Kinetic Modeling of Escherichia coli O157:H7 Growth in Rainbow Trout Fillets as Affected by Oregano and Thyme Essential Oils and Different Packing Treatments

Abstract

The effect of different essential oils (oregano and thyme) and packing (aerobic packing, modified atmosphere packing, and vacuum packing) treatments on the growth of Escherichia coli O157:H7 was studied using nonlinear regression of modified-Gompertz and logistic equations to generate the best fit. Parameters of nonlinear modified-Gompertz and logistic models of E. coli O157:H7 in trout samples treated with the different essential oils and packing treatments were matched satisfactorily. Both the modified-Gompertz and logistic models showed a good fit to all the growth curves as evaluated by using mean percentage error, mean bias error, root mean square error, modeling efficiency, and χ² as well as the correlation coefficients (R) between the experimental and predicted values. Generally, higher values of R² and modeling efficiency values were obtained with the modified Gompertz model. However, both models resulted in similar absolute values of mean bias error, root mean square error, mean percentage error, and χ². The growth of E. coli O157:H7 was remarkably delayed by the essential oils and packing treatments. These treatments can be used in the protection of fish and fishery products from microbial risks and the fish can also be aromatized with the essential oils. Based on the obtained results in this study, it can be concluded that the modified Gompertz and logistic models can be used effectively to predict the effect of plant essential oils on growth potential of E. coli O157:H7 in fish products to be stored under aerobic, modified atmosphere packing, and vacuum packing conditions.

Keywords:

• Predictive microbiology
• Escherichia coli O157:H7
• Essential oils
• Packing treatments

INTRODUCTION

The application of mathematical models allows quantifying and predicting the rate of growth of microorganisms under environmental conditions, enabling us to ensure the hygienic quality of food materials and determine their shelf life.[1] Therefore, mathematical models have become very popular tools, which are widely used in describing the inactivation, survival, or behavior of microorganisms. At present, the application of the modified Gompertz model for the description of microbial survival/inactivation and growth is well documented and described in the literature.[2 ⁻ 4]

Escherichia coli O157:H7, causing foodborne diseases through consumption of contaminated foods, is one of the most popular of the hazardous foodborne pathogens. Generally, E. coli O157:H7 usually leads to illnesses, such as hemorrhagic colitis in the intestine and hemolytic uremic syndrome characterized by kidney failure and temporary anemia. The relatively recent emergence of E. coli O157:H7 as a foodborne pathogen has a significant impact on the food industry.[5] Acid survival plays an important role in bacterial enteric infections. In order to control and minimize foodborne diseases caused by this organism, it has been necessary to change primary production, processing, retailing, and consumer handling practices. Despite these new control measures, foodborne disease outbreaks caused by this bacterium continue to occur and are accompanied by an increase in the number of cases of illness in many countries.[5,6] Given the fact that EHEC (enterohemorrhagic E. coli O157:H7) outbreak has continued to rise in Germany, the necessity to find some effective ways to prevent this species from occurring in food products should be taken into consideration. Accordingly, there have been recent efforts exerted to prevent EHEC, indicating that essential oils and hydrosols of spices and plants significantly decreased the pathogenic bacteria counts of foods, especially E. coli O157:H7.[7,8] In addition, Moreira et al.[9] reported that oils of eucalyptus, tea, rosemary, mint, clove, lemon, oregano type thyme, pine, and basil had antimicrobial effects on E. coli O157:H7.

Oregano (Origanum vulgare L.) and thyme (Thymus vulgaris L.), are two members of the Lamiaceae family and widely grown in Turkey. Leaves, essential oil, and hydrosols of oregano and thyme have been widely used as an antioxidant and antimicrobial in meat and fish products for years.[7,10,11] In this respect, spice essential oils have been regarded as natural preservative agents against foodborne pathogens.[12] The inhibition of Escherichia species under aerobic conditions in the presence of various essential oils, such as oregano, pimento, horseradish, and mint, has been widely reported in traditional Greek salads,[13] precooked roast beef,[14] beef,[15] tarhana,[16] and ayran.[17]

