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Journal of Food Pmtection, Vol. 70, No. 3, 2007, Pages 607-615Copyright e International Association for Food Protection

Pathogen Kinetics and Heat and Mass Transfer—BasedPredictive Model for Listeria innocua in Irregular-Shaped

Poultry Products during Thermal ProcessingABANI K. PRADHAN,' YANBIN Ll, 1 - 2 - 3 JOHN A. MARCY,2 MICHAEL C. JOHNSON, 3 - 4 AND MARK L. TAMPLIN5

'Department of Btologtcoi and Agricultural Engineering, 2Ce,tter of Excellence for Poultry Science. 3 cenrer for Foot! Safe/v. institute of Food Scienceand Engineering, and 4Dejiorinie,i, of Food Science, Unnersitv of Arkansas, Faettei-il/e, Arkansas 7270/: and 5Microbial Food Sofk'rv Research,

U.S. Department of Agriculture, Ag rica/na-al Research Service, Eastern Rei,'ionol Research Cenie,-, Wvnd,noor. i'ennsvls'ania 19038 ' USA

MS 06-212: Received I I April 2006/Accepted 30 September 2006

ABSTRACT

The increasing demand of ready-to-eat poultry products has led to serious concerns over product safety, and more emphasishas been placed on thorough cooking of products. In this study, processing conditions and thermal inactivation of Listeriainnocua in chicken breast meats were evaluated during convection cooking in a pilot-plant scale air-steam impingement oven.A predictive model was developed by integratin g heat and mass transfer models with a pathogen kinetics model to predicttemperature. water content, product yield, and bacterial inactivation during air-steam impingement cookin g . Skinless bonelesschicken breasts were cooked at oven air temperatures of 177 and 200°C for 2 to 10 min at a humidity of 70 to 75% (moistureby volume) anti an air velocity of 1 nm/s at the exit of the nozzles. The reduction in f.isteria in chicken breasts after 2 to 5min of cooking was from 0.3 to 1.4 log CFU/g and froni 0.8 to 1.8 log CFIJ/g at 177 and 200°C, respectively. After cookingfor 10 mm at both temperatures, no survivors were detected in any of the cocked chicken breasts from an initial bacterialconcentration of 10° CFU/g. The standard errors of prediction for the endpoint center temperatures after 2 to 10 min of cookingwere 2.8 and 3.0C for air temperatures of 177 and 200°C, respectively. At 177 and 200°C. the median relative errors ofprediction for water content were 2.5 and 3.7% and those for product yield were 5.4 and 8.4%, respectively. The developedmodel can be used as a tool to assist in evaluating thermal processing schedules for poultry products cooked in an air-steamimpingement oven.

Listeria inonocvtogenes is a foodhorne pathogen withconsiderable public health significance and is a major con-cern to food industries and regulatory agencies. L. mono-cytogenes is a relatively thermotolerant, gram-positive,non—spore-forming bacterium that has several characteris-tics that make it a formidable pathogen in food-processingenvironments: relatively high heat resistance and salt tol-erance, ability to grow at refrigeration temperatures andover a wide range of p!-1 values, and the ability to formbiofllms on many types of surfaces (3, 13, 21, 28). TheCenters for Disease Control and Prevention estimates that2,500 cases of human listeriosis occur annually in the Unit-ed States with a case fatality rate of 20% that leads to anestimated 500 deaths per year and a projected cost of $233million (9, 12, 17, 27, 35). L. monocvtogenes has been as-sociated with cooked ready-to-eat (RTE) poultry products(3, 7, 11). The U.S. Department of Agriculture Food Safetyand Inspection Service (USDA FSIS) has issued a zero tol-erance policy for L. nionocvtogenes in RTE foods (7, 16,22, 30). Therefore, it is important to control L. monocvro-genes contamination in RTE meat and poultry products.Sporadic cases and incidences of several foodborne illnessoutbreaks associated with RTE meats have prompted reg-ulatory agencies to impose an interim final rule for con-

Author for correspondence. Tel: 479-575-2424; Fax: 479-575-7139:E-mail: yanhinti@uark.edu.

trolling L. monoc'vtogenes in RTE poultry and meat prod-ucts (15, 40).

