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Food Control 36 (2014) 273e279

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Food Control

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Modeling Vibrio parahaemolyticus inactivation by acidic electrolyzedwater on cooked shrimp using response surface methodology

Jing Jing Wang a, Zhao Huan Zhang a, Ji Bing Li a, Ting Lin a, Ying Jie Pan a, b, c,Yong Zhao a, b, c, *

a College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, Chinab Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture,Shanghai 201306, Chinac Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, China

a r t i c l e i n f o

Article history:Received 19 March 2013Received in revised form4 August 2013Accepted 21 August 2013

Keywords:Vibrio parahaemolyticusAcidic electrolyzed waterResponse surface methodologyInactivation

* Corresponding author. College of Food Science andUniversity, Shanghai 201306, China.

E-mail address: yzhao@shou.edu.cn (Y. Zhao).

0956-7135/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.foodcont.2013.08.031

a b s t r a c t

Vibrio parahaemolyticus is the leading cause of seafood-derived illness in China and a possible mecha-nism leading to illness is cross contamination of cooked shrimp. The objective of this study was toestablish a mathematical model of the inactivation of V. parahaemolyticus on cooked shrimp by acidicelectrolyzed water (AEW) as a function of three variables (NaCl concentration to electrolysis, X1; treat-ment time, X2; treatment temperature, X3) and to define priority factors which can significantly enhancethe bactericidal efficiency to reduce the risk of illness caused by V. parahaemolyticus. The combined ef-fects of NaCl concentration (0.7e2.4 g/L), treatment time (3.6e10.4 min) and temperature (23e57 �C) onLog reductions of V. parahaemolyticus on cooked shrimp were investigated according to a central com-posite design, and the Log reductions were modeled using a response surface model. The result showedthe established RS model had a goodness of fitting quantified by the parameters of R2 (0.982), lack of fittest (p > 0.05), the root-mean-squares error (RMSE ¼ 0.15), the accuracy factor (Af ¼ 1.10) and bias factor(Bf ¼ 0.99). The model was validated with additional random 8 conditions within the range of theexperimental domain. It showed that the established RS model possessed a good performance andsuitability approved by RMSE (0.43), Af (1.28) and Bf (1.19). Moreover, the effects of the independentvariable and their interactions on response value were ranked as X3 ¼ X3

2 >> X1X3 > X2 > X1 according toPareto charts and response surface plots analysis. The present work could serve as useful tools forpredicting the inactivation of V. parahaemolyticus on cooked shrimp by AEW.

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1. Introduction

Food food-borne illness caused by food-borne microorganismshas become an increasingly important public health issue. Somesterilization methods are used to ensure food safety, especiallytraditional thermal processing methods. However, traditionalthermal processing methods cause loss of desirable propertiesrelated to texture, flavor, color, and nutrient components. Mean-while modern consumers are eager to consume high-quality,minimally processed, nutritious, and fresh-like products. There-fore food scientists and the food industry are searching for someemerging methods to inactivate undesired microorganisms withless adverse effects on products’ qualities to meet consumers’

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demands (Ju, Gao, Yao, & Qian, 2008). Acidic electrolyzed water(AEW), which is generated from anodic electrolysis of a dilute saltsolution in an electrolytic cell, is a novel non-thermal food pres-ervation bactericidal technology and has a strong disinfectant effecton a variety of microorganisms (Park, Hung, & Brackett, 2002;Smigic et al., 2009; Xie, Sun, Pan, & Zhao, 2012a) due to its specif-ically low pH, available chlorine concentration (ACC) and oxida-tionereduction potential (ORP) (Abadias, Usall, Oliveira, Alegre, &Vinas, 2008; Hao, Qiu, Li, Chen, Liu, & Li, 2012; Kim, Hung,Brackett, 2000; Liao, Chen, & Xiao, 2007; Xiong, Liu, Liu, & Li,2010). Moreover, it has been clarified that AEW has less adverseimpact on users’ health as well as the environment, which makes itattracted a great deal of attention from the food, medical, andagricultural industries (Huang, Hung, Hsu, Huang, & Hwang, 2008;Katayose, Yoshida, Achiwa, & Eguchi, 2007; Sakurai, Ogoshi, Kaku, &Kobayashi, 2002; Sakurai, Nakatsu, Sato, & Sato, 2003; Tanaka et al.,1999).