On the other hand, many various preservative methods are used for increasing of shelf life of fish and fishery products. One of them, modified atmosphere packing (MAP), has been effectively used in the food sector for inhibiting the growth of spoilage organisms on perishable foods, such as meat, fish, and their products.[18] Additionally, the storage of fish in MAP has generally been combined with refrigeration and other methods for a longer shelf life.[19,20] Combined effects of MAP, essential oil, and cold storage on the microbiological properties of fish and fishery products were investigated by several investigators.[21 ⁻ 25] Many researchers have implied that MAP can increase survival of E. coli O157:H7 in food products.[26,27] In MAP, E. coli O157:H7 generally survives and E. coli O157:H7 growth depends on the kind of products, package atmosphere, storage temperature, and E. coli O157:H7 strain.[28]

Very limited studies have been reported on the effect of MAP on the growth/survival of foodborne pathogens,[29] and even fewer have tested the effect of MAP in combination with essential oils against pathogenic bacteria. Furthermore, no study has appeared on the evaluation of inhibitory or growth delaying effect of essential oils in several food products on E. coli O1575:H7 in combination with MAP/vacuum packing (VP) conditions using kinetic modeling. Therefore, in this study it was aimed to determine the performance of the modified Gompertz and logistic models to predict the effect of oregano and thyme essential oils on growth of E. coli O157:H7 in rainbow trout fillets stored under aerobic, MAP/VP conditions.

MATERIALS AND METHODS

Sample Preparation

Fresh rainbow trout (Oncorhynchus mykiss) fillets were provided from a fish market in Kayseri, Turkey. They were immediately transferred in ice boxes to the laboratory. After the skin was removed under aseptic conditions, the trout fillets were cut in pieces (each weighing approximately 30 g) and immediately sealed in polyethylene bags. Experiments were carried out on three different batches of fish fillets.

Distillation of the Essential Oils

In this study, oregano (Origanum vulgare L.) and thyme (Thymus vulgaris L.) leaves were used to obtain essential oil. Dried oregano and thyme spices were identified by the scientists of botany in Erciyes University in Kayseri, Turkey and purchased from a local retail spice market. The essential oils of the species were extracted by the Clevenger hydrodistillation apparatus (Ildam, Turkey). Plant materials (100 g) were cut into small pieces, placed in a distillation apparatus with 2 L of double distilled water, and hydro distilled for 3 h. After the oils were dried over anhydrous sodium sulphate, they were stored at 4°C until analyses.

Gas Chromatography-Mass Spectrometry (GC-MS) Analysis of the Essential Oils

GC-MS analyses of volatile components of the essential oils were run with some modifications according to the procedure of Ayvaz et al.[30] on an Agilent 7890A GC gas chromatograph system (Agilent Technologies, Avondale, PA, USA) coupled to a mass selective detector (Agilent Technologies), HP-5MS column (50 m × 0.2 mm, film thickness 0.25 μm), pressure 90 kPa, split 1:25, and injection volume 1 μL. The oven temperature was held at 70°C for 2 min, from 70 to 230°C at 5°C/min, finally increased to 230°C/min and held for 5 min. The carrier gas was helium with a flow rate of 1.6 mL/min. Qualitative analysis was based on the comparison of retention times and the computer mass spectra libraries using Wiley GC/MS Library and Nist (Tutore Libraries). The percentage composition was computed from the GC peak areas.[31]

Culture Preparation and Inoculation

In the study, E. coli O157:H7 33150 was used as the test bacterium. First, stock culture of E. coli O157:H7 was inoculated to nutrient broth for obtaining fresh culture and was then grown at 37°C for 24 h. Second, the fresh culture of E. coli O157:H7 was again inoculated and activated (10⁷ CFU/mL) in nutrient broth, after a second incubation at 37°C for 24 h, and finally inoculated on the fillet samples up to a final population of 10⁵ CFU/g.