The use of heat to achieve a specific lethality is one ofthe important critical control factors used to assure the micro-bial safety of processed food and plays a major role in ac-complishing the goal of preventing future outbreaks associatedwith RTE meat and poultry products (6, 11, 15, 35). Inade-quate cooking of poultry products has been one of the majorcauses of foodhorne disease outbreaks (3. The market forRTE poultry products has experienced tremendous growth inrecent years, with a wide range of products available to con-sumers. The increasing number of RTE products in the mar-ketplace has led to increased concerns about controlling path-ogens. particularly L. inonocyrogenes, and an emphasis hasbeen placed on thorough cooking of poultry products (7, 10,11, 19, 30, 33, 34). In the commercial thermal processing ofpoultry products. air-steam impingement cooking is widelyused. and a more than 2 billion pounds (0.9 billion kilograms)of poultry products are processed with this technique annuallyin the United States (30).

Studies on thermal inactivation of bacteria in a varietyof substrates and testing systems. including nutrient medi-um broths, meat slurries, and ground meat and poultry sam-ples, have been conducted (1, 5, 6, 11, 15, 16, 20, 21, 30,31, 33). The results from such studies are limited to thespecific medium and conditions used and are difficult toextrapolate to commercial operations in which real products

608PRADHAN El AL. J. Food Prot.. Vol. 70. No. 3

are processed under a completely different set of conditionssuch as air-steam impingement convection cooking. Somestudies have been conducted on thermal inactivation ofpathogens in ground meat and poultry products (30-33) andin whole-muscle poultry products (29) processed in air con-vection cooking systems; however, no information is avail-able on thermal lethality of pathogens in poultry productswith naturally irregular shapes, such as chicken breasts andchicken wings, processed under cooking conditions similarto those found in commercial processing plants.

Given the impracticality of challenge studies under in-dustrial conditions, predictive models become important foranalyzing processing schedules in commercial operations toensure that the processed meat or poultry products meet path-ogen lethality requirements. Predictive modeling providesbenefits to the food industry as a faster and more efficientproblem-solving approach and is an important link in quan-titative microbial risk assessment to estimate how changes inunit operations are likely to affect the overall safety of a foodproduct (2. 37, 41). Several reports of thermal processing in-activation models for different products under various cookingconditions have been published, but these studies are limitedby regression techniques (1. 5, 15, 16, 20, 21, 29-31, 33-35),i.e., they can be applied only to processing conditions andproducts within the tested conditions.

Cooking processes involve simultaneous heat and masstransfer and microbial destruction. In a few available stud-ies, heat and mass transfer models with or without thermalinactivation models have been considered for differentpoultry and meat products (4, 24-26, 42). Marks et al. (25,26) proposed a heat and mass transfer model coupled withpathogen kinetics for prediction of thermal lethality of Sal-monella in poultry patties and presented their preliminaryresults. Chen et al. (4) described coupled heat and masstransfer models for thermal processing of chicken patties ina small convection oven. Mallikarjunan et al. (24) devel-oped mathematical models to calculate heat and mass trans-fer and microbial kinetics of Salmonella Typhimurium dur-ing microwave cooking of chicken breasts. Watkins andMarks (42) developed models for combined heat and masstransfer and inactivation of Salmonella in ground meat pat-ties processed by moist air impingement cooking. Conse-quently, there still remains a need for models integratingpathogen inactivation kinetics and heat and mass transferto simultaneously predict the temperature, water content.yield, and pathogen destruction in naturally occurring prod-ucts that are processed in commercial cooking systems.

The methods of quantitative microbiology could be ex-tended by coupling pathogen inactivation models with pro-cess (heat and mass transfer) models to evaluate differentprocessing conditions in real poultry products in commer-cial cooking and to develop a user-friendly interface thatcould be used as a tool in desi gning and controlling cookingprocesses. The objectives of this study were (i) to evaluatethe thermal processing conditions during convection cook-ing of poultry products with naturally irregular shapes, suchas chicken breasts, in a pilot-plant scale air-steam impinge-ment oven and (ii) to develop a predictive model by inte-grating a thermal inactivation kinetics model with heat and

mass transfer models to simultaneously predict producttemperature, product water content, product yield, and path-ogen inactivation during air-steam impingement oven cooking.

