relμmax of these systems. However, further studies are required to fully unravel the impact of different gel systems with varying tanδ values in the range of 0.1 to 0.5 on the μmax. To our knowledge, this is the first time that the maximum specific growth rate is linked to a single mechanical parameter which can be employed to describe the gel microstructure, regardless of the gelling agent used. 4. Conclusions L. monocytogenes growth was studied in three different gelling media. The growth of pathogen was faster in the liquid than in the respective gelled systems regardless of the gelling agent used, although each gelling system affected the microbial behavior in a different way. When gels, characterized by a similar G′, were compared, it was found that the microorganism grew faster in the gels with higher tanδ, indicating that gel elasticity is related with the growth rate better than the storage modulus, G′. In the present study the effect of including glucose in the gel matrix was also examined. Glucose was really important for both alginate and gelatin gels in diminishing the reduction in growth rate caused by the polymer network microstructure. Furthermore, when the temperature

Z. Aspridou et al. / Food Research International 64 (2014) 683–691

increased towards the optimum values for the pathogen's growth, the microbial growth suppression was moderated, implying that the substrate's microstructure affects microbial growth according to the hurdle theory. Gelation induction during incubation of a liquid medium with the culture was also found to impose a stress that suppresses microbial growth as reflected by an additional lag phase or a reduced growth rate. Overall, the findings of this work highlighted the need to incorporate a single parameter for describing structure of solid-like matrices in predictive models in order to derive more reliable predictions of the growth kinetics of pathogens in real food products. That is, the substrate's mechanical properties should be quantified and taken into account in the evaluation of the shelf life and safety of food products as they relate to growth of spoilage flora or pathogenic bacteria. References Antwi, M., Geeraerd, A. H., Vereecken, K. M., Jenné, R., Bernaerts, K., & Van Impe, J. F. (2006). Influence of a gel microstructure as modified by gelatin concentration on Listeria innocua growth. Innovative Food Science & Emerging Technologies, 7(1–2), 124–131. Baranyi, J., & Roberts, T. A. (1994). A dynamic approach to predicting bacterial-growth in food. International Journal of Food Microbiology, 23(3–4), 277–294. Brocklehurst, T. F., Mitchell, G. A., & Smith, A. C. (1997). A model experimental gel surface for the growth of bacteria on foods. Food Microbiology, 14(4), 303–311. Brocklehurst, T. F., Parker, M. L., Gunning, P. A., Coleman, H. P., & Robins, M. M. (1995). Growth of food-borne pathogenic bacteria in oil-in-water emulsions. 2. Effect of emulsion structure on growth-parameters and form of growth. Journal of Applied Bacteriology, 78(6), 609–615. Brocklehurst, T. F., & Wilson, P. D. G. (2000). The role of lipids in controlling microbial growth. Grasas Y Aceites, 51(1–2), 66–73. Corbo, M. R., Bevilacqua, A., Campaniello, D., D'Amato, D., Speranza, B., & Sinigaglia, M. (2009). Prolonging microbial shelf life of foods through the use of natural compounds and non-thermal approaches — a review. International Journal of Food Science and Technology, 44(2), 223–241. Diaz, G., Wolf, W., Kostaropoulos, A. E., & Spiess, W. E. L. (1993). Diffusion of lowmolecular compounds in food model systems. Journal of Food Processing and Preservation, 17(6), 437–454. Djabourov, M., Leblond, J., & Papon, P. (1988). Gelation of aqueous gelatin solutions. I. Structural investigation. Journal de Physique France, 49(2), 319–332. Donati, I., & Paoletti, S. (2009). Material properties of alginates. In B. H. A. Rehm (Ed.), Alginates: Biology and Applications, 13. (pp. 1–53). Springer Berlin Heidelberg. Draget, K. I., Smidsrød, O., & Skjåk-Bræk, G. (2005). Alginates from algae. Biopolymers online. Wiley-VCH Verlag GmbH & Co. KGaA. EFSA (European Food Safety Authority), & ECDC (European Centre for Disease