Table 1Code and level of variables used for the central composite design.

Levels Variables

X1

NaCl concentration (g/L)X2

Time (min)X3

Temperature (�C)

�1.68 0.7 3.6 23�1 1.0 5 300 1.5 7 401 2.0 9 501.68 2.4 10.4 57

J.J. Wang et al. / Food Control 36 (2014) 273e279274

Cooked seafood, especially shrimp that are picked by hand, canbe easily contaminated with food-borne pathogenic microorgan-isms, such as Listeria monocytogenes, Vibrio parahaemolyticus, et al.,through poor manufacturing practices and personal hygiene (Liu,Duan, & Su, 2006; McCarthy, 1997; Xie et al., 2012a), and duringeach course including storage, transportation and distribution(Dupard, Janes, Beverly, & Bell, 2006; Gudbjorndottir, Einarsson, &Thorkelsson, 2005). V. parahaemolyticus is a human pathogen thatoccurs naturally in the marine environments and can be frequentlyfound in freshly caught and landed shrimp (Xie et al., 2012a).Moreover, microbiologic risk assessment related withV. parahaemolyticus on cooked black tiger shrimp has been con-ducted in Malaysia in 2008 and 2012, and the results showed thatconsuming cooked shrimp contaminated with V. parahaemolyticuscan cause illness (Sani, Ariyawansa, Babji, & Hashim, 2012; Sani,Ariyawansa, & Babji, 2008). Therefore, it is imperative that shrimpneed to be sanitized before consumption in order to reduce the riskof food-borne pathogens contamination (Xie, Sun, Pan, & Zhao,2012b).

Up to now, AEW has been widely used as a sanitizing agent inboth research and practice. Although there are a large number ofpublished literatures with regard to using AEW to decontaminatefood-borne pathogenic microorganisms in aquatic product,including V. parahaemolyticus on cooked shrimp (Xie et al., 2012a,2012b), Listeria monocytogenes on raw fish surfaces (McCarthy &Burkhardt, 2012), E. coli and V. parahaemolyticus on tilapia (Huanget al., 2006), et al, few work have been done to determine thecombined effects of the NaCl concentration to electrolysis, treat-ment time and temperature of AEW on the inactivation of patho-genic microorganisms, systematically. And priority factors can’t beput forward definitely when food industry want to enhance thebactericidal efficiency to reduce the risk of illness caused by path-ogens using AEW.

Mathematical modeling, to describe the behavior (growth orinactivation) of microorganisms, is a useful tool to assess andimprove food safety and quality. The response surface methodology(RSM) is an empirical modeling approach that can be used to dealwith complex phenomena, including the investigation of microbialchanges during food processing (Bover-Cid, Belletti, Garriga, &Aymerich, 2012). The most common experimental design used inRSM is the Central Composite Design (CCD) (Liu & Tzeng, 1998;Reddy, Mrudula, Ramesh, Reddy, & Seen-ayya, 2000) which hasequal predictability in all directions from the center. Particularly,the CCD has been successfully used to describe the high hydrostaticpressure induced inactivation of Bacillus sporothermodurans LTIS27spores (Aouadhi et al., 2013), Salmonella inactivation on dry-curedham (Bover-Cid et al., 2012), reduction of Bacillus subtilis (Gao, Ju,Qiu, & Jiang, 2007).

