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Foodborne pathogens continue to pose a potential food safety hazard in ready-to-eat (RTE) meat. Chlorine is commonly used to sanitize processing equipment where Escherichia coli O157:H7 (Ec) may survive and contaminate food products. The objective of this study was to characterize the survival behavior of Ec with different stresses on RTE meats. A multi-strain cocktail of Ec was pre-treated with freezing shock for 15 - 20 h and/or chlorine (0, 25, and 50 ppm) for one hour, and then inoculated onto RTE meat surfaces to obtain about 3.0 log CFU/g. Samples were stored at three abuse temperatures (12℃, 18℃, and 24℃) and Ec was enumerated during the storage. The freezing shock impact was studied using the Ec cocktail stored in a freezer overnight followed by chlorine exposure for one hour. The lag phase and growth rate of Ec were estimated using DMFit (Combase, Baranyi’s model). Results indicated that Ec growth was suppressed by chlorine treatment. Freezing shock was found to have little impact in terms of lag time and growth rate. The lag phase of Ec after exposure to 0 ppm of chlorine (50.3 h) was shorter than that of Ec treated with 25 ppm (54.6 h) and 50 ppm (164.1 h) at 12℃. However, the lag phase decreased with an increase in temperature, e.g. at 25 ppm, lag times were 54.6, 51.1 and 48.9 h for 12℃, 18℃ and 24℃, respectively. Lag times increased with an increase in chlorine concentration. At 24℃, lag times were 15.8, 48.9, and 52.4 h for 0, 25, and 50 ppm, respectively. The growth rate increased with an increase in temperature for 0 and 25 ppm chlorine levels, but decreased at 50 ppm level. Growth rate and lag phase as a function of temperature and chlorine concentration can be described by polynomial models and modified Ratkowsky-type and Zwietering-type models. Results of this study will contribute to risk assessment of RTE meats.

Foodborne pathogen (e.g. Escherichia coli O157:H7, Listeria monocytogenes and Salmonella spp.) contamination in foods is one of the safety concerns for the consumers, manufacturers and regulatory agencies. E. coli O157:H7 has been isolated from ready-to-eat (RTE) foods and implicated in several outbreaks [1,2]. The Centers for Disease Control and Prevention (CDC) [

With the ubiquity of E. coli O157:H7 in the environment, cross-contamination of this microorganism could result from contacts among contaminated processing equipments, workers, and foods which have no further treatment before consumption. In order to reduce the microbial foodborne hazard, surface sanitation using chlorinated water is one of the most common sanitation methods in food processing plants. Chlorine is a very effective agent in water treatment (portable water and water used in recreational settings) to eliminate E. coli O157:H7 [

Cold stress is another factor which could affect microbial survival. It is well known that most food pathogens may survive the cold storage which is commonly used in food industry. E. coli O157:H7 can multiply very slowly at temperature as low as 44˚F (6.7˚C) in ground beef [

The objectives of this study were 1) to investigate the effects of stresses (including freezing shock and chlorine exposure) on the growth of E. coli O157:H7 in RTE meat and 2) to model the E. coli O157:H7 behavior as affected by the imposed stresses and temperature.

A cocktail of five E. coli O157:H7 strains, which were isolated and obtained from the Food Safety and Inspection Service (FSIS) from pork sandwich (strain OB- 90520A), bottom round (strain OB1525C), beef patty (strain OB1423C), ground beef (strain OB1680G), and salami outbreak (strain 380 - 94), was used in this study. A loopful of each strain was transferred from a −80˚C stock culture into 10 ml Brian Heart Infusion broth (BHI, Becton, Dickinson and Company, Sparks, MD) and incubated at 37˚C for 6 h. A loopful of cell suspension of each strain was then separately transferred to a fresh 10 ml BHI broth and incubated at 37˚C for 24 h. Each strain was plated to verify cell counts for equal contribution with adjustment of adding 0.1% peptone water (PW) before the final cocktail preparation. One ml of cell suspension from each strain was combined, and the cocktail was further diluted with sterile 0.1% PW to the targeted level of E. coli O157:H7.

