Modelling the effect of temperature, carbon dioxide, water activity and pH on growth and histamine formation by Morganella psychrotolerans

International Journal of Food Microbiology 128 (2008) 226–233

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

Modelling the effect of temperature, carbon dioxide, water activity and pH on growth and histamine formation by Morganella psychrotolerans Jette Emborg ⁎, Paw Dalgaard Department of Seafood Research, National Institute of Aquatic Resources, Technical University of Denmark, Søltofts Plads, Building 221, DK-2800, Kgs. Lyngby, Denmark

a r t i c l e

i n f o

Article history: Received 26 March 2008 Received in revised form 1 August 2008 Accepted 24 August 2008 Keywords: Growth dampening Yield factor Tuna Broth Expanded Logistic model

a b s t r a c t A mathematical model was developed to predict growth and histamine formation by Morganella psychrotolerans depending on temperature (0–20 °C), atmosphere (0–100% CO2), NaCl (0.0–6.0%) and pH (5.4–6.5). Data from experiments with both sterile tuna meat and Luria Bertani broth was used to develop the mathematical growth and histamine formation model. The expanded Logistic model with a growth dampening parameter (m) of 0.7 was used as primary growth model. A primary model for histamine formation during storage was obtained by combining the expanded Logistic growth model with a yield factor (YHis/CFU). 120 maximum specific growth rate (µmax)-values were generated for M. psychrotolerans and used to model the combined effect of the studied environmental parameters. A simple cardinal parameter type secondary model was used to model the effect of the four parameters on µmax. The maximum population density (log Nmax) was correlated with log (YHis/CFU) and a simple constrained polynomial (quadratic) secondary model was developed for the effect of the environmental conditions on these model parameters. The developed model describes the effect of initial cell concentrations, storage conditions and product characteristics on histamine formation. This is a significant progress compared to previously available models for the effect of storage temperature only. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Histamine fish poisoning (HFP) results from consumption of certain marine finfish when these contain more than 500–1000 ppm histamine. In seafood, histamine is formed by the bacterial enzyme histidine decarboxylase (HDC, EC 4.1.1.22) and the substrate is so-called free histidine which is part of the soluble extractives in finfish flesh. Concentrations of free histidine can be as high as 10 000 to 20 000 ppm for some finfish, particularly tuna species (Emborg et al., 2005; Fletcher et al., 1995). Finfish becomes toxic when specific HDC-producing bacteria, at some stage between catch and consumption, grow to high concentrations and form significant amounts of histamine (Lehane and Olley, 2000; Taylor, 1986). HFP is common and the intoxication occurs world-wide (Dalgaard et al., 2008; DeWaal et al., 2006; McLauchlin et al., 2006). Since the early 1990s, HFP has caused 32% of all the reported seafood-borne incidents of human disease in England and Wales whereas the corresponding value for the USA was 38%. HFP is a relatively mild intoxication with allergy like symptoms, but due to its frequent occurrence HFP contributes negatively to the consumer's image of seafood. In this way, HFP is problematic due to economic losses and because it counteracts potential health benefits from increased seafood consumption. To reduce the frequency of HFP, information about the bacteria actually responsible for histamine formation in the implicated products is essential. Specifically, a quantitative description of their growth and ⁎ Corresponding author. Tel.: +45 45254918; fax: +45 45884774. E-mail address: [email protected] (J. Emborg). 0168-1605/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijfoodmicro.2008.08.016

activity can be used to identify combinations of storage time, storage conditions and product characteristics that will reduce the risk of histamine formation in toxic concentrations. However, compared to the high number of HFP incidents, a surprisingly small number of studies have identified histamine-producing bacteria directly from the implicated finfish products (Dalgaard et al., 2008; Emborg and Dalgaard, 2006; Emborg et al., 2005; Havelka, 1967; Kanki et al., 2004; Kawabata et al., 1956; Lerke et al., 1978; Sakabe, 1973; Taylor et al., 1979). Interestingly, the early studies found mesophilic Enterobacteriaceae and particularly Morganella morganii responsible for histamine formation whereas, recent studies in Denmark and Japan found the psychrotolerant bacteria Morganella psychrotolerans and Photobacterium phosphoreum responsible for the production of histamine in toxic concentrations. This new information is important because these psychrotolerant bacteria can produce toxic concentrations of histamine in seafood even when products are stored chilled as requested according to the existing regulation for both EU and USA (EC, 2005; FDA/CFSAN, 2001). A recent study confirmed the importance of toxic histamine formation during chilled storage of seafood. In fact, average concentrations of more than 500 ppm histamine were observed in 18 out of 59 storage trials (31%) with naturally contaminated seafood when these were stored between −1 °C and +5 °C (Dalgaard et al., 2008). M. psychrotolerans has been responsible for the formation of toxic concentrations of histamine in both fresh vacuum-packed (VP) tuna and VP cold-smoked tuna that caused incidents of HFP (Emborg and Dalgaard, 2006; Emborg et al., 2005). In addition, M. psychrotolerans has been isolated as part of the dominating spoilage microflora in

J. Emborg, P. Dalgaard / International Journal of Food Microbiology 128 (2008) 226–233

modified atmosphere packed (MAP) fresh tuna stored at 2 °C, frozen and thawed MAP (26% CO2) garfish stored at 5 °C and lumpfish roe stored at 5 °C (pH 5.4 and with 3.5–4.8% NaCl in the water phase) (Basby et al., 1998; Dalgaard et al., 2006; Emborg and Dalgaard, 2006; Emborg et al., 2006; Emborg et al., 2005). More precise quantitative information, however, is not available concerning the effect of storage conditions and product characteristics on growth and histamine formation by M. psychrotolerans. Clearly, this makes it difficult to determine if histamine formation by this bacterium represents an important risk for a given chilled seafood product depending on storage conditions and product characteristics. The objective of the present study was to develop a mathematical model that allows growth and histamine formation by M. psychrotolerans to be predicted in relevant seafoods. Growth and histamine formation were determined for various combinations of storage conditions (temperature, CO2) and product characteristics (NaCl/aw and pH). Appropriate primary models for growth and histamine formation during storage were selected and then secondary models were developed for the effect of the environmental conditions on relevant primary-model parameters including lag time (tlag), maximum specific growth rate (µmax), maximum population density (Nmax) and the yield factor for histamine formation (YHis/CFU). 2. Materials and methods 2.1. Strains and pre-culturing A mixture of four strains of M. psychrotolerans (Mix-Mp) was studied. The strains (LMG 23374T = U2/3T, U2/5, JB-T11 and JB-T12) were previously isolated from fresh and cold-smoked tuna (Emborg and Dalgaard, 2006; Emborg et al., 2005). Prior to studies of growth and histamine formation, the isolates were grown in brain heart infusion broth (BHI CM225, Oxoid, Basingstoke, UK). The strains were moved from −80 °C and initially grown for 24 h at 25 °C and subsequently pre-cultured at 2–15 °C for 1–4 days depending on the temperature to be used in the experiment. Pre-cultures were harvested in the late exponential growth phase, corresponding to a relative change in absorbance of 0.1 to 0.4 units at 540 nm (Novaspec II, Pharmacia Biotech, Allerød, Denmark). The inoculum (Mix-Mp) was prepared by mixing the four pre-cultures to obtain similar concentrations of the isolates and then diluting as appropriate in 0.85% NaCl.