The objectives of the present study were (1) to build and vali-date a RS model of the inactivation of V. parahaemolyticus by AEWon cooked shrimp as a function of NaCl concentration to electrol-ysis, treatment time and temperature; (2) to define priority factorswhich can significantly enhance the bactericidal efficiency toreduce the risk of illness caused by V. parahaemolyticus on cookedshrimp.

2. Materials and methods

2.1. Bacterial strain and culture preparation

Vibrio parahaemolyticus strain ATCC33847was used in the study.To prepare the inoculum culture, 20 ml of the stock culture (storedin 25% glycerol at �20 �C) were transferred to 10 ml tryptic soybroth (TSB, Beijing Land Bridge Technology Company Ltd., Beijing,China) plus 3 % NaCl and incubated at 37 �C for 18e20 h. Twenty ml

were transferred to a second tube of 10 ml TSB and incubated for10e12 h at 37 �C, resulting in early-stationary-phase culture.Enriched cultures were pooled into a sterile centrifuge tube andcentrifuged at 3000 g, 15 �C for 10 min (Centrifuge 5417R, Eppen-dorf, Germany). The resulting cell pellet was washed withphosphate-buffered saline (PBS) twice, and subsequently adjustedto a final inocula level of 9 log CFU/ml on average.

2.2. Preparation and inoculation of cooked shrimp samples

Live shrimp (10 � 1 g per sample) were purchased from a localsupermarket in Shanghai, PR China and stored at �20 �C beforetreatment.

Shrimp thawed firstly and then were exposed to boiling waterbath for 20min to inactivate the native bacteria of shrimp includingthe intestinal bacteria. Then cooked shrimp were transferred into abiosafety hood quickly until the temperature of shrimp cooled toroom temperature (25 � 2 �C). Before inoculation, shrimp werepicked up with a sterile tweezers to drain excessive water onshrimp to avoiding the inoculated bacteria dropping downwith theexcessive water. Each sample was inoculated withV. parahaemolyticus by gently spreading each 50 ml culture sus-pension onto the backside and abdominal region of shrimp,respectively. Subsequently, all inoculated shrimp were air-driedunder biosafety hood for 30 min at room temperature to allowfor bacterial attachment. The final concentration ofV. parahaemolyticus inoculated on shrimp was 7 log CFU/g onaverage. Samples for each treatment were prepared and sampled atleast in duplicate.

2.3. Preparation of acidic electrolyzed water (AEW)

Different physicochemical characteristics of AEWwere preparedby electrolyzing different concentration of sodium chloride (NaClconcentration to electrolysis) according to CCD (Table 1) usingacidic electrolyzed water generator (FW-200, AMANO, Japan) withan electrochemical cell where the anode and cathode are separatedby a diaphragm. The generator was allowed to run for 15 min withthe amperage setting as 10 A before collecting water. The pH andORP were determined using a pH/ORP meter (model pH 430,Corning Inc., NY). The ACC in AEW was determined by a colori-metric method using a digital chlorine test kit (RC-2Z, KasaharaChemical Instruments Corp., Saitama, Japan). All measurementswere carried out in triplicate. The AEWhad a range of pH 2.32e2.41,ORP 1158e1172, and ACC 40e105 ppm. The solutions were placedinto a water bath to reach the treatment temperatures. Consideringthe acidic electrolyzed water is not stable enough, immediate ex-periments were done according to experimental sets.

2.4. Treatment of shrimp samples with AEW and microbiologicaldeterminations

Inoculated shrimp (10 � 1 g) were immersed in 100 ml AEWunder different conditions (Table 2). After treatment, all samples

J.J. Wang et al. / Food Control 36 (2014) 273e279 275

were removed from AEW and diluted in 100 ml neutralizing agent(PBS containing 0.8% Na2S2O3), then homogenized for 2 min in astomacher (BagMixer400VW, Interscience, France). Surviving bac-teriawas determined by serial dilutions in sterile 3% saline solution.A 0.1 ml of diluted sample was spread onto Vibrio selective TCBS(thiosulfateecitrateebile saltsesucrose, TCBS, Beijing Land BridgeTechnology Company Ltd., Beijing, China) agar plates, colonies werecounted after the plates were incubated at 37 �C for 18 h. Un-inoculated cooked shrimp yielded no colonies for either of micro-organisms on the selective agar (TCBS). Two trials with two repli-cates per trial in each treatment conditions were done.