Chlorine solutions at 250 ppm and 500 ppm were prepared using a 5.25% sodium hypochlorite solution (Fisher Scientific, Pittsburgh, PA). The 250 ppm chlorine solution was prepared by adding 1 ml of sodium hypochlorite with 99 ml of sterile water in a dark glass bottle and mixed well just before the use. Similarly, the 500 ppm chlorine solution was prepared using the sodium hypochlorite and sterile water volume ratio at 1:49.

The experimental conditions used to simulate the survival behavior of E. coli O157:H7 exposed to chlorine concentration and abuse temperature were 0 - 50 ppm and 12˚C - 24˚C, respectively. For model development, the full combinations of chlorine and temperature included 9 sets of combination at chlorine concentrations of 0, 25 and 50 ppm and temperatures of 12˚C, 18˚C and 24˚C (i.e. 2 factors × 3 levels). Each set was repeated twice and with duplicate samples for each set; a total of 18 data sets (9 × 2) were used in the regression analyses. Freezing shock was performed using the same design with the addition of an overnight storage in a freezer. The developed models were verified by experimental data selected inside and outside the parameter ranges.

E. coli O157:H7 cocktail was exposed to chlorine solutions of various concentrations for one hour before inoculation onto RTE meat. The cocktail mixture was blended with a chlorine solution and 0.1% PW at a ratio of 1:1:8 (v/v) to attain the targeted microbial counts and chlorine concentrations. For example, a 5 log CFU/ml E. coli with 25 ppm chlorine was prepared using a 1 ml of 6 log CFU/ml E. coli, 1 ml of 250 ppm chlorine solution and 8 ml of PW. The mixture was vortexed 30 seconds and kept for 1 h to achieve the chlorine exposure time. The pH value of each final mixture was measured by replacing the 1 ml of E. coli O157:H7 cocktail with PW, i.e. 1 ml chlorine solution and 9 ml of PW, using a Daigger pH meter (Daigger Model 5500 pH Meter, Vernon Hills, IL). Since the E. coli O157:H7 survival counts will be reduced during the chlorine shocking treatment, the targeted inoculation counts were attained in several trials. For freezing shock treatment, the prepared cocktail was stored in a freezer overnight before mixing with chlorine. The cold shock was harsher than the treatment at 10˚C, 1.5 h by Bollman et al. [

The samples were stored at 12˚C, 18˚C and 24˚C for up to 10 days. Duplicate samples were enumerated for E. coli O157:H7 counts during storage using the CT-SMAC selective media. At least two sampling were performed for each treatment. To enumerate E. coli O157:H7 count in samples, 0.1 and 0.5 ml of the stomached liquid samples with proper dilutions were evenly spread on the CT-SMAC agar plates and incubated at 37˚C for 24 to 48 h.

The growth data were analyzed using the DMFit software available on the Combase website (www.combase.cc). Baranyi and Roberts model [

In order to cover a broader range of target parameters, the chlorine upper limit was initially set at 100 ppm. However, it was found difficult to achieve a stable and reliable initial count with 1 h of chlorine exposure. The chlorine was further reduced to 75 ppm and similar results were attained. It implied that chlorine at 75 ppm level and higher imposed a great stress on microbial cells so that a considerable percentage of E. coli O157:H7 was eliminated which is similar to the findings for L. monocytogenes reported by Sheen et al. [^{6} in seconds or minutes on contact. It should be noted that the chlorine impact on microbial survival depends on many factors, e.g. chlorine concentration, pH, water activity, organic compounds, temperature, contact surface, foods, etc. Peptone (organic compounds) may have enhanced the E. coli O157:H7 survival in the current study, which is consistent with the conclusion by Virto et al. [

In this study, the lag phase became longer at low temperatures (i.e. 12˚C) and increased the potential of other microbes (e.g. mold growth was observed) to outgrow E. coli O157:H7 that resulted in making reliable E. coli O157:H7 cell counts difficult. The 0 ppm chlorine treatment served as a control for E. coli O157:H7 growth on the RTE ham at three different temperatures. An additional step in preparing the stressed E. coli O157:H7 sample was performed to further examine E coli O157: H7 growth on RTE meat surface. Following one hour chlorine shock, E. coli (~10^{3} CFU/ml survival) was centrifuged (3000 × g) and washed with 0.1% PW twice and re-suspended, then inoculated on ham surface. No significant difference in either LAG or GR calculations compared with those without wash procedures was observed (P > 0.05). Chlorine residuals were tested using Reflectoquant strip and the RQflex plus system (LRE Relais und Elektronik GMBH, Munich, Germany), and the result showed the residual chlorine was less than 1 ppm.