227

2.2. Growth and histamine formation Experiments with tuna meat and broth containing relevant high concentrations of the substrate histidine were used to quantify the effect of temperature, CO2, NaCl/aw and pH on growth and histamine formation by Mix-Mp. Table 1 shows the specific environmental conditions studied using tuna meat or broth, respectively. Although it is labour intensive to quantify histamine formation during storage of tuna meat such experiments were carried out initially to evaluate possible major differences in histamine production between tuna meat and broth. Growth and histamine formation by M. psychrotolerans were evaluated by inoculation of sterile tuna meat as no methods for selective enumeration of this bacterium are available yet. Canned tuna was used as a convenient source of sterile tuna meat with a high concentration of free histidine. 2.2.1. Challenge studies with canned tuna The effect of five different CO2 concentrations and of three different storage temperatures on growth and histamine formation was evaluated in experiments using canned tuna meat (Table 1, Code A, B and C). Canned tuna in water (~ 1% NaCl), produced in Thailand from yellowfin tuna (Thunnus albacares) meat, was obtained form a local retailer (Amanda Seafood Ltd., Frederikshavn, Denmark). Tuna meat from several cans (1350 g per can) was drained and mixed under sterile conditions. 1% (v/w) of the diluted pre-culture Mix-Mp was added to the tuna meat to obtain an initial concentration of 2–3 log (CFU/g). The inoculum was added as three portions of 0.33% and after each addition the tuna meat was mixed to distribute the inoculum. Inoculated tuna meat and a batch of non-inoculated (control) tuna meat were divided into 50 g portions and then packed in modified atmospheres with different mixtures of N2 and CO2 (AGA, Copenhagen, Denmark) by using a Multivac C 500 packaging machine (Multivac Ltd, Vejle, Denmark). A packaging film (NEN 40 HOB/ LLPDE 75, Amcore Flexibles, Horsens, Denmark) with low gas permeability (0.45 ± 0.15 cm3 m− 2 d− 1 atm− 1 for O2 and 1.8 ± 0.6 cm3 m− 2 d− 1 atm− 1 for CO2) and a gas/tuna ratio of approximately 3:1 was used. At regular intervals during storage samples were withdrawn in duplicate for analysis of growth and histamine formation by M. psychrotolerans as described in Section 2.2.3. The concentration of NaCl and dry matter in the tuna meat was measured at the beginning and at the end of the storage trial (See Section 2.2.3).

Table 1 Experiments used to determine the effect of four environmental parameters on growth and histamine formation by M. psychrotolerans Type of experiments Growth and histamine formation Canned tuna

Brothc in Hungate tubes

Growth rates in brothc Bioscreen C

a

Experiments

na

Temp. (°C)

% COb2

Water phase salt (%)

pH

A B C D E F G H I J K L

2 (10) 2 (6) 2 (2) 2 (10) 2 (4) 2 (4) 2 (4) 2 (6) 2 (6) 2 (6) 2 (6) 2 (6)

2 5, 10, 20 20 0, 5, 10, 15, 20 2 2 20 20 5 10 10 5

0, 17, 38, 66, 100 50 100 0 0 0 0 0 0 0 0 0

1.5 1.3 1.3 1.0 1.0 5.0 1.0 5.0 2.0, 3.0, 4.0 3.0 2.0, 3.0, 4.0 3.0

5.9 6.0 6.0 5.9 6.3, 6.5 6.2, 6.5 6.2, 6.5 5.9, 6.2, 6.5 5.9 5.6, 5.9, 6.3 5.9 5.5, 5.9, 6.3

M

2 (22)

10

0

1.0

N

2 (20)

10

0

O

4 (8)

10, 17.5

0

0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.3, 5.5, 5.8, 6.0 1.0

5.4, 5.5(2), 5.6, 5.8, 6.0, 6.3, 6.5, 5.9 (2), 6.0 5.9

Number of replicates for specific experiments. Total number of histamine and/or growth kinetics for each experiment is shown within brackets. b Equilibrium concentrations in headspace gas. c Amino acid-enriched LB Broth, Miller (244620 Difco™, Becton and Dickinson Company, Sparks, MD, USA).