MATERIALS AND METHODS

Products. Boneless skinless chicken breasts used in thisstudy were obtained from a commercial processing plant (TysonFoods Inc.. Springdale, Ark.). The chicken breast samples werekept for up to 4 months at —20°C until used in tests. Before eachtest, samples were thawed overnight in the cooler room at 4°C.Thawed samples were weighed, and each critical dimension(length. width, and thickness) was measured. Before the test, ran-dom samples were screened for the presence of naturally occurringListeria, which were undetectable in all of the tested samples.

Bacterial strain and inoculation. Although it would be ad-vantageous to evaluate L. nlonocvtogene.s in different thermaltreatments, in reality it is generally undesirable to risk workingwith this pathogen in a pilot food processing or cooking facility.In this study. L. innocua was used as a surrogate microorganism;this bacterium is nonpathogenic and has a higher heat resistancethan does L. monocvtogenes (15, 18. 31, 34). Frozen stocks of L.wnocua ATCC 33090 (USDA Agriculture Research Service[ARS1, Poultry Production and Product Safety Research Unit,Fayetteville. Ark.) were maintained in brain heart infusion (Bi-Il;Remel Inc., Lenexa, Kans.) broth with 12% glycerol at —80°C.A 24-h culture incubated at 37°C conferred the most heat resis-tance for L. innocua (31. 33); therefore, for each trial a 24-h cul-ture was prepared at 37°C in BHI broth, and the cell count for L.jnnocua was approximately 10 8 to l0 CFU/ml.

For inoculated samples, chicken breasts were injectedthroughout the muscle tissue at different locations at a ratio of IMI of culture per 100 g of sample. thus obtaining an initial bac-terial concentration of 106 to 10 CFU/g. The injection was doneperpendicular to the sample surface on both sides from top andbottom. Care was taken to ensure that the injection needle reachedthe center of the sample. This procedure was performed to sim-ulate a worst-case scenario where an injection method would beused to marinate the muscle meat and could result in contami-nation of the internal meat tissues. Inoculated samples were keptat 4°C for 30 rnin to allow excess fluid to drip off, then the samplesurface was washed with 50 ml of sterile 0.1% buffered peptonewater (BPW; Remel) to remove loosely attached cells. For eachtrial, one of the inoculated samples was not cooked and was usedto determine the initial bacterial concentration. This method ofinoculation at different locations throughout the chicken breastfollowed by holding for 30 min at 4°C ensured uniform distri-bution of L. innocua in the sample. To check the uniformity ofinoculation, five randomly selected inoculated samples were test-ed. The concentration of L. iiznocua in these tested samples wasnearly the same.

Thermal processing. In this study, a pilot-scale air-steamimpingement oven (lab model 102, Stein Inc.. Sandusky, Ohio)was used to cook the chicken breasts. The samples were movedthrough the equilibrium chamber before entering the cookingchamber of the oven. The inside dimensions of the cooking cham-ber were 1.5 by 1.5 m. Chicken breasts were cooked in the cook-ing chamber under predetermined testing conditions. The convey-or belt was 6 m long and 45 cm wide. Air and steam flowedthrough nozzles (612 distribution holes of 12.7 mm diameter) todistribute moist air from top and bottom consistently over thesamples on the conveyor belt. Nozzles were evenly distributedabove and below the belt on five distribution boxes.

IkII)I( I I\I \1(II)li 10k II/I lII\ 101 IIk\ 1111 k\t.\L I'korI-.,'l\(;

Chicken samples were processed at oven air temperatures of177 and 200°C for cooking times of 2 to 10 mm. The cookingtime was chosen based on preliminary tests to obtain a certainpathogen lethality. The oven was maintained at an air humidityof 70 to 75% (percentage moisture to volume) as determined witha humidity probe (Humitrol Model II. Stein) and an air velocityof I m/s at the exit of nozzles. Steam was introduced into bothequilibrium and cooking chambers at a flow rate of 38.6 kg/h. Ineach trial, eight chicken breast samples (five inoculated replicates,two uninoculated samples for temperature, and one uninoculatedsample for water content measurement) were cooked. After cook-ing, each sample was immediately put into a sterile stomacher hag(7.5 by 12 in. [19.0 by 30.0 cml: Nasco Whirlpak. Fort Atkinson,Wis.) and placed in an ice water bath at 0°C to cool.