2.5. RS model development and statistical analysis

Analysis of the Variance (ANOVA) and the corresponding post-hoc contrasts (LSD) were performed to compare the results of thedifferent experiment conditions. RSM was used to describe therelationship and interactions between three technological variables(Table 1) on the inactivation of V. parahaemolyticus. The responsevalue was expressed as the Log reductions between final load afterthe treatments and the initial inocula per gram shrimp. DesignExpert package (Version 8.0.6, Stat-Ease Inc., Minneapolis, USA)was used for regression analysis and to generate the second orderpolynomial model. The statistical significance and goodness of fit ofthemodel were evaluated using the determination coefficients (R2),the P-values from the Fisher F-test and the Lack of Fit test. Theaccuracy of RS model describing the inactivation ofV. parahaemolyticus was evaluated by following criteria: the biasfactor (Af, Eq. (1)) and accuracy factor (Bf, Eq. (2)); the root-mean-squares error (RMSE, Eq. (3)) (Dong, Tu, Guo, Li, & Zhao, 2007;Zurera-Cosano, García-Gimeno, Rodríguez-Pérez, & Hervás-Martí-nez, 2006) which were shown as follows :

Af ¼ 10

�PjLogðpred=obsÞj

n

�(1)

Bf ¼ 10

�PLogðpred=obsÞ

n

�(2)

Table 2Actual and predicted reduction of V. parahaemolyticus on cooked shrimp by RSmodel according to the central composite design arrangement.

Trail X1 X2 X3 Actual value(Log CFU/g)

Predicted value(Log CFU/g)

1 1.0 5 30 0.73h(0.045) 0.642 2.0 5 30 0.80gh(0.117) 0.733 1.0 9 30 0.91gh(0.067) 1.064 2.0 9 30 0.83gh(0.066) 0.715 1.0 5 50 1.70e(0.070) 1.936 2.0 5 50 2.93c(0.065) 2.897 1.0 9 50 2.83c(0.307) 3.018 2.0 9 50 3.32b(0.037) 3.529 0.7 7 40 0.81gh(0.123) 0.5810 2.4 7 40 1.02g(0.176) 1.0911 1.5 3.6 40 0.90gh(0.127) 0.9312 1.5 10.4 40 2.00d(0.057) 1.8113 1.5 7 23 1.24fg(0.102) 1.3714 1.5 7 57 5.10a(0.415) 4.8215 1.5 7 40 1.32f(0.148) 1.0916 1.5 7 40 1.11fg(0.075) 1.0917 1.5 7 40 1.10fg(0.143) 1.0918 1.5 7 40 1.01g(0.071) 1.0919 1.5 7 40 0.99g(0.070) 1.0920 1.5 7 40 0.98g(0.088) 1.09

Mean (standard deviation); Values with different superscript letters indicate sta-tistical significant differences according to the LSD test; All log-transformed colonycounts didn’t conform to a ND (Not Detectable).

RSME ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP ðobs� predÞ2

n

s(3)

where obs was observed values, pred was predicted values by RSmodel, and the n stands for the number of observations. Ideally,predictive models would have Af ¼ Bf ¼ 1. The response surfacegraphs and contour plots were used to describe the individual ef-fects and interactions of the variables. Beta coefficients corre-sponding to the standardized coefficients were used to definepriority factors.

2.6. Model validation

After the establishment of inactivation model, the performanceof RS model was assessed with additional random eight conditions(Table 3) within the range of the experimental domain, but not usedto build the model (Bover-Cid et al., 2012; Dong et al., 2007). Actualexperimental values were obtained according to the random eightconditions, while predicted values were calculated from theestablished RS model. The line of 100% correlation (y ¼ x) was usedto evaluate the performance of RS model between the actualexperimental values and predicted values.