5.9

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2.2.2. Experiments with broth Luria Bertani Broth, Miller (244620 Difco™, Becton and Dickinson Company, Sparks, MD, USA) was used with added amino acids and lactic acid to match the concentrations of these compounds in tuna meat (LBAA). LB-AA was prepared by mixing a previously sterilized (121 °C, 15 min) 10-fold stock solution of LB broth with a sterilized (121 °C, 15 min) solution containing water with dissolved amino acids, lactic acid, buffer and NaCl. Final concentrations of the added amino acids, buffer and lactic acid in LB-AA were: 50 mg/L L-arginine (A-3784 Sigma, Steinheim, Germany), 250 mg/L L-lysine mono-hydrochloride (1.05700 Merck Darmstadt, Germany), 20 000 mg/L L-histidine mono-hydrochloride (Sigma H-8125), 50 mg/L L-phenylalanine (78020 Fluka, SigmaAldrich, Steinheim, Germany), 120 mg/L L-tyrosine hydrochloride (Sigma T-2006), 7 g/L of H2KPO4 and 7 g/L of HK2PO4 and 20 700 mg/L of a 60% w/w solution of sodium lactate (Sigma L-1375). pH was adjusted for sub-batches of LB-AA by using HCl/NaOH. Duplicate experiments were carried out for different combinations of temperature, NaCl and pH (Table 1). Each sub-batch of LB-AA was inoculated with Mix-Mp to a final concentration between 1 and 5 log CFU/mL. Inoculated LB-AA were distributed into Hungate tubes (Bellco Hungate tubes, E. Pedersen og Søn Ltd., Oslo, Norway) which were filled with N2, sealed and stored in water baths at the different temperatures studied. At regular intervals during storage samples were withdrawn for analysis of growth and histamine formation (See Section 2.2.3). 2.2.3. Microbiological, physical and chemical analyses 15–25 g of canned tuna meat was diluted 10-fold in physiological saline (PS; 0.85% NaCl and 0.1% Bacto-peptone) and homogenised in a stomacher 400 (Seward Medical, London, UK).10-fold dilutions of this homogenate or of samples taken directly from inoculated broth were made in PS as appropriate. M. psychrotolerans was determined by spread plating (25 °C, 2 d) on tryptone soya agar (TSA, CM131, Oxoid, Basingstoke, UK). Concentrations of histamine in tuna and broth were quantified by HPLC. Post-column derivatisation with ο-phthalaldehyde (OPA, Sigma P1378, Steinheim, Germany) was followed by excitation at 354 nm and fluorescence detection at 430 nm as previously described (Jørgensen et al., 2000a). Analyses were performed on filter sterilized (0.20 μm) broth or after extraction of canned tuna meat samples (15 g) with 7.5% trichloroacetic acid (1.00807 Merck, Darmstadt, Germany). Storage temperature in all experiments was continuously recorded by data loggers (TinytagPlus, Gemini Data Loggers Ltd., Chichester, UK). For packed (previously canned) tuna meat the equilibrium concentration of CO2 in the headspace gas during storage was measured by a gas analyser (Combi Check 9800-1; PBI Dansensor, Ringsted, Denmark). pH in broth was measured directly while pH in tuna meat was measured after dilution of 10 g in 10 mL of demineralised water using a PHM 250 Analyser (MetroLab™, Radiometer, Copenhagen, Denmark). The percentage of dry matter was determined for 2 g of tuna meat after heating (102 °C, 24 h) (AOAC International, 2007a). The concentration of NaCl in tuna meat was determined by automated potentiometric titration (AOAC International, 2007b). The concentrations of lactic acid and free amino acids in tuna meat were determined by different HPLC methods as previously described (Barkholt and Jensen, 1989; Dalgaard and Jørgensen, 2000). 2.2.4. Growth rates determined in broth by absorbance measurements In a total of 50 growth experiments the effect of 19 different combinations of temperature (10.0 and 17.5 °C), NaCl (0.0–6.0%) and pH (5.4–6.0) (Table 1) on the maximum specific growth rate (µmax) of Mix-Mp was determined using LB-AA (See Section 2.2.2) and automated absorbance measurements. For each experiment Mix-Mp was 10-fold serially diluted to final concentrations of 105, 104, 103, 102, 101 CFU/mL in LB-AA with a specific NaCl concentration and pH. From each of these dilutions, 250 μL inoculated LB-AA was transferred into honeycomp 2 microplates (with 100 wells), added 50 μL sterile paraffin oil and incubated. Changes in absorbance at 540 nm were measured automatically at regular time intervals by using a Bioscreen C

instrument (Labsystems, Helsinki, Finland). Experiments were run in duplicate (two microplates) and μmax-values for each treatment were calculated from absorbance detection times of the serially diluted cultures as previously described (Dalgaard and Koutsoumanis, 2001). 2.3. Modelling and predicting the growth and histamine formation of M. psychrotolerans Predictive food microbiology methods for development of mathematical models to predict growth of microorganisms in foods under various environmental conditions are relatively well described (McKellar and Lu, 2004; McMeekin et al., 1993). In comparison, models for metabolite formation have been very little used within predictive food microbiology but extensively studied in relation to basic microbial kinetics and fermentation technology (Bailey and Ollis, 1986; Pirt, 1975). The absolute rate of histamine formation (dHis/dt, mg/kg/h) can be related to the absolute growth rate (dN/dt, CFU/g/h) by a constant yield factor for histamine formation (YHis/CFU, mg/CFU) (Eq. (1)). In food, bacteria can grow exponentially from very low initial concentrations until they approach the maximum population density (Nmax, CFU/g). In this situation the dampening of growth close to Nmax can have an important effect on metabolites formation as shown in Fig. 1. To describe this growth dampening the expanded Logistic model (Eq. (2)) includes the parameter m (Dalgaard, 2002; Turner et al., 1969). An integrated version of this model was used in the present study (Eq. (3)). dHis dN : 1000 ¼ YHIs= : dt CFU dt

ð1Þ

  dN N m ¼ N : μ max : 1− dt Nmax

Log Nt ¼ Log N0

Log Nt ¼ Log Nmax

ð2Þ

tbt lag

=

1þ




 !      1=m Nmax m −1 : exp −μ max :m: t−tlag N0

t≥tlag

ð3Þ 2.3.1. Primary modelling of growth and histamine formation Eq. (3) was used to estimate lag time (tlag, h), maximum specific growth rate (µmax, h− 1) and maximum population density

Fig. 1. Growth (bold lines, log (CFU/g)) and histamine formation (fine lines, ppm). Growth was simulated by the classical Logistic model (solid lines, Eq. (2), m = 1) and by the expanded Logistic model (Eq. (2), dashed lines m = 0.4, dotted lines m = 0.25). For each of the growth curves histamine formation was predicted using Eq. (4) with a constant yield factor (YHis/CFU) of 10− 7.2 mg histamine/CFU.

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(Nmax, CFU/g or CFU/mL) from individual viable count growth curves determined for tuna meat (n = 18) or LB-AA (n = 52) (Table 1). Eq. (3) was fitted to log-transformed concentrations of M. psychrotolerans and for all viable count growth curves the parameter m, that characterizes the dampening of growth when N t approaches Nmax, was fixed to 0.4, 0.7, 1.0 and 1.3. It was not attempted to determine the value of m by curve fitting as stable estimates of this parameter cannot always be obtained (Dalgaard and Koutsoumanis, 2001). A primary model for histamine formation during storage was obtained by combining the expanded Logistic growth model (Eq. (3)) with a yield factor (YHis/CFU, mg histamine/CFU) (Eq. (4)).

Hist ¼ His0 þ YHis=

CFU

: ðNt −N0 Þ : 1000

229

ð4Þ

In Eq. (4) Hist and His0 (ppm) are the concentrations of histamine (ppm) at time t and 0, respectively whereas Nt and No (CFU/g or CFU/ mL) are the corresponding concentrations of M. psychrotolerans. Eq. (4) was fitted to non-transformed data for histamine concentrations (Hist, ppm) to estimate values of the yield factor (YHis/CFU) at specific environmental conditions. When fitting a specific histamine formation curve, the values for Nt in Eq. (4) were obtained from Eq. (3) (Nt = 10logNt) with the values of No, tlag, µmax and Nmax obtained from the corresponding growth curve. Thus, the only parameter estimated from each histamine formation curve was YHis/CFU. Each histamine

Fig. 2. Growth (open symbols) and histamine formation (solid symbols) by Morganella psychrotolerans. Data from experiment A, D and I (Table 1). Fig. 2A) Experiment D with broth at 0 °C (squares) and at 10 °C (triangles). Fig. 2C) Experiment A with modified atmosphere packed tuna meat at 2 °C with equilibrium CO2 concentration in the headspace gas of 38% (squares) and 99.6% (circles). Fig. 2E) Experiment I with broth at 5 °C with 2% NaCl (squares), 3% NaCl (circles) and 4% NaCl (triangles). Maximum specific growth rates (µmax) of M. psychrotolerans as affected by (Fig. 2B) temperature, (Fig. 2D) equilibrium CO2 concentrations at 2 °C and (Fig. 2F) water activity at 5 °C.