During cooking and cooling, thermocouples (type J. OmegaEngineers. Stamford. Conn.) were placed at the top and bottomsurfaces and at the center of two uninoculated samples. whichwere processed with the same treatment conditions as inoculatedsamples to monitor the temperatures via a data acquisition system(model 34970. Hewlett Packard, Loveland. Cob.). Another typeJ thermocouple was used to monitor the oven air temperature dur-ing cooking and the ice water bath temperature during cooling viathe same data acquisition system. The location for the center tem-perature measurement in a chicken breast was determined in thepreliminary studies. Two chicken breasts were weighed. and theirdimensions were measured, including the thickness at the thickestportion. Thermocouples were placed at different locations near thecenter of the thickest portion, and the samples were processedunder the tested oven conditions. The measured internal temper-atures in the samples were compared with the predicted values todetermine the location with the slowest heating rate (the coldestspot). As a result, the center of the thickest portion in the chickenbreast was used for the center temperature measurement.

Water content and product yield. Water loss was calculatedfrom the water content before and after cooking of the uninocu-lated sample during each trial using oven drying method at 110°Cfor 24 h (32, 33). Water loss was described as the change in watercontent expressed on a percentage wet basis. Product yield wascalculated by weighing the sample before and after thermal pro-cessing at ambient temperature, and the percentage yield was cal-culated: product yield % = (product mass after thermal process-ing/product mass before thermal processing) X 100.

Microbial enumeration. Each cooked inoculated sample inthe sterile Stomacher bag was mixed with 50 nil of sterile 1%BPW and stomached for 4 min with a stomacher (model 400.Seward Medical Ltd., London, UK). The stomached sample wasserially diluted (1:10) and plated on modified Oxford agar (MOX)plates in triplicate for each dilution. The plates were incubated at37°C. and colonies were counted after 24 to 48 h (detection limitof 14 CFU/g). The plates were returned to the incubator and re-counted the next day until viable counts ceased to increase. Theplates were kept at 37°C in the incubator for up to 4 days. Anenrichment procedure also was used to check for survivors insamples where Li.cteria cells were not detected. The enrichmentprocedure was conducted by mixing the entire sample in the stom-acher bag with 300 ml of sterile BHI solution and incubating at37°C for 24 h (29, 31). The enriched solution was plated on MOXplates, which were incubated at 37°C for 24 to 72 h and checkedfor survivors.

Thermal inactivation kinetics model. In predictive micro-biology, a primary model describes the change in microbial num-bers over time, the secondary model describes how the primary

model parameters change with environmental factors such as pHand temperature. and the tertiary model joins the above modelswith a user-friendly interface to give a modeling system such asthe Pathogen Modeling Program produced by the USDA-ARSEastern Regional Research Center and the Food Micro Model, acommercial package supported by the Ministry of Agriculture.Fisheries, and Food and subsequently the Food Standards Agencyin the United Kingdom (22, 38, 41).

Microbial inactivation at high temperatures (exhibiting loglinear behavior) can generally be expressed as a first order kineticequation (24. 34, 41, 42):

c/N

(It

where N is the number of viable organism at a given time, I istime in minutes, and r is the reaction rate constant per minute:at r = 0. N = No and is the initial number of bacteria. The r, isusually a function of temperature and the type of microorganism,and the temperature dependence of a reaction is commonly ex-pressed by the Arrhenius equation (8, 34, 38):

r,. = ae uJRl (2)

where o is an empirical constant (Arrhenius constant). E is theactivation energy (Joules per mol), R is the universal gas constant(Joules per mol per degree Kelvin). and 1' is absolute temperaturein degree Kelvin. Kinetic parameters for the air-steam impinge-ment cooking are unavailable in the literature, and determinationof these parameters was beyond the scope of this study. The Ar-rhenius constant and activation energy values used in this studywere obtained from Murphy et al. (34). Substituting equation 2into equation I and integrating from t = 0 to t, the final form ofthe thermal inactivation model used in this study can be writtenas follows:

In() = 0 f e 1dr (3)