Essential Oil Application and Packing Treatments

After the fresh fillet samples were tested for the presence of E. coli O157:H7 and the pathogen was not detected, the essential oil applications were conducted. The number of E. coli O157:H7 was evaluated in three samples, i.e., control (fillets without essential oil application), oregano (fillets added with oregano essential oil), and thyme (fillets added with thyme essential oil). Appropriate volumes of oregano and thyme essential oils were applied by surface spraying, yielding 0.2% (v/w) essential oil per sample. The number of E. coli O157:H7 was also evaluated in the samples packed under three gas atmospheres, i.e., air, modified atmosphere (30% CO₂/70%N₂/4 ppm O₂ gas mixture at 1.2 ppm moisture), and vacuum. For the aerobic packing (AP) storage, the samples were placed in sterile Petri dishes. For MAP, the samples were packed with polyethylene films with low O₂ permeability (O₂ transmission rate of 5 ml/m2/24 h at 23°C and 75% R.H.), and for vacuum packed samples, the samples were packed with Cryovac BB405 bags (Cryovac A/S, Oslo, Norway), using a vacuum machine (Unal Machine Equipments, model KVG 010, Adapazari, Turkey). After packing, the samples were stored at 4 ± 1°C prior to enumeration of the pathogen cells. All experiments were applied in triplicate.

Enumeration of E. coli O157:H7 Cells

Twenty-five grams of fillet samples were homogenized with 225 ml of a sterile solution of 0.85% (w/v) sodium chloride at 45°C. Decimal dilutions were prepared in 9 ml sterile NaCl (0.85%) until 10⁹ dilutions. Sorbitol-MacConkey Agar (SMAC, Merck, Germany) was used for the enumeration of E. coli O157:H7. Dilutions were plated on SMAC (Merck) using the pour plate technique. Plated samples were incubated at 37°C for 24 h. Typical E. coli O157:H7 colonies on SMAC were counted as colony forming units (CFU) after the incubation period.

Modeling of Microbial Growth

The modified Gompertz model (Eq. 1) and logistic model (Eq. 2) were used to fit the microbial growth curves of E. coli O157:H7:[2]

(1)

where log N (log CFU/g) is the logarithm of the cell number E. coli O157:H7 at time t; log N ₀ (log CFU₀) is the logarithm of the cell number of E. coli O157:H7 counts at time 0; a is the count increment as time increases indefinitely, that is number of log cycles of growth (log CFU/g); B is the specific growth rate at time m (1/h), and m is the time at which the absolute growth rate is at a maximum (h).

(2)

where log N, log N ₀, and a have the same meaning as above; d is a dimensionless parameter and c is the specific growth rate (1/h). Because modified Gompertz and logistic models are sigmoidal models, both of them are more appropriate to fitting growth curves complete with all three phases, involving lag, exponential, and stationary phases. When incomplete growth was the case, especially once the stationary phase data are missing, these models may not be suitable for fitting the growth curves.[32] For the modified Gompertz model, the exponential specific growth rate (log CFU/g/h) can be calculated from:

(3)

and for the logistic model, the maximum specific growth rate (log CFU/g/h) can be derived from:

(4)

Nonlinear Regression of the Derived Models and Their Comparisons

A nonlinear regression procedure in Statistica software (Release 5.0, Statsoft Inc., Tulsa, OK, USA) was used to fit each individual set of growth data to the four primary models, minimizing the sum of squares of the difference between experimental data and the fitted model, i.e., loss function (observed, predicted). The Quasi-Newton algorithm option of the nonlinear regression procedure was used during numerical iteration to search for the calculated parameters of each model. After several iterations in the nonlinear procedure, the starting values converged to estimated values of the parameters.

The comparison of the derived models (Eqs. 1 and 2) was carried out using various statistical parameters, such as the mean percentage error (MPE), the mean bias error (MBE), the root mean square error (RMSE), the modeling efficiency (EF), and chi-square (χ²), in addition to R ². These statistics allow for the detection of the differences between experimental data and the model estimates. These parameters can be estimated as follows:[33]

(5)

(6)

(7)

(8)

(9)

where log N _exp,i (log CFU/g) is the experimental logarithm of the cell number E. coli O157:H7 at time t; log N _pre,i is the predicted logarithm of the cell number of E. coli O157:H7 at time t (log CFU/g); log N _exp,ave is the average of logarithm of the cell number E. coli O157:H7 at time t (log CFU/g), n is the number of data points, and n _u is the number of model parameters.