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formation curve was fitted with four different but fixed degrees of growth dampening (m = 0.4, 0.7, 1.0 and 1.3). The corresponding residual sum of squares (RSS) was recorded to determine the most appropriate m-value.

described when Eq. (4) was expanded with a maintenance coefficient (M) (Eq. (6)). Eq. (6) was fitted to histamine formation curves by using Microsoft Excel 2003 (Microsoft Corp., Redmond, WA, USA) and manual adjustment of YHis/CFU and M to minimize RSS.

2.3.2. Secondary modelling of growth and histamine formation To model the effect of the environmental parameters temperature (T, °C), NaCl (water phase salt or aw), pH and CO2 (%) on the maximum specific growth rate (µmax) a combination of a square root and a cardinal parameter type model was used (Eq. (5), Ross and Dalgaard, 2004). Concentrations of water phase salt (WPS) were determined from concentrations of NaCl and water in fish meat. %WPS were transformed into the corresponding water activity (aw) by using the following equation: aw = 1 − 0.0052471 ⁎ %WPS − 0.00012206 ⁎ %WPS2 (Resnik and Chirife, 1988). Appropriate transformations of terms for the different environmental parameters in Eq. (5) were determined by plotting square root-transformed or non-transformed µmax-values against values of the relevant individual environmental parameters (Fig. 2).

3. Results and discussion



 T−Tmin 2 aw −aw min μ max ¼ μ max−ref : : : 1−10pHmin −pH Tref −Tmin aw opt −aw min

ð


 CO2 max −CO2 2 : :n CO2 opt −CO2 max

Þ

ð5Þ In Eq. (5), µmax-ref corresponds to µmax at the selected reference temperature (Tref = 20 °C) whereas Tmin, aw opt, aw min, pHmin, CO2 opt and CO2 max are model parameters (cardinal parameters) describing the effect of temperature, aw, pH and CO2 on µmax (Ross and Dalgaard, 2004). For M. psychrotolerans CO2 opt and aw opt were assumed to be 0 and 1, respectively. Terms for each of the environmental parameters in Eq. (5) have a value between 0 and 1. In this way Eq. (5) corresponds to the gamma-concept (Zwietering et al., 1992) but with the effect of interaction between environmental parameters (ξ) being taken into account as previously studied e.g. for growth of Listeria (Le Marc et al., 2002; Mejlholm and Dalgaard, 2007). A total of 120 square root-transformed µmax data (Table 1) were used to estimate values of the parameters in Eq. (5). Firstly, the value of CO2 max was estimated from data obtained in experiment A (Table 1). Subsequently, Eq. (5), with a fixed CO2 max-value, was fitted (n = 120) to estimate µmax-ref, Tmin, aw min and pHmin by non-linear regression. Data from experiment A, D, M and N (n = 62) could have been used to estimate values of µmax-ref, Tmin, aw min, pHmin and CO2 max by modelling each environmental parameter separately (Table 1, Fig. 2). The applied two step approach allowed data from experiment B, C and D–L (n = 48) to contribute to the secondary growth model although these experiments were primarily carried out to quantify and model histamine formation (Table 1). The effect of the environmental parameters on tlag, Nmax and YHis/ CFU was described by simple secondary models. tlag from experiment A–L (n = 70) was related to µmax by calculation of relative lag times (RLT = tlag/generation time = tlag ⁎ µmax/Ln(2)) (Ross and Dalgaard, 2004). Data from experiment A–L (n = 68) and a simple constrained linear (quadratic) model (Eq. (7)) was used to describe the effect of important environmental parameters on log(Nmax). Finally, a correlation between log(Nmax) and log(YHis/CFU) was established (n = 68).

3.1. Growth and histamine formation The studied environmental parameters (Table 1) markedly influenced both growth and histamine formation by M. psychrotolerans. Importantly M. psychrotolerans was able to grow to high concentrations and form toxic concentrations of histamine (above 500– 1000 ppm) at environmental conditions corresponding to those of chilled fresh and lightly preserved seafood (Fig. 2). These conditions included 0 °C (Fig. 2A), 100% CO2 at 2 °C (Fig. 2C) and 4% NaCl at 5 °C (Fig. 2E). Specific experiments were not designed as part of the present study to test if growth and histamine formation by M. psychrotolerans were similar in tuna meat and broth (Table 1). However, the average difference between observed and fitted square root-transformed µmax-values were 0.03 ± 0.04 for tuna meat (n = 18) and −0.005 ± 0.03 for broth (n = 102). For log(YHis/CFU) the residuals were −0.07 ± 0.14 for tuna meat (n = 18) and 0.05 ± 0.11 for broth (n = 50). These similar residuals suggest there was no important difference for growth and histamine formation in tuna meat and in the LB-AA broth used in the present study. A simple strait line relation was observed between storage temperature and square root-transformed µmax-values for M. psychrotolerans (Fig. 2B). This suggests that sub-optimal temperatures (0–20 °C) influence growth rates of M. psychrotolerans in the same way as observed for numerous other bacteria (Ratkowsky et al., 1982; Ross and Dalgaard, 2004). The estimated theoretical minimum growth temperature (Tmin) of −6.2 °C for M. psychrotolerans (Fig. 2B, Table 2) was about 7 °C lower than the reported Tmin-values for M. morganii (Emborg and Dalgaard, 2008-this issue; Ratkowsky et al., 1983), but similar to Tmin-values determined for psychrotolerant pseudomonads (Neumeyer et al., 1997; Ratkowsky et al., 1982) and slightly higher than the Tmin-value for spoilage bacteria in iced fresh fish including H2Sproducing Shewanella and P. phosphoreum (Dalgaard, 2002). Increasing concentrations of CO2 reduced the growth rate and delayed the histamine formation by M. psychrotolerans (Fig. 2C). The CO2 max-value of 266% CO2, corresponding to a partial pressure of 2.7 atm, (Table 2, Fig. 2D) indicates M. psychrotolerans was more CO2 resistant than psychrotolerant pseudomonads and H2S-producing