This type of inactivation model can be applied to real thermalprocess by dividing the process into a series of small time stepsand calculating lethality by stepping through these time steps andrecalculating survivors after each interval. Murphy et al. (34) con-ducted thermal inactivation studies of Salmonella and Listeria inchicken breast patties cooked in a lab-scale air convection ovenat five different air temperatures from 163 to 218'C and for com-parison used the kinetic parameters from water bath cooking avail-able in the literature. At a final center temperature of patties lowerthan 65°C, bacterial survival was not substantially different fromthat obtained in the air convection oven study and that found inthe literature. However, when the final center temperature wasabove 65°C. higher survival was obtained in air convection ovenstudy than that in the literature, which indicated that use of kineticparameters from the literature would overpredict the thermal le-thality of poultry products processed in an air convection oven (34).

Heat and mass transfer models with numerical solutiontechniques. During convection oven cooking, heat is transferredmainly by convection from heated air to the product surface andby conduction within the product. Initial temperature and waterdistribution in the product were assumed to be uniform. Assumingthat water diffuses to the outer boundary of the product in liquidform and that evaporation takes place both on the surface andinside the product, the heat and mass transfer equations of un-

WE-

I

010PRADHAN ET AL.

TABLE I. Ph ysical, thermal, and transport properties used inthis study fbr finite element simulation

Property Value

Ps 1,070 kg/M33,810 J/(kg °C)1,880 J/(kg °C)

k 0.491 W/(m °C)75 W/(rn2 °C)

h 1.67 X 10 11 kg/(m2 s)1) 0.39 X 10 m2/s

Qf 2.466 x 106 i/kg

steady-state modified Luikov's equations can he expressed in acylindrical coordinate s ystem as follows (14, 43:

hM/a2M\(I i)M'\/h2M\(4)—=Df---I+DI---J+Dj----I)(t\ tr2 J\r hr J\ dz' /

hT/21'\/i aT\/h2T\hMI(5)ht\hr2Jr hr/\hz-Ji)t I + M

The corresponding boundary and initial conditions for governingheat and mass transfer equations 4 and 5 during thermal process-ing are

= h,,,(M - Me) (6)

hMV I= h,(T - 1,) ++- T)]-- I + M (7)

t = 0, M=M0, T=T0 (8)

where A is the product surface area (square meters), c 5 and c"arethe specific heat of the product and water vapor, respectively(Joules per kilogram per degree C). 1) is a diffusion coefficient(square meters per second), h,, is the convective mass transfercoefficient (meters per second), h, is the convective heat transfercoefficient (Watts per square meter per degree C), k is the productthermal conductivity (Watts per meter per degree C). M is theproduct moisture content (dry basis). M is the product equilib-rium moisture content (dry basis), n is the flux in a normal direc-tion, Q, 5 is the latent heat of vaporization (Joules per kilogram),r and z are the axes in a cylindrical coordinate system, I is theprocessing time (minutes), T is the product temperature (degreesQ. 7, is the heated air temperature (degrees C), V is the productvolume (cubic meters), and p is the product density (kilogramsper cubic meter). The physical, thermal, and transport propertiesof products and processing conditions used in this study wereobtained from Chen et al. (4), Mallikarjunan et al. (24), and Mur-phy et at. (32) and are listed in Table I. The main computationaltechnique used in this study to solve the governing partial differ-ential equations was the finite element method, which divides anellipsoidal chicken breast sample into a large number of smalltriangular elements across a section of the sample and describesthe variation of a field within an element using interpolating poly-nomials. The samples were assumed to he ellipsoidal, because thetested samples in this study can be approximated and character-ized by a symmetrical ellipsoid, which can ease finite elementcomputation. The finite element method described by Jia et al.(14) and Yang et al. (43) was applied to solve these equations. Asimulation program using the finite element method was devel-oped using Matlab (The Math Works, Inc., Natick, Mass.) con-

J. Food Prot.. Vol. 70, No. 3

Physical, thermal, and Initial contaminationtransport properties level

mass transferHeat and Pathogen

kineticsdistribtitionmodels model

Temperature, water Bacterial survivalcontent, product yield

FIGURE 1. Block diagram of the integration of the pathogen ki-netics model with heat and mass transfrr models.

sisting of the partial differential equation toolbox, graphical li-brary, and math library for performing finite clement computationand conducting other computing and programming tasks. The sig-nificant advantage of the finite element method is that it can han-dle irregular geometry and variable spacing of the nodes routinelyand the ease with which nonhomogeneous and anisotropic mate-rials may he handled.