Statistical Analysis

Conventional statistical methods were used to calculate means and standard deviations. Collected data were subjected to statistical analyses using MINITAB for Windows Release 13^R (Minitab Inc., State College, PA, USA).[34]

RESULTS AND DISCUSSION

The Chemical Composition of Essential Oils

The chemical compositions of the oregano and thyme essential oils were determined by GC-MS. GC-MS results containing % essential oil components were shown in Table 1 and the components lower than 1% were not included in the table. As shown in Table 1, oregano and thyme essential oils had 12 and 6 different major components, respectively. While major components of the oregano essential oil were linalool (61.22%), carvacrol (15.02%), o-cymene (5.90%), linalool oxide (3.05%), and caryophyllene (2.77%), those of the thyme essential oil were found as carvacrol (51.82%), γ-terpinene (7.68%), o-cymene (7.55%), and linalool (4.22%).

Table 1  Major chemical composition of oregano and thyme essential oils (%)*

Components	RT	Oregano	Thyme
Borneol	23.72	—	2.05
Carvacrol	34.66	15.02	51.82
Carvacrol methyl ether	21.24	—	1.02
Caryophyllene	21.36	2.77	—
Linalool	19.71	61.32	4.22
Nerolidol acetate	24.22	—	1.47
cis-Linalool oxide	17.13	3.05	—
α-Phellandrene	6.19	—	1.42
β-Phellandrene	9.18	—	1.74
δ-Carene	9.76	—	1.99
γ-Terpinene	11.62	—	7.68
o-Cymene	12.31	5.90	7.55
Terpinen-4-ol	21.32	—	2.42
Thymol	33.82	1.88	2.14
Total (%)		89.9	85.5
RT: retention time; —: no detection.
*Minor components of the essential oils at <1% concentration were not included in the table.

Fitting of the Microbial Growth Curves

The fittings of modified Gompertz and logistic models to the experimental data with respect to the effect of essential oil and packing treatments were shown in Figs. 1 and 2, respectively. Both figures were plotted to indicate the growth data of E. coli O157:H7 versus growth time. In addition, in Fig. 1, the modified Gompertz model was simultaneously shown to fit experimental data obtained from the growth of E. coli O157:H7 in rainbow trout fillets as affected by different essential oil and packing treatments: aerobic, MAP, and VP conditions. However, as shown in Fig. 2, the logistic model was simultaneously demonstrated to fit the experimental data obtained from the species under the same conditions. Briefly, from these figures, the experimental growth curve of E. coli O157:H7 and the curves fitted to these curves using the modified Gompertz and logistic models can be explicitly seen. It can be also said that these models were satisfactorily fitted to each individual growth curve with high R ² values ranging from 0.95 to 0.99 (Table 2) and from 0.88 to 0.99 (Table 3).

Figure 2 Fitting of logistic model to experimental data for the growth of E. coli O157:H7 in rainbow trout fillets as affected by different essential oil and packing treatments: (a) aerobic, (b) MAP, and (c) VP conditions; means ± SD.

Figure 1 Fitting of modified Gompertz model to experimental data for the growth of E. coli O157:H7 in rainbow trout fillets as affected by different essential oil and packing treatments: (a) aerobic, (b) MAP, and (c) VP conditions; means ± SD.