Table 2 Parameter values in secondary growth and histamine formation models for M. psychrotolerans Models and parameters

Standard errors

Other information n = 120 RSS = 0.127 r2 = 94.5% RMSE = 0.033

29

r2 = 90.0%

Maximum cell density model: Eq. (7) (Log Nmax) b0 −2321 b1 4659 b2 −0.00914 b3 −2330

653 1327 0.00140 674

n = 68 RSS = 6.07 r2 = 79.8% RMSE = 0.31

Yield factor model: Eq. (8) (Log YHis/CFU) α −0.148 β −0.881

0.185 0.023

n = 68 RSS = 1.05 r2 = 95.7% RMSE = 0.13

CO2

2.3.3. Curve fitting and statistical analysis SigmaStat for Windows Version 3.10 (Systat Software Inc. San Jose, CA, USA) was used for curve fitting and statistical analysis. Data were reported as mean ± standard deviation. Relation between the values of m with lowest RSS for histamine formation (Eqs. (3) and (4)) and the environmental parameters was evaluated by multiple linear regression and by using an F-test to identify significant factors. The same methods were used to identify the studied environmental parameters that significantly influenced log(Nmax) (Eq. (7)). An F-test was also used to test if histamine formation curves were significantly better

Estimated values

Maximum specific growth rate (µmax) model: Eq. (5) µmax-ref, h− 1 0.535 0.018 Tmin, °C −6.22 0.474 aw min 0.963 0.0005 pHmin 5.12 0.051 max

, %CO2

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231

with 3–5% NaCl. It is expected, even though not documented in the present study, that increasing concentrations of CO2 and NaCl results in filaments of normal sized cells of M. psychrotolerans. For these filaments it is likely that one colony observed in an agar plate originate from several attached cells of normal size and that CO2 and NaCl, therefore reduced the cell concentration determined as viable counts more than the corresponding histamine formation. 3.2. Modelling and predicting the growth and histamine formation for M. psychrotolerans

Fig. 3. Growth (circles) and histamine formation (triangles) by Morganella psychrotolerans in broth at 2 °C. Data show replicate 1 (Fig. 3A) and replicate 2 (Fig. 3B) from experiment E (Table 1) with pH 6.3. Growth curves were fitted with Eq. (3) and histamine formation curves were described using Eq. (3) together with Eq. (1) (solid lines) and Eq. (3) together with Eq. (6) (dashed lines). Fitted parameter values for growth and histamine formation are shown in Table 3.

Shewanella, approximately as CO2 sensitive as lactic acid bacteria and less CO2 resistant than P. phosphoreum (Dalgaard, 2002). The aw minvalue of 0.963 (Fig. 2F, Table 2) suggests M. psychrotolerans was slightly less NaCl-resistant than other psychrotolerant and Gram negative bacteria from fresh and lightly preserved seafood including pseudomonads, H2S-producing Shewanella and P. phosphoreum (Dalgaard, 2002). This, however, is not in agreement with data from challenge tests where M. psychrotolerans grew faster than P. phosphoreum in vacuum-packed cold-smoked tuna with 4.4 ± 0.8% WPS (Emborg and Dalgaard, 2006). One explanation for this could be that the smoke components in that product inhibited the growth of P. phosphoreum more than the growth of M. psychrotolerans. The effect of smoke components on growth of these bacteria, however, is not known and further research is relevant since both organisms have been involved in outbreaks of HFP due to smoked seafood (Emborg and Dalgaard, 2006). Increasing concentrations of CO2 and NaCl reduced both µmax and Nmax for M. psychrotolerans (Fig. 2C, E). Interestingly, a close to ten-fold (88%) reduction of Nmax from 108.3 CFU/g to 107.4 CFU/g, as observed with 2% NaCl compared to 4% NaCl did not result in a proportional decrease in histamine formation. In fact, the corresponding histamine concentrations decreased from 10 200 ppm to 7400 ppm (27%). That each cell of M. psychrotolerans seems to produce more histamine with 4% NaCl compared to 2% NaCl might be explained by an apparent elongation of cells when exposed to salt stress. Previously elongated cells have been observed for Salmonella and Listeria when exposed to high NaCl concentrations (Geng et al., 2003; Hazeleger et al., 2006; Mattick et al., 2000; Mukhopadhyay et al., 2006). In those studies it was documented that filaments formed by Salmonella and Listeria were composed of several normal sized cells. For M. psychrotolerans Emborg (2007) observed a marked elongation of cells when grown

3.2.1. Primary modelling of growth and histamine formation For the 70 histamine formation curves generated in the present study a value of 0.7 for the growth dampening parameter m in Eq. (3) resulted in lowest RSS-values on average. For different specific experiments m-values of 0.4, 0.7, 1.0 and 1.3 all resulted in the lowest RSS-value but no significant effect of the studied environmental parameters on m-values with lowest RSS was identified by multiple linear regression (P N 0.05). The m-value of 0.7 was therefore selected as the most appropriate to use. Previously, Bermejo et al. (2004) evaluated growth and histamine formation for a mixed microflora from jack mackerel. In that study both the appropriate growth dampening parameter and the yield factor for histamine formation differed markedly between experiments with broth and fish muscle. The m-value of 0.7 from the present study, however, cannot be directly compared to data from Bermejo et al. (2004) as they studied a mixed microflora. Jørgensen et al. (2000b) used the classical Logistic model with m of 1.0, and a constant yield factor to describe growth and histamine formation by P. phosphoreum in cold-smoked salmon. Other more flexible models, however, were not evaluated in that study. As shown in Fig. 3 histamine formation curves could be appropriately described by combining a growth model (Eq. (3)) and a simple yield factor (Eq. (1)). However, not all histamine formation curves seemed appropriately fitted by this simple approach as shown in Fig. 3B. Expanding the differential form of the simple histamine formation model (Eq. (1)) by adding a maintenance coefficient (M, mg histamine/CFU/h) as shown in Eq. (6) improved the ability of the model to describe the histamine formation in some experiments (Fig. 3B and Table 3). An F-test showed that 42 of the 70 histamine formation curves were better fitted (P b 0.05) by Eq. (6) compared to Eq. (1). It is important to note, however, that introducing M into Eq. (6) had little effect on estimated values of the yield factor (YHis/CFU) (Table 3) as well as on the predicted time to formation of histamine concentrations below 2000 ppm (Fig. 3). Consequently, to predict