Integration of the pathogen kinetics model with heat andmass transfer models. Figure I shows the block diagrani forintegration of the pathogen kinetics model with the heat and masstransfer models to predict temperature, water content, productyield, and bacterial survival under specified processing conditionsin the air-steam impingement oven. Inputs such as physical, ther-mal. and transport properties for the heat and mass transfer modeland initial contamination level in samples for the pathogen inac-tivation model were used during model simulation. The heated airconditions, product initial conditions, and processing end condi-tion were specified before running the simulation. During con-vection cooking in the oven, sample temperature changed withprocessing time. Temperature distribution obtained from the heatand mass transfer models were used as input values in the path-ogen kinetics model to calculate the microbial inactivation. TheIntegration of both models finally returned output values relatedto temperature and water content along with bacterial inactivation.The calculation of bacterial inactivation involved the division oftotal processing time into a series of small time steps. Based onthe temperature distribution, inactivation was calculated by steppingthrough these time steps. calculating the inactivation after eachinterval, and aggregating for the total inactivation during a process.

Development of a user-friendly interface ThermalSimnu.Mathematical models should be user friendly and computationallymanageable to ensure their practical applicability. In this study,an interface called TherinalSimu was developed using Matlab un-der the Microsoft Windows XP platform (Microsoft, Redmond,Wash.), integrating the thermal inactivation kinetics and heat andmass transfer models to simulate thermal processing. The partialdifferential equation toolbox was used to automatically generateoptimized finite element meshes. The math library provided a use-ful tool for computational work to solve the partial differentialequations and other mathematical problems. The graphical librarywas used to generate two-dimensional—three-dimensional fullycolored graphical outputs to enhance the data presentation and thevisual effects for the end users. ThermalSinzu is mainly composedof three graphical user interfaces: a main window; a simulationinput window where the user can define product shape and size,

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J. Food ['rot.. Vol. 70, No. 3 PREDICTIVE MODEL FOR LISILRIA IN POULTRY THERMAL PROCESSINGoil

product and processing conditions, and initial concentration ofbacteria present in product; and an output window where the userwill get the predicted temperature, water content, product yield,and thermal inactivation of the pathogen. The animation featurein the output graphical user interface can animate the temperatureand water content changes inside the product during thermal pro-cessing.

Data analysis. All pathogen survivor data were subjected tolog transformation and expressed in log units before analyses wereconducted. Bacterial survivor data were presented as the meanthe standard deviation. The standard deviations of all replicateswere plotted as error bars at data points (iMP software. SAS In-stitute Inc.. Cary, N.C.). The relative error (RE) of each predictionwas calculated using equation 9 to evaluate the performance ofthe model (23. 36):

(X,X)

RE = ' (9)xp

where X1 , is the predicted value and X,, is the observed value. Thedifference between the observed and predicted values for the end-point center temperature and the difference between the predictedand observed values for water content and product yield were usedfor calculating the RE. The average or median RE (MRE) wascalculated by taking the average RE in prediction cases for tem-perature, water content. and product yield. The errors were cal-culated based or) endpoint values.

The standard error of prediction (SEP) or root mean squareerror for the prediction of endpoint center temperature was cal-culated using equation 10 (4. 42):

/(T - T )2SEP= (10)

\n—I

where T, and T, are the predicted and measured endpoint centertemperatures and n is the number of observations. The maximumtemperature measured in the experimental chicken breast duringcooking was designated as the endpoint temperature.

RESULTS AND DISCUSSION

Product temperature. Temperature histories were re-corded during cooking of chicken breasts at oven air tem-peratures of 177 and 200°C for cooking times of 2 to 10min and further cooling in an ice water bath at 0°C. Figure2 shows a typical temperature profile for the surface andinternal temperature in a chicken breast during cooking andcooling. Surface temperatures were monitored on the topand bottom of each breast. The same thermocouple wasused to measure the air temperature during cooking and theice water temperature during cooling. The air temperaturein Figure 2 is the temperature in the air environment sur-rounding the chicken breasts on the conveyor belts in theoven cooking chamber. The air temperature quickly reachedthe oven temperature when the chicken samples entered theoven. The surface temperatures of the chicken breast im-mediately increased but the center temperature increasedmore slowly. The surface temperature of the chicken breastwas always higher than the center temperature throughoutthe cooking process (Fig. 2). Therefore, a substantial tem-perature gradient occurred between the surface and centerof the chicken breast.