3. Results and discussion

3.1. Establishment and evaluation of response surface model

Twenty trials of experiments under different combined condi-tions (NaCl concentration to electrolysis, X1; treatment time, X2;treatment temperature, X3) were designed by central compositedesign (CCD). Table 2 showed the results of Log reductions ofV. parahaemolyticus on cooked shrimp under different combinedconditions. The actual values of Log reductions ranged from 0.73 logCFU/g (81.379%) to 5.10 log CFU/g (99.999%). The results of Log re-ductions involved in the CCD were very close to the results ofpublished literatures from our group under similar conditions (Xieet al., 2012a).

Based on Table 2, the RS model was established by regression fit,and the established model was as follows:

R ¼ 11:11140� 0:38163X1 þ 0:41363X2 � 0:58673X3

þ 0:025006X21 � 0:11225X1X2 þ 0:00819X1X3

� 0:35435X22 þ 0:04325X2X3 þ 0:00708X2

3

where R was response value (log CFU/g). The results of the ANOVAfor quadratic model indicated the goodness of fit of the regressionequation: the R2 and Adj.R2 were 0.982 and 0.966, respectively. Thevalue of the Adj.R2 indicated a high degree of correlation between

Table 3Actual and predicted reduction values of V. parahaemolyticus on cooked shrimp byRSM under the additional random eight conditions

X1 X2 X3 Actual valuea

(Log CFU/g reductions)Predicted value(Log CFU/g reductions)

1.8 4 45 1.07(0.12) 1.651.0 5 55 3.49(0.01) 3.101.0 5 50 1.31(0.01) 1.921.8 6 45 1.66(0.00) 1.731.5 7 35 0.52(0.05) 0.721.5 7 45 2.10(0.19) 1.741.5 8 40 0.88(0.03) 1.201.5 8 50 2.44(0.14) 3.00

a Mean (standard deviation).

J.J. Wang et al. / Food Control 36 (2014) 273e279276

the observed and predicted values, which suggested that only 3.4%of the total variation can’t be explained by the current model (Ding,Dong, Rahman, Oh, 2011). RMSE also provided a measure of thegoodness-of-fit of a model to the data used to produce it (Box &Draper, 1987; Dong et al., 2007) and its value was 0.15 indicatingthat the RS model fitted well with the observed data.

The statistical significance and adequacy of the model weredetermined using the Fisher F-test and Lack of Fit test (Table 4). F-value of 59.83 and probability value (p < 0.0001) indicated that thedifferences between different treatments were highly significant.The model is adequate since the Lack of Fit test(0.063878 > p > 0.05) was not significant (Bahçeci & Acar, 2007).

In order to evaluate the prediction model established, internalevaluations according to the data to build the RS model wereapplied as described by McClure, Beaumont, Sutherland, andRoberts (1997). The accuracy factor (Af) and bias factor (Bf) calcu-lated for the model were 1.10 and 0.99, respectively. The Af of theinternal model evaluations was within the acceptable limit, takinginto account that the accuracy factor will increase by 0.10e0.15 forevery variable in the model (Bover-Cid et al., 2012; Ross, Dalgaard,& Tienungoon, 2000). Results of internal evaluation showed that Bf(0.99) was lower than 1 for predicting response value. When Bf wasbelow 1, it indicates that the predicted reduction values ofV. parahaemolyticus were, on average, higher than the observedvalues, which tend to be fail-safe in practice (Dong et al., 2007; Rosset al., 2000). Moreover, Bf valuewas within the proposed acceptablelimits, i.e. 0.75e1.25, whichwas suggested by Ross et al. (2000), andindicated that the established RS model had a good predictiveperformance.