The chlorine exposure or treatment in this study simulated the cross-contamination in a processing facility where equipment was sanitized between two shifts and biofilm is not involved. However, the sanitization was assumed to be incomplete and E. coli O157:H7 that survived on equipment surface experienced chlorine stress, and then contaminated the RTE meats and proceeded to the finished product line. The chlorine-stressed E. coli O157:H7 level used to inoculate the RTE meat was about 2.7 ± 0.2 log CFU/ml on the non-selective media plate count for each experiment. The cell counts with 50 ppm chlorine treatment were 0.5 log cfu/ml lower (P < 0.05, ANOVA, SAS v9.1) than that with 25 ppm exposure. The chlorine exposed E. coli O157:H7 was inoculated onto the ham surface, packed, vacuum sealed, then, incubated at 12˚C, 18˚C and 24˚C. The pH of the inoculum with 0, 25 and 50 ppm chlorine was 7.0 ± 0.2.

DMFit was applied to evaluate the growth/survival data to obtain lag time and growth rate of each experiment for secondary model development. All growth data fit the Baranyi model well with R^{2} ≥ 0.96. Based on the lag time and growth rate results with freezing shock, it can be concluded that there was no significant impact due to freezing stress (P > 0.05). Those data were combined with non-freezing shock data for model development. For modeling purpose, polynomial models are commonly used to describe or predict microbial lag phase and growth rate within ranges of selected parameters with proper experimental design. Cheroutre-Vialette and Lebert [

Lag time (LAG) and growth rate (GR) data from the experimental combinations were suitable for polynomial model development using the general linear regression procedures. Models developed for the LAG and GR are shown below in Equations (1) and (2), respectively.

LAG = 28.7980 + 3.0308CLO – 0.1501(T _{*} CLO)

+ 0.0241CLO^{2} (1)

where LAG is the lag time in hours, T is temperature in ˚C; CLO is chlorine concentration in ppm. The R^{2} is 0.91 and the temperature factor in terms of T and T^{2} were not significant (P > 0.05) in the regression analyses.

GR^{0.5} = 0.1923 + 0.0084CLO − 0.0005 (T _{*} CLO)

+ 0.0005T^{2} (2)

where GR is the growth rate in log cfu/hr and R^{2} = 0.87. The terms in T and CLO^{2} were not significant (P > 0.05). GR was expressed in a squared-root which can be easily compared with the results from the modified Ratkowskytype secondary model [

Although the polynomial equations may well describe the relationship among different parameters, the models typically were limited by the ranges of parameters applied in the experimental design. In the current study, temperatures at 12˚C - 24˚C and chlorine concentrations at 0 - 50 ppm were applied. Furthermore, polynomial models may include quadratic, cubic and quartic equations, in which the quadratic model was most commonly used. It is highly desirable to develop models which can cover a wider range of important factors for applications.

Another secondary model considered and tested for lag time is the hyperbola model, which was proposed by Zwietering et al. [

where, p is the rate of change of lag time as a function of temperature; q is the temperature at which lag time is infinite; and m is an exponent to be estimated. Other factors (in terms of X_{i}) may be integrated into the model (using Π operator) and a general multi-factor equation can be generated which also shown in Equation (3). However, this kind of model with applications is little investigated and may be applicable to certain conditions only. For current application, the model is constructed to take into account of both temperature and chlorine factors and shown as:

Or,

where, a, b and c are coefficients to be estimated. The temperature (T) and chlorine concentration (CLO) where E. coli O157:H7 has infinite lag time were set at 6˚C and 100 ppm, respectively. Rutala and Cole [

Statistical analyses showed F-value = 256.36; Pr > F (<0.0001); Sum of squared error/uncorrected total = 1937.8/101294.0. The R^{2} is typically not available for non-linear regression analysis. Coefficient “a” does not satisfy the definition set for “p” in Equation (3), which was not derived based on growth theory but relatively an empirical model. Therefore, “a” may only be treated as an unknown to be estimated as well as “b” and “c”. In Equation (5), if (100 + CLO) was replaced by a coefficient “k”, which became similar to temperature factor term, the non-linear regression result was found singular and failed to converge. The T_{min} set to 4˚C or 5˚C was also tested, the regression showed similar statistical results.