Table 3 Evaluation of primary histamine formation model for M. psychrotolerans Parameter values and statisticsa Log (N0, CFU/mL) Log (Nmax, CFU/mL) Lag time, hours µmax, hours− 1 Eq. (1) Log (YHis/CFU, mg/CFU) Residual sum of squares (RSS) Eq. (6) Log (YHis/CFU, mg/CFU) Maintenance coefficient, Log(mg/CFU/h)) Residual sum of squares (RSS) Difference between Eqs. (1) and (6) Yield factor (mg/CFU) Residual sum of squares (RSS) a

Ab

Bb

1.4 8.3 32 0.0390

1.4 8.3 10 0.0385

−7.530 1.01 ⁎ 106

−7.481 3.95 ⁎ 106

−7.525 −12.30 9.80 ⁎ 105

−7.550 −10.55 1.88 ⁎ 106

1% 3%

15% 52%

Corresponding growth and histamine formation curves are shown in Fig. 3. Replicates from a growth and histamine formation experiment with M. psychrotolerans in amino acid-enriched LB Broth, Miller at 2 °C (pH 6.3, 1% water phase salt and 0% CO2). b

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histamine formation of importance in seafood a simple model with a yield factor but without a maintenance coefficient (i.e. Eq. (1)) seemed sufficient from a practical point of view and this approach was used within the present study (Eqs. (1) and (4)). dHis ¼ dt


 dN YHis=CFU : þ M : N : 1000 dt

ð6Þ

3.2.2. Secondary modelling of growth and histamine formation The secondary µmax-model for M. psychrotolerans (Eq. (5)) explained 94.5% of the variation in the experimental data (Table 2) corresponding to a residual mean square error (RMSE) of 0.033. The inclusion of an interaction term (ξ) in Eq. (5) did not affect the parameter values or residual sum of squares (RSS). However, this may change when more data becomes available in the growth boundary region. Lag times for M. psychrotolerans depended on the environmental parameters and RLT showed some variability being 2.55±2.82 (n=70). These values are similar to data previously reported for E. coli and Listeria monocytogenes (Augustin and Carlier, 2000; Ross, 1999). RLT depends on the initial physiological condition of cells (Ross and Dalgaard, 2004) and the observed variability for M. psychrotolerans probably reflects the preculturing at different temperatures between 2 °C and 15 °C. The maximum population density for M. psychrotolerans decreased with (i) increasing concentrations of CO2 and (ii) increasing concentration of NaCl, corresponding to decreasing aw-values (Fig. 2C and E). In fact, it was shown by multiple linear regression that aw and CO2 had a statistically significant effect on log(Nmax) (P b 0.05). Eq. (7) described 79.8% of the variation in the log(Nmax) data (Table 2) corresponding to a RMSE of 0.308. To avoid modelling of errors, Eq. (7) was constrained ðNmax Þ by A log b0 corresponding to b2 b 0 and by A logAaðNwmax Þ N0 correspondACO2 ing to b1 + 2b3 N 0. The fitted parameter values with these constrains are reported in Table 2. It was previously observed that CO2 reduced Nmax for pseudomonas (Willocx et al., 1993) and that the final optical density for cultures of Escherichia coli decreased with decreasing aw (Krist et al., 1998). However, in many predictive modelling studies a similar effect of NaCl and CO2 has not been significant and this effect is for example not included in either the ComBase Predictor (www. combase.cc) or the Pathogen Modeling Program (http://www.arserrc. gov/mfs/PMP7_Start.htm). LogðNmax Þ ¼ b0 þ b1 : aw þ b2 : kCO2 þ b3 : ðaw Þ2

ð7Þ

YHis/CFU was influenced by the environmental parameters but a simple secondary model was obtained by relating log(YHis/CFU) and log (Nmax) (Eq. (8), Table 2). The RMSE was 0.13.   Log YHis=CFU ¼ α þ β : LogðNmax Þ

ð8Þ

Clearly, the maximum concentration of histamine formed depends on the available concentration of the substrate i.e. the free amino acid histidine. The canned tuna meat used in the present study contained from 7000 to 8600 ppm (n =6) of free histidine resulting in a maximum theoretical histamine formation within the range of 5000 to 6150 ppm. This corresponded well with the concentrations of histamine actually produced by M. psychrotolerans (Fig. 2C). However, when the developed model is used to predict histamine formation in fish flesh with low concentrations of free histidine the theoretical maximum histamine formation must be taken into account by introducing an absolute rate of histamine formation (dHis/dt) equal to zero from the time when the substrate is consumed and no longer is available for histamine formation. To evaluate histamine formation, simple empirical models have been suggested previously for the effect of low storage temperatures (−1.1 to 15.6 °C) and higher storage temperatures (21.1 to 37.8 °C) on skipjack tuna (Frank and Yoshinaga, 1987; Frank et al., 1983). These models however, have not been successfully validated for other fish species

(Dalgaard et al., 2008; Frank and Yoshinaga, 1987; Frank et al., 1983). Models relating growth and histamine formation during storage have been suggested for P. phosphoreum in cold-smoked salmon at 5 °C (Jørgensen et al., 2000b), for M. morganii (Torres et al., 2002) and for a mixed microflora (Bermejo et al., 2004) in mackerel. However, secondary mathematical models to predict the effect of different combinations of storage conditions and product characteristics on histamine formation in seafood have not previously been developed. Thus, the model developed in the present study for M. psychrotolerans represent considerable progress with respect to prediction of histamine formation depending on both storage conditions and product characteristics. Evaluation and product validation of the developed M. psychrotolerans growth and histamine formation model is important and this aspect has been included as part of separate study (Emborg and Dalgaard, 2008this issue). In brief, they showed that the new model developed in the present study successfully predicted growth of M. psychrotolerans in seafood at constant and variable storage temperature. The only unacceptable prediction was obtained for cold-smoked tuna and further evaluation of the growth model is needed for naturally contaminated products and seafood with high concentration of NaCl (Emborg and Dalgaard, 2008-this issue). The new M. psychrotolerans-histamine-formation model on average provided slightly conservative (fail-safe) prediction for time to toxic concentrations of histamine in seafood (500 and 2000 ppm). Possible improvements of the model are discussed by Emborg and Dalgaard (2008-this issue). The M. psychrotolerans-histamine-formation model developed in the present study can support decisions concerning histamine formation in chilled finfish products. Predictions are not highly accurate but information on potential histamine formation depending on storage conditions, product characteristics and initial concentration of M. psychrotolerans can be obtained rapidly and seems to correspond well with much more costly and time consuming challenge studies (Emborg and Dalgaard, 2008-this issue). The developed model has been included the Seafood Spoilage and Safety Predictor software version 3 from 2008 and in this way the model becomes accessible and easy to use (www. difres.dk/micro/sssp/). To evaluate histamine formation for various seafoods and storage conditions it is relevant to develop similar model for other histamine-producing bacteria, particularly, M. morganii and P. phosphoreum. Such new histamine formation models may be developed by using the same kinetic approach as applied in the present study where a classical growth model with variable growth dampening (Eq. (3)) has been related to histamine formation by a constant yield factor (Eq. (4)). Within predictive food microbiology models for growth and metabolite formation have been little studied. Nevertheless, the importance of such models to predict formation of microbial toxins or production of metabolites responsible for food spoilage seems considerable. Predictive microbiology growth models typically focus on lag time and growth rate to predict the time required to reach critical cell concentrations of importance to food safety or quality. To model growth and metabolite formation it becomes important to have more precise information on how the maximum cell density (Nmax) and yield factors depend on storage conditions and product characteristics. Acknowledgements The authors thank Tina Dahl Dewitt, Lina Pedersen and Nadereh Samieian for excellent technical assistance. Most of the research was carried out within the EU Integrated Project SEAFOODplus, contract no. FOOD-CT-2004-506359. Partial financing of the work by the European Union is gratefully acknowledged. References AOAC International, 2007a. AOAC official methods of analysis, Method 950.46, Moisture in meat. 18th ed. AOAC International, Gaithersburg, Maryland, USA. AOAC International 2007b. AOAC official methods of analysis. 18th ed. Method 976.18, Salt (chlorine and sodium chloride) in seafood. Potentiometric method, in