Prevention, Control) (2014). The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2012. EFSA Journal, 12(2), 3547 312 pp. Available online: www.efsa.europa.eu/efsajournal. Ferry, J. D. (1948). Mechanical properties of substances of high molecular weight. IV. Rigidities of gelatin gels; dependence on concentration, temperature and molecular weight 1. Journal of the American Chemical Society, 70(6), 2244–2249. Friedman, L., & Kraemer, E. O. (1930). The structure of gelatin gels from studies of diffusion 1. Journal of the American Chemical Society, 52(4), 1295–1304. Grant, G. T., Morris, E. R., Rees, D. A., Smith, P. J. C., & Thom, D. (1973). Biological interactions between polysaccharides and divalent cations: the egg-box model. FEBS Letters, 32(1), 195–198. Hendrickx, M., Ooms, C., Engels, C., Pottelbergh, E. V., & Tobback, P. (1987). Obstruction effect of carrageenan and gelatin on the diffusion of glucose. Journal of Food Science, 52(4), 1113–1114. Huang, Y., Chapman, B., Wilson, M., & Hocking, A. D. (2009). Effect of agar concentration on the matric potential of glycerol agar media and the germination and growth of xerophilic and non-xerophilic fungi. International Journal of Food Microbiology, 133(1–2), 179–185. Jouenne, T., Tresse, O., & Junter, G. -A. (1994). Agar-entrapped bacteria as an in vitro model of biofilms and their susceptibility to antibiotics. FEMS Microbiology Letters, 119(1–2), 237–242. Junter, G. A., Coquet, L., Vilain, S., & Jouenne, T. (2002). Immobilized-cell physiology: current data and the potentialities of proteomics. Enzyme and Microbial Technology, 31(3), 201–212. Kapetanakou, A. E., Ampavi, A., Yanniotis, S., Drosinos, E. H., & Skandamis, P. N. (2011). Development of a model describing the effect of temperature, water activity and (gel) structure on growth and ochratoxin A production by Aspergillus carbonarius in vitro and evaluation in food matrices of different viscosity. Food Microbiology, 28(4), 727–735. Karathanos, V. T., & Saravacos, G. D. (1993). Porosity and pore size distribution of starch materials. Journal of Food Engineering, 18(3), 259–280. Koutsoumanis, K. P., Kendall, P. A., & Sofos, J. N. (2004). A comparative study on growth limits of Listeria monocytogenes as affected by temperature, pH and aw when grown in suspension or on a solid surface. Food Microbiology, 21(4), 415–422.

691

Kroll, R. G., & Patchett, R. A. (1992). Induced acid tolerance in Listeria-monocytogenes. Letters in Applied Microbiology, 14(5), 224–227. Lebert, I., Dussap, C. G., & Lebert, A. (2004). Effect of aw, controlled by the addition of solutes or by water content, on the growth of Listeria innocua in broth and in a gelatine model. International Journal of Food Microbiology, 94(1), 67–78. Leistner, L. (2000). Basic aspects of food preservation by hurdle technology. International Journal of Food Microbiology, 55(1–3), 181–186. Malakar, P. K., Martens, D. E., van Breukelen, W., Boom, R. M., Zwietering, M. H., & van't Riet, K. (2002). Modeling the interactions of Lactobacillus curvatus colonies in solid medium: consequences for food quality and safety. Applied and Environmental Microbiology, 68(7), 3432–3441. McMeekin, T. A., Hill, C., Wagner, M., Dahl, A., & Ross, T. (2010). Ecophysiology of foodborne pathogens: essential knowledge to improve food safety. International Journal of Food Microbiology, 139(Supplement(0)), 64–78. Meldrum, R. J., Brocklehurst, T. F., Wilson, D. R., & Wilson, P. D. G. (2003). The effects of cell immobilization, pH and sucrose on the growth of Listeria monocytogenes Scott A at 10 °C. Food Microbiology, 20(1), 97–103. Mitchell, A. J., & Wimpenny, J. W. T. (1997). The effects of agar concentration on the growth and morphology of submerged colonies of motile and non-motile bacteria. Journal of Applied Microbiology, 83(1), 76–84. Moat, A. G., Foster, J. W., & Spector, M. P. (2003). Microbial stress responses. Microbial physiology (pp. 582–611). John Wiley & Sons, Inc. Mogollón, M. A., Marks, B. P., Booren, A. M., Orta-Ramirez, A., & Ryser, E. T. (2009). Effect of beef product physical structure on Salmonella thermal inactivation. Journal of Food Science, 74(7), 