The secondary model for growth rate consideration was the square-root-type or Ratkowsky-type model [24, 28,29]. MeMeekin et al. [

where, k is a coefficient to be determined and X, Y, Z can be water activity or pH or others. T_{min} is the minimum growth temperature. X_{min} (or Y_{min}, Z_{min}) is the minimum factor X (or Y, Z), e.g. pH, below which growth becomes not possible. A more general model expression was presented by Ross et al. [

where, k, g and h are coefficients to be determined. The non-linear regression results are:

Statistical analyses showed F-value = 218.45; Pr > F (<0.0001); Sum of squared error/uncorrected total = 0.0553/2.4526.

Since Equation (7) was derived from the general observation of different microbial growth in various conditions, it might not fit all conditions which impact the growth curve. Huang [_{max} = k(T − T_{min})^{1.5}, which can be converted to (μ_{max)}^{1/2} = k_{1}(T − T_{min})^{0.75}. With more parameters involved, the model may become much complicated to achieve acceptable statistical analyses.

Two secondary types of models are presented in this study, i.e. the linear regression models and the empirical models based on observations and curve fitting. It is interesting to compare the models in terms of their predicted vs. experimental data.

the solid line, the predicted values are over or under estimated, respectively. Similarly,

Model performance in accuracy of prediction was evaluated using a 40 ppm chlorine shock (pH 7.1), then inoculated on ham and stored at 12˚C and 18˚C. The predicted LAG and GR (for 12˚C) from Equations (1), (2), (6) and (9) were 116.542, 0.1298, 115.0272 and 0.0971, respectively. The experimental data were 109.891 (for LAG) and 0.1228 (for GR). For 18˚C, predicted values were 80.518, 0.1091, 68.3198 and 0.1296, respectively, and the experimental data were 76.245 (LAG) and 0.1238 (GR). All developed models predicted the LAG and GR within 20% of the experimental values at both 12˚C and 18˚C. Another experiment was performed to evaluate the developed models outside the chlorine parameter range, i.e. chlorine shock at 60 ppm and 18˚C abused temperature. The experimental data resulted for LAG and GR were 125.323 h and 0.1254 log cfu/h, respectively. Equations (6) and (9) predicted the values of 134.804 h and 0.1152 log cfu/hr, which were within 10% range of the experiment data. However, Equations (1) and (2) showed the predicted values of 135.298 h (LAG) and 0.1013 log cfu/h (GR). It is generally agreed that the linear regression model with experimental design is only applicable within the designed parameter ranges. In the current case, Equations (1) and (2) were found suitable for applications with chlorine shock at 60 ppm. Since no

reliable data with chlorine shock at 75 ppm were available, Equations (1) and (2) can not be further validated for wider range of chlorine concentration. General speaking, secondary models similar to modified Ratkowskytype and Zwietering-type might be more suitable for applications with broader parameter ranges.

This study provided useful results and information regarding the effects of chlorine concentration, abuse temperatures and freezing shock on the survival and growth behavior of E. coli O157:H7 in RTE meat. The freezing shock was found to have little impact on E. coli O157:H7 survival behavior in terms of lag time and growth rate. Two types of secondary models, as a function of chlorine concentration and abuse temperature, were developed and presented, i.e. the polynomial regression models, modified Ratkowsky-type and Zwietering-type models. The modified Ratkowsky-type and Zwietering-type models were applicable to a wider range of parameters, although the polynomial regression models were found suitable with chlorine at 60 ppm which is outside the parameter range. Those models may be used for risk assessment, within applicable conditions, to enhance the RTE meat safety.

The authors thank Dr. John Phillips for his assistance in the SAS program set-up and analyses, and recognize the dedicated laboratory work of Sonya Costa and Peggy Williamson of the Food Safety and Intervention Technologies Research Unit, ERRC/ARS/USDA, Wyndmoor, PA.