3.2. Response surfaces describing the effect of AEW variables

The graphical representations were obtained by solving the RSMregression equation using Design Expert 8.0.6. In Fig. 1(AeC),response surfaces and contour plots showed the effect of NaClconcentration, treatment time and temperature of AEW on theinactivation of V. parahaemolyticus on cooked shrimp. In eachfigure, one variable was maintained constant at its central point.

Fig. 1A showed the effect of treatment temperature and time onthe inactivation of V. parahaemolyticus while keeping the NaClconcentration at its central point (1.5 g/L). The reduction ofV. parahaemolyticus increased with increasing treatment tempera-ture and time. However, the sensitivity of V. parahaemolyticus onshrimp to treatment temperature increase was higher than thesensitivity to the treatment time increase. And the influence oftemperature on inactivation of V. parahaemolyticus did not follow a

Table 4Analysis of variance (ANOVA) for response surface quadratic model for reduction ofV. parahaemolyticus on cooked shrimp.

Source Sum ofsquares

Degrees offreedom

Meansquare

F value P value

Model 23.92806 9 2.658673 59.82653 <0.0001X1 0.938444 1 0.938444 21.21768 0.000987X2 0.31169 1 0.31169 7.047136 0.02438X3 14.35527 1 14.35527 324.5645 <0.0001X12 0.145818 1 0.145818 3.259742 0.100169

X22 0.113029 1 0.113029 2.588412 0.141727

X32 7.238135 1 7.238135 163.3891 <0.0001

X1X2 0.099013 1 0.099013 2.238616 0.165478X1X3 0.214513 1 0.214513 4.827046 0.052703X2X3 0.374113 1 0.374113 8.41843 0.015794Residual 0.444397 10 0.044439Lack of fit 0.36265 5 0.072529 4.436094 0.063878Pure error 0.08175 5 0.01635Total 24.37246 19

first order kinetics as the quadratic term of temperature was sig-nificant. This was in similar with study performed by Ding et al.(2011).

The interactions of treatment time and NaCl concentration at aconstant of temperature (40 �C) was shown in Fig. 1B. Over 1 log(>90.0%) reduction of V. parahaemolyticus was obtained in NaClconcentration range of 1e1.5 g/L and the time range of 8e10 min.Comparedwith treatment temperature, the NaCl concentration andtreatment time played a slight effect on the inactivation ofV. parahaemolyticus in selected three variables domain.

It was observed that Log reductions changed significantly(p < 0.05) with the interactions between temperature and NaClconcentration to electrolysis (Fig. 1C) indicating that the NaClconcentration and treatment temperature played an importanteffect on the inactivation of V. parahaemolyticus in cooked shrimp.However, the treatment temperature of AEW played more positiveeffects on inactivating V. parahaemolyticus on cooked shrimpcompared with NaCl concentration at treatment time 7 min. Whenthe treatment temperature was lower than 38.5 �C, the reductionsof V. parahaemolyticus were less than 1.0 log CFU/g (<90%) and didnot change greatly from the contour, although the NaCl concen-tration had an increase from 1.0 g/L to 2.0 g/L. Subsequently, withthe temperature increasing (>38.5 �C), more reduction ofV. parahaemolyticus on cooked shrimp was observed. And over2.5 log CFU/g (>99.7%) reductions of V. parahaemolyticus could beobtained in NaCl concentration domain 1.2e2 g/L and treatmenttemperature domain 48e50 �C with the treatment time at 7 min.