Effect of Essential Oil and Packing Treatments

Table 2 shows the effect of different essential oil and packing applications on the derived parameters of the modified Gompertz model, a, B, m, EGR, and R ². The addition of oregano and thyme essential oils was observed to reduce the EGR values of E. coli O157:H7 in the fillets stored under all the packing conditions (Table 2) indicating that essential oil application delayed the growth of E. coli O157:H7. Antimicrobial effects of

Table 2  Parameters of modified Gompertz model for the growth of E. coli O157:H7 in rainbow trout fillets as affected by different essential oil and packing treatments

	Air packed			Modified atmosphere packed			Vacuum packed
Parameters	Control	Oregano	Thyme	Control	Oregano	Thyme	Control	Oregano	Thyme
Modified Gompertz
a (log CFU/g)	5.0 ± 0.4	9.5 ± 1.2	8.6 ± 0.1	18.7 ± 1.1	2.3 ± 0.03	10.7 ± 0.4	4.7 ± 0.5	2.7 ± 0.2	4.0 ± 0.3
B (1/h)	0.03 ± 0.00	0.02 ± 0.00	0.02 ± 0.00	0.01 ± 0.00	0.03 ± 0.00	0.00 ± 0.0	0.03 ± 0.00	0.02 ± 0.00	0.01 ± 0.00
m (h)	55.9 ± 4.9	90.9 ± 10.3	88.2 ± 9.9	185.9 ± 9.6	37.0 ± 0.3	209.0 ± 7.4	83.8 ± 12.5	92.5 ± 12.8	116.7 ± 13.3
EGR (log CFU/g/h)	0.040	0.037	0.035	0.039	0.027	0.018	0.024	0.011	0.010
R ^2	0.95	0.97	0.96	0.96	0.99	0.98	0.98	0.95	0.95
Regression parameters
MPE	−0.134	−0.169	−0.114	−0.016	0.020	0.046	−0.004	0.009	0.025
MBE	−0.005	−0.009	−0.007	0.009	0.005	0.002	0.005	−0.000	0.001
RMSE	0.200	0.223	0.212	0.145	0.069	0.103	0.067	0.035	0.078
EF	0.94	0.91	0.91	0.95	0.99	0.92	0.97	0.94	0.89
χ^2	0.05	0.06	0.06	0.03	0.01	0.01	0.01	0.00	0.01

Table 3  Parameters of logistic model for the growth of E. coli O157:H7 in rainbow trout fillets as affected by different essential oil and packing treatments

	Air packed			Modified atmosphere packed			Vacuum packed
Parameters	Control	Oregano	Thyme	Control	Oregano	Thyme	Control	Oregano	Thyme
Logistic
a (log CFU/g)	5.7 ± 0.60	8.1 ± 0.95	8.8 ± 1.07	8.9 ± 0.70	2.1 ± 0.018	11.3 ± 0.55	3.3 ± 0.23	2.6 ± 0.24	3.1 ± 0.29
d (dimensionless)	2.4 ± 0.03	2.3 ± 0.008	2.2 ± 0.09	2.0 ± 0.09	2.6 ± 0.069	2.5 ± 0.06	1.5 ± 0.05	1.4 ± 0.08	1.2 ± 0.01
c (1/h)	0.05 ± 0.00	0.03 ± 0.00	0.03 ± 0.00	0.02 ± 0.001	0.06 ± 0.00	0.01 ± 0.00	0.04 ± 0.00	0.03 ± 0.00	0.02 ± 0.00
μ (log CFU/g/h)	0.045	0.044	0.044	0.035	0.029	0.030	0.020	0.011	0.011
R ^2	0.93	0.91	0.91	0.93	0.99	0.93	0.97	0.93	0.88
Regression parameters
MPE	−0.205	−0.188	−0.150	−0.021	0.035	0.037	0.005	−0.007	0.001
MBE	−0.002	−0.011	−0.007	0.012	0.004	0.001	0.004	0.001	0.007
RMSE	0.214	0.229	0.217	0.151	0.054	0.093	0.064	0.033	0.073
EF	0.93	0.90	0.91	0.93	0.99	0.94	0.98	0.94	0.90
χ^2	0.06	0.07	0.06	0.03	0.00	0.01	0.01	0.00	0.01