J. Emborg, P. Dalgaard / International Journal of Food Microbiology 128 (2008) 226–233 combination with 937.07, Fish and marine products. Treatment and preparation of sample procedure, and 971.27, Sodium chloride in canned vegetables. Potentiometric method. AOAC International, Gaithersburg, Maryland, USA. Augustin, J.-C., Carlier, V., 2000. Mathematical modelling of the growth rate and lag time for Listeria monocytogenes. International Journal of Food Microbiology 56, 29–51. Bailey, J.E., Ollis, D.F., 1986. Biochemical Engineering Fundamentals, 2 edn. McGraw-Hill Book Company, New York, USA. Barkholt, V., Jensen, A.L., 1989. Amino-acid analysis — determination of cysteine plus half-cystine in proteins after hydrochloric-acid hydrolysis with a disulfide compound as additive. Analytical Biochemistry 177, 318–322. Basby, M., Jeppesen, V.F., Huss, H.H., 1998. Characterization of the microflora of lightly salted lumpfish (Cyclopterus lumpus) roe stored at 5 °C. Journal of Aquatic Food Product Technology 7, 35–51. Bermejo, A., Mondaca, M.A., Roeckel, M., Marti, M.C., 2004. Bacterial formation of histamine in jack mackerel (Trachurus symmetricus). Journal of Food Processing and Preservation 28, 201–222. Dalgaard, P., 2002. Modelling and predicting the shelf-life of seafood. In: Bremner, H.A. (Ed.), Safety and Quality Issues in Fish Processing. Woodhead Publishing Ltd., Cambridge, England, pp. 191–219. Dalgaard, P., Jørgensen, L.V., 2000. Cooked and brined shrimps packed in a modified atmosphere have a shelf life of N 7 months at 0 °C, but spoil in 4–6 days at 25 °C. International Journal of Food Science and Technology 35, 431–442. Dalgaard, P., Koutsoumanis, K., 2001. Comparison of maximum specific growth rates and lag times estimated from absorbance and viable count data by different mathematical models. Journal of Microbiological Methods 43, 183–196. Dalgaard, P., Madsen, H.L., Samieian, N., Emborg, J., 2006. Biogenic amines formation and microbial spoilage in chilled garfish (Belone belone belone) — effect of modified atmosphere packaging and previous frozen storage. Journal of Applied Microbiology 101, 80–95. Dalgaard, P., Emborg, J., Kjølby, A., Sørensen, N.D., Ballin, N.Z., 2008. Histamine and biogenic amines — formation and importance in seafood. In: Børresen, T. (Ed.), Improving seafood products for the consumer. Woodhead Publishing Ltd., Cambridge, England, pp. 292–324. DeWaal, C.S., Hicks, G., Barlow, K., Alderton, L., Vegosen, L., 2006. Foods associated with food-borne illness outbreaks from 1990 through 2003. Journal of Food Protection 26, 466–473. EC, 2005. Commission regulation (EC) No 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. Official Journal of the European Union 338,1–25. Emborg, J. , 2007. Morganella psychrotolerans — identification, histamine formation and importance for histamine poisoning. Ph.D. thesis. Technical University of Denmark, Danish Institute for Fisheries Research. Lyngby, Denmark, 97 pp. Emborg, J., Dalgaard, P., 2006. Formation of histamine and biogenic amines in coldsmoked tuna: an investigation of psychrotolerant bacteria from samples implicated in cases of histamine fish poisoning. Journal of Food Protection 69, 897–906. Emborg, J., Dalgaard, P., 2008. Growth, inactivation and histamine formation of Morganella psychrotolerans and Morganella morganii — development and evaluation of predictive models. International Journal of Food Microbiology 128, 234–243 (this issue). Emborg, J., Laursen, B.G., Dalgaard, P., 2005. Significant histamine formation in tuna (Thunnus albacares) at 2 °C — effect of vacuum- and modified atmospherepackaging on psychrotolerant bacteria. International Journal of Food Microbiology 101, 263–279. Emborg, J., Dalgaard, P., Ahrens, P., 2006. Morganella psychrotolerans sp. nov., a histamine-producing bacterium isolated from various seafoods. International Journal of Systematic and Evolutionary Microbiology 56, 2473–2479. FDA/CFSAN 2001. Scombrotoxin (histamine) formation, Fish and Fishery Products Hazards and Controls Guidance. 3 edn, Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Seafood, Washington, DC, USA, pp. 83–102. Fletcher, G.C., Summers, G., Winchester, R.V., Wong, R.J., 1995. Histamine and histidine in New Zealand marine fish and shellfish species, particularly kahawai (Arripis trutta). Journal of Aquatic Food Product Technology 4, 53–74. Frank, H.A., Yoshinaga, D.H., 1987. Table for estimating histamine formation in skipjack tuna, Katsuwonus pelamis, at low nonfreezing temperature. Marine Fisheries Review 49, 67–70. Frank, H.A., Yoshinaga, D.H., Wu, I.P., 1983. Nomograph for estimating histamine formation in skipjack tuna at elevated temperatures. Marine Fisheries Review 45, 40–44. Geng, T., Kim, K.P., Gomez, R., Sherman, D.M., Bashir, R., Ladisch, M.R., Bhunia, A.K., 2003. Expression of cellular antigens of Listeria monocytogenes that react with monoclonal antibodies C11E9 and EM-7G1 under acid-, salt- or temperature-induced stress environments. Journal of Applied Microbiology 95, 762–772. Havelka, B., 1967. The role of bacteria from Hafnia genus in the origination of histamine in tunny meat. Ceskoslovenska hygiena 12, 343–352. Hazeleger, W.C., Dalvoorde, M., Beumer, R.R., 2006. Fluorescence microscopy of NaClstressed, elongated Salmonella and Listeria cells reveals the presence of septa in filaments. International Journal of Food Microbiology 112, 288–290. Jørgensen, L.V., Dalgaard, P., Huss, H.H., 2000a. Multiple compound quality index for cold-smoked salmon (Salmo salar) developed by multivariate regression of biogenic amines and pH. Journal of Agricultural and Food Chemistry 48, 2448–2453.