3.3. Model validation

Validation is an important step, which can assess the capacity ofdeveloped model (Bang et al., 2008). Comparative plot (Bahçeci &Acar, 2007; Dong et al., 2007; Wang, Rahman, Ding, & Oh, 2011)between the observed and predicted Log reductions ofV. parahaemolyticus by RSM under additional random eight condi-tions (Table 3) was shown in Fig. 2. The predicted value showed agood agreement with the actual values, since the points of responsevalues were very close to the line of 100% correlation (y¼ x) (Fig. 2),and the R2 and Adj.R2 were 0.96, 0.95, respectively. Simultaneously,the Af, Bf, RSME of the external evaluations were calculated andtheir values were 1.28, 1.19 and 0.43, respectively. In any case, thevalues of Af, Bf were within the proposed acceptable limitsfollowing above mentioned principles. Moreover, RMSE revealedthat the predicted values from RS model fitted well with theobserved data. However, it should be noted that this study hasexamined only RS model established within certain range ofexperimental conditions in laboratory, and it remains primarily aresearch rather than an industrial tool as considered by McMeekin,Olley, Ross, and Ratkowsky (1993).

3.4. Defining priority factors

After the establishment of the model, priority factors wereneeded to be defined definitely to reduce the risk of illness causedby V. parahaemolyticus using AEW based on the establishedmodel. Pareto chart of the standardized coefficients correspond-ing to the independent variable and their interactions with sta-tistical significance (p < 0.05) (Table 4) was used to investigatethe relative contribution (Fig. 3). The result showed the treatmenttemperature was the most important factor determining theextent of AEW inactivation of V. parahaemolyticus on cookedshrimp within the selected variables, and the effects of the in-dependent variable and their interactions were ranked asX3 ¼ X3

2 >> X1X3 > X2 > X1 according to Fig. 3. Similar resultswere found in some published literatures. Xie et al. reported that

Fig. 1. Response surface plots describing the effect of AEW technological variables on inactivation of V. parahaemolyticus on cooked shrimp. (A) Effects of treatment time (X2) andtreatment temperature (X3) of AEW on reduction of V. parahaemolyticus with AEW from electrolyzing NaCl concentration of 1.5 g/L; (B) Effects of NaCl concentration to electrolysis(X1) and treatment time (X2) of AEW on reduction of V. parahaemolyticus with AEW treatment temperature 40 �C; (C) Effects of NaCl concentration to electrolysis (X1) and treatmenttemperature (X3) of AEW on reduction of V. parahaemolyticus with AEW treatment time 7 min.

J.J. Wang et al. / Food Control 36 (2014) 273e279 277

mild heat greatly enhanced efficacy of electrolyzed water againstV. parahaemolyticus on cooked shrimp and the effective order oftemperatures on bactericidal activities of AEW was50 �C > 20 �C > 4 �C (Xie et al., 2012a, 2012b). In addition, Ozer

et al. reported that the treatment temperature of electrolyzedwater played a more important role in inactivating Escherichia coliO157:H7 on raw salmon compared with other selected variables(Ozer & Demirci, 2006).

Fig. 2. Linear correlation comparison between observed and predicted reductions ofV. parahaemolyticus on cooked shrimp under additional random 8 conditions withinthe range of the experimental domain.

Fig. 3. Pareto chart of the effects of technological (independent) variables with sta-tistical significance (p < 0.05) on the AEW inactivation of V. parahaemolyticus oncooked shrimp. Beta coefficients correspond to the standardized coefficients estimatedby the regression analysis. Te is the abbreviation for Temperature.

J.J. Wang et al. / Food Control 36 (2014) 273e279278

4. Conclusions

From the model validation and mathematical evaluation,established RS model had a good statistical performance and suit-ability due to its higher R2 and lower RMSE as well as acceptableranges of the accuracy factor and bias factor. The effects of selectedvariables (NaCl concentration to electrolysis, X1; treatment time,X2; treatment temperature, X3) on response value were ranked asX3 ¼ X3

2 >> X1X3 > X2 > X1. The present work could serve as usefultools for predicting the inactivation of V. parahaemolyticus oncooked shrimp by AEW, and could be beneficial to control the riskto health of the lack of microbiological safety in sea foods.

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China (31271870), the project of Science and

Technology Commission of Shanghai Municipality (11310501100,12391901300), Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation (11DZ2280300), Cross-disci-pline project (B5201120040).

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