extracts and hydrosols of oregano and thyme on E. coli O157:H7 were well established in the literature.[35 ⁻ 37] It is also well known that many spices and herbs have an effect to delay the onset of spoilage or prevent the growth of foodborne pathogens because their essential oils possess antimicrobial activity.[38] MAP and VP treatments had also remarkable delaying effects on the growth of E. coli O157:H7 as can be seen by the lower EGR values determined for the fillets (Table 2), but the delaying effect of VP was more remarkable. On the other hand, Boerema et al.[39] found that carbon dioxide packing extended the lag phase in cured sliced ham compared with vacuum packaging. Similarly, MAP and thyme oil (0.2% v/w), as a natural preservative in a previous study, were combined for preservation of fresh filleted sea bass during storage in a refrigerator. Total microbial counts for the sample in normal air exceeded log 7 CFU/g at the end of 7 days, while the fillet sample packaged under MAP with thyme oil in a refrigerator reached the total microbial levels later.[24] Inconsistency between the results could be attributed to the fact that the effectiveness of carbon dioxide depends on the original and final concentrations of the gas, the storage temperature, and the original population of the organisms.[40] It is known that MAP technique can extend shelf life of foods by showing antimicrobial effect. The major contributing factor to antimicrobial effect of this technique is the application of some cases, mainly carbon dioxide. The inhibitory effect of MAP is resulted from the delay of the lag phase and generation time during the logarithmic phase of the growth of organisms. By high concentrations of carbon dioxide, the microbial growth could be delayed in a variety of products.[40] It was suggested that carbon dioxide could show the antimicrobial activity as a consequence of the gas being absorbed onto the surface of the food by forming carbonic acid, following ionization of carbonic acid and a reduction in pH.[41] However, it was thought that such a minimal decrease in pH would not lead to such a remarkable delay in the growth of E. coli O157:H7. In this respect, Farber[42] suggested several reasons, such as the alteration of cell membrane function, direct inhibition of enzyme systems, and direct changes to physicochemical properties of proteins. As for the results obtained by logistic model, comparable results were also obtained. The μ values obtained from the model generally showed a similar trend with the EGR values obtained by the modified Gompertz model (Table 3).

Performance of Models

The regression parameters indicating the performance of Eqs. (1) and (2) are presented in Tables 2 and 3. The higher the values of EF are and the lower the values of MPE, MBE, RMSE, and χ² are, the better the goodness of fit will be.[33] In this respect, it can be seen that the modified Gompertz and logistic models described the relationship with very good fit values; however, statistical analysis indicated that higher EF values were generally obtained with the modified Gompertz model. However, both models resulted in similar absolute values of MBE, RMSE, MPE, and χ². Figures 3 and 4 show the correlations between observed and estimated data for the behavior of E. coli O157:H7 growth in the trout fillets calculated with the modified Gompertz and logistic models, respectively, for AP, MAP, and VP conditions. As can be seen from the figures, both models fitted successfully to all growth curves as assessed using the correlation coefficients (ranging from 0.92 to 0.98).

Figure 3 Correlations between observed and predicted data for the growth of E. coli O157:H7 in rainbow trout fillets calculated with a modified Gompertz model for (a) aerobic, (b) MAP, and (c) VP conditions.

Figure 4 Correlations between observed and predicted data for the growth of E. coli O157:H7 in rainbow trout fillets calculated with a logistic model for (a) aerobic, (b) MAP, and (c) VP conditions.

CONCLUSIONS

Considering the fact that essential oils have found limited applications in foods due to their strong flavoring characteristics and need for relatively high concentrations, the results of this study should be useful because the essential oils tested in this study exhibited their antimicrobial effect on E. coli O157:H7, even at a relatively low level (0.2% v/w) in combination with MAP and VP treatments. Therefore, it can be stated here that VP and MAP applications in conjunction with addition of thyme and oregano essential oils can be used as a combined hurdle system to delay the growth of E. coli O157:H7, as such combined hurdles have a synergistic effect. On the other hand, it was revealed that the modified Gompertz and logistic models could describe successfully the effect of oregano and thyme essential oils on the growth of E. coli O157:H7 in trout fillets stored under aerobic, MAP/VP conditions. Therefore, in the HACCP procedure, these models can be used to set critical limits for the storage of fish products.