347–351. Moschakis, T., Lazaridou, A., & Biliaderis, C. G. (2012). Using particle tracking to probe the local dynamics of barley β-glucan solutions upon gelation. Journal of Colloid and Interface Science, 375(1), 50–59. Mossel, D. A. A., & Ingram, M. (1955). The physiology of the microbial spoilage of foods. Journal of Applied Microbiology, 18(2), 232–268. Noriega, E., Laca, A., & Díaz, M. (2008). Modelling of diffusion-limited growth for food safety in simulated cheeses. Food and Bioproducts Processing, 86(2), 122–129. Noriega, E., Laca, A., & Díaz, M. (2009). Listeria growth under diffusional limitations in synthetic meats. International Journal of Food Science and Technology, 44(4), 725–734. O'Driscoll, B., Gahan, C. G. M., & Hill, C. (1996). Adaptive acid tolerance response in Listeria monocytogenes: isolation of an acid-tolerant mutant which demonstrates increased virulence. Applied and Environmental Microbiology, 62(5), 1693–1698. Øyaas, J., Storrø, I., Svendsen, H., & Levine, D. W. (1995). The effective diffusion coefficient and the distribution constant for small molecules in calcium-alginate gel beads. Biotechnology and Bioengineering, 47(4), 492–500. Pirt, S. J. (1967). A kinetic study of the mode of growth of surface colonies of bacteria and fungi. Journal of General Microbiology, 47(2), 181–197. Prachaiyo, P., & McLandsborough, L. A. (2003). Oil-in-water emulsion as a model system to study the growth of Escherichia coli O157:H7 in a heterogeneous food system. Journal of Food Science, 68(3), 1018–1024. Robins, M. M., & Wilson, P. D. G. (1994). Food structure and microbial-growth. Trends in Food Science & Technology, 5(9), 289–293. Ross, T., McMeekin, T. A., & Baranyi, J. (1999). Predictive microbiology and food safety. In K. R. Richard (Ed.), Encyclopedia of food microbiology (pp. 1699–1710). Oxford: Elsevier. Saltzman, W. M., Radomsky, M. L., Whaley, K. J., & Cone, R. A. (1994). Antibody diffusion in human cervical mucus. Biophysical Journal, 66(2, Part 1), 508–515. Schimel, J., Balser, T. C., & Wallenstein, M. (2007). Microbial stress-response physiology and its implications for ecosystem function. Ecology, 88(6), 1386–1394. Shumi, W., Hossain, T., & Anwar, M. N. (2004). Production of protease from Listeria monocytogenes. International Journal of Agriculture and Biology, 6(6), 1097–1100. Skandamis, P., Tsigarida, E., & Nychas, G. J. E. (2000). Ecophysiological attributes of Salmonella typhimurium in liquid culture and within a gelatin gel with or without the addition of oregano essential oil. World Journal of Microbiology & Biotechnology, 16(1), 31–35. Smidsrød, O. (1970). Solution properties of alginate. Carbohydrate Research, 13(3), 359–372. Smidsrød, O., & Skjåk-Bræk, G. (1990). Alginate as immobilization matrix for cells. Trends in Biotechnology, 8, 71–78. Stecchini, M. L., Del Torre, M., Sarais, I., Saro, O., Messina, M., & Maltini, E. (1998). Influence of structural properties and kinetic constraints on Bacillus cereus growth. Applied and Environmental Microbiology, 64(3), 1075–1078. Theys, T. E., Geeraerd, A. H., Verhulst, A., Poot, K., Van Bree, I., Devlieghere, F., Moldenaers, P., Wilson, D., Brocklehurst, T., & Van Impe, J. F. (2008). Effect of pH, water activity and gel micro-structure, including oxygen profiles and rheological characterization, on the growth kinetics of Salmonella Typhimurium. International Journal of Food Microbiology, 128(1), 67–77. Walker, S. L., Brocklehurst, T. F., & Wimpenny, J. W. T. (1997). The effects of growth dynamics upon pH gradient formation within and around subsurface colonies of Salmonella typhimurium. Journal of Applied Microbiology, 82(5), 610–614. Wilson, P. D. G., Brocklehurst, T. F., Arino, S., Thuault, D., Jakobsen, M., Lange, M., Farkas, J., Wimpenny, J. W. T., & Van Impe, J. F. (2002). Modelling microbial growth in structured foods: towards a unified approach. International Journal of Food Microbiology, 73(2–3), 275–289. Wimpenny, J. W. T., Leistner, L., Thomas, L. V., Mitchell, A. J., Katsaras, K., & Peetz, P. (1995). Submerged bacterial colonies within food and model systems: their growth, distribution and interactions. International Journal of Food Microbiology, 28(2), 299–315.