233

Jørgensen, L.V., Huss, H.H., Dalgaard, P., 2000b. The effect of biogenic amine production by single bacterial cultures and metabiosis in cold-smoked salmon. Journal of Applied Microbiology 89, 920–934. Kanki, M., Yoda, T., Ishibashi, M., Tsukamoto, T., 2004. Photobacterium phosphoreum caused a histamine fish poisoning incident. International Journal of Food Microbiology 92, 79–87. Kawabata, T., Ishizaka, K., Miura, T., Sasaki, T., 1956. Studies on the food poisoning associated with putrefaction of marine products. VII. An outbreak of allergy-like food poisoning caused by ‘sashimi’ of Parathunnus mebachi and the isolation of causative bacteria. Bulletin of the Japanese Society of Scientific Fisheries 22, 41–47. Krist, K.A., Ross, T., McMeekin, T.A., 1998. Final optical density and growth rate; effects of temperature and NaCl differ from acidity. International Journal of Food Microbiology 43, 195–203. Le Marc, Y., Huchet, V., Bourgeois, C.M., Guyonnet, J.P., Mafart, P., Thuault, D., 2002. Modelling the growth kinetics of Listeria as a function of temperature, pH and organic acid concentration. International Journal of Food Microbiology 73, 219–237. Lehane, L., Olley, J., 2000. Histamine fish poisoning revisited. International Journal of Food Microbiology 58, 1–37. Lerke, P.A., Werner, S.B., Taylor, S.L., Guthertz, L.S., 1978. Scombroid poisoning — report of an outbreak. Western Journal of Medicine 129, 381–386. Mattick, K.L., Jørgensen, F., Legan, J.D., Cole, M.B., Porter, J., Lappin-Scott, H.M., Humphrey, T.J., 2000. Survival and filamentation of Salmonella enterica serovar Enteritidis PT4 and Salmonella enterica serovar Typhimurium DT104 at low water activity. Applied and Environmental Microbiology 66, 1274–1279. McKellar, R.C., Lu, X., 2004. Modeling Microbial Responses in Food. CRC Press, Boca Raton, USA. 343 pp. McLauchlin, J., Little, C.L., Grant, K.A., Mithani, V., 2006. Scombrotoxic fish poisoning. Journal of Public Health 28, 61–62. McMeekin, T.A., Olley, J., Ross, T., Ratkowsky, D.A., 1993. Predictive Microbiology: Theory and Application. Research Studies Press Ltd., Taunton, UK. 340 pp. Mejlholm, O., Dalgaard, P., 2007. Modeling and predicting the growth boundary of Listeria monocytogenes in lightly preserved seafood. Journal of Food Protection 70, 70–84. Mukhopadhyay, A., He, Z.L., Alm, E.J., Arkin, A.P., Baidoo, E.E., Borglin, S.C., Chen, W.Q., Hazen, T.C., He, Q., Holman, H.Y., Huang, K., Huang, R., Joyner, D.C., Katz, N., Keller, M., Oeller, P., Redding, A., Sun, J., Wall, J., Wei, J., Yang, Z.M., Yen, H.C., Zhou, J.Z., Keasling, J.D., 2006. Salt stress in Desulfovibrio vulgaris Hildenborough: an integrated genomics approach. Journal of Bacteriology 188, 4068–4078. Neumeyer, K., Ross, T., McMeekin, T.A., 1997. Development of a predictive model to describe the effect of temperature and water activity on the growth of spoilage pseudomonads. International Journal of Food Microbiology 38, 45–54. Pirt, S.J., 1975. Principles of Microbial and Cell Cultivation. Blackwell Scientific Publication, Oxford, England. 274 pp. Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A., 1982. Relationship between temperature and growth-rate of bacterial cultures. Journal of Bacteriology 149, 1–5. Ratkowsky, D.A., Lowry, R.K., McMeekin, T.A., Stokes, A.N., Chandler, R.E., 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. Journal of Bacteriology 154, 1222–1226. Resnik, S.L., Chirife, J., 1988. Proposed theoretical water activity values at various temperatures for selected solutions to be used as reference sources in the range of microbial-growth. Journal of Food Protection 51, 419–423. Ross, T., 1999. Predictive Microbiology for the Meat Industry. Meat and Livestock Australia. 196 pp. Ross, T., Dalgaard, P., 2004. Secondary models. In: McKellar, R.C., Lu, X. (Eds.), Modeling Microbial Responses in Food. CRC Press, Boca Raton, pp. 63–150. Sakabe, Y., 1973. Studies on allergylike food poisoning 2. Studies on the relation between storage temperature and histamine production. Journal of the Nara Medical Association 24, 257–264. Taylor, S.L., 1986. Histamine food poisoning: toxicology and clinical aspects. CRC Critical Reviews in Toxicology 17, 91–128. Taylor, S.L., Guthertz, L.S., Leatherwood, M., Lieber, E.R., 1979. Histamine production by Klebsiella pneumoniae and an incident of scombroid fish poisoning. Applied and Environmental Microbiology 37, 274–278. Torres, S., Roeckel, M., Marti, M.C., 2002. Histamine formation by Morganella morganii isolated from Trachurus murphyii (Chilean mackerel). Latin American Applied Research 32, 209–214. Turner, M.E., Blumenst, B.A., Sebaugh, J.L., 1969. A generalization of the Logistic law of growth. Biometrics 25, 577–580. Willocx, F., Mercier, M., Hendrickx, M., Tobback, P., 1993. Modeling the influence of temperature and carbon-dioxide upon the growth of Pseudomonas fluorescens. Food Microbiology 10, 159–173. Zwietering, M.H., Wijtzes, T., De Wit, J.C., Van't Riet, K., 1992. A decision support system for prediction of the microbial spoilage in foods. Journal of Food Protection 55, 973–979.