0102030405060

Time (mm)

FIGURE 2. Typical temperature protile of a chicken breast sam-ple thei'mall'processed for 9 loin in an air-steam impingementoven at an air temperature of 200°C and air humidit y of 72%,followed bcoo/mg at 0°C. (a) Top surf ace temperature.' (h) bot-tom .rurftmce temperature: (c) center temperature,' (d) air temper-ature.

This temperature gradient is the important driving forcefor change in the product temperature during cooking in theoven. The difference between the surface and center tem-peratures varied with cooking time and operation condition.The processed chicken breasts were taken out of the ovenafter cooking for predetermined times and placed in an icewater bath to cool. After cooking. the center or internaltemperature initially increased slightly and then graduallydecreased, whereas the surface temperature started to de-crease immediately during cooling at a faster decrease ratethan that of the internal temperature. There was a lag timeof 30 to 60 s (depending on treatment conditions) beforethe product internal temperature started to decrease. A sim-ilar lag period at the beginning of the cooling process afterheat treatment was also observed for chicken breast pattiesthat were cooked via an air-steam impingement oven fol-lowed by cooling in an ice water bath (31, 33). The presentstudy was focused on pathogen thermal lethality duringcooking. The short lag period observed before the centertemperature decreased after exiting the oven was not in-cluded in the model, thereby the predictions of pathogenlethality from the model would be conservative.

Application of thermal processes for food products arebased on temperature histories of the location within theproduct where the slowest heating occurs. In commercialfood processing applications, process lethality is based onthe center temperature of the product and is an importantfactor in determining thermal processing conditions. Thetemperature measured at the center of the sample chickenbreasts was compared with the model prediction (Fig. 3).The measured endpoint temperatures at the center of thechicken breasts after 10 min of cooking in the oven were79 and 82.6°C at air temperatures of 177 and 200°C, re-spectively. According to the USDA FSIS guideline. RTEcooked poultry products should reach an internal temper-ature of at least 71 . 1°C before being removed from thecooking medium; this internal temperature is required forpoultry to be considered fully cooked (32. 39). Therefore,the measured center temperatures of chicken breasts cookedat both air temperatures after 10 min met the minimum

80

0

600CL

500

400C)

30

•Measured (1770)Simulated (177C)

£ Measured (200C)Simulated (2000)

612PRADHAN ET AL.

90

20 I024681012

Cooking time (mm)

FIGURE 3. Comparison of simulated (curves) and measured(s ymbols) endpoint center temperatures of chicken breast samplesthermal/v processed in the air-steani impingement oven at 177and 200 °C for 2 to 10 min.

J. Food Prot., Vol. 70, No. 3

12

10 -1

- 8J('IU,I2 6-1

4 77C

2 -*- 2000

0-l---02468

1012

Cooking time (mm)

(a)

ppr-

requirement temperature to be labeled as a fully cookedproduct. At the beginning of the cooking process, temper-ature increased at a faster rate and eventually decreased.The predicted center temperature at both oven air temper-atures was close to the measured value toward the end ofcooking processes.

The SEPs for the endpoint center temperature were 2.8and 3.0°C for air temperatures of 177 and 200°C, respec-tively, indicating that simulated results agreed reasonablywell with the measured values. The plot of measured andpredicted center temperatures (Fig. 3) illustrates that themodel underpredicted the endpoint temperature. The RE ofthe model prediction for the center temperature of chickenbreasts after 2 to 10 min of cooking ranged from 2.1 to10% and from 2.4 to 9.7% at 177 and 200°C, respectively.The MREs of center temperature prediction were 5.2 and5% at 177 and 200°C, respectively, when chicken breastswere processed for cooking times of 2 to 10 mm. Heattransfer in chicken breasts is heterogeneous, and althoughall types of heat transfer are dependent on a temperaturegradient, the thermal process is complex in chicken breastsduring heating because of many physical and chemicalchanges that occur in the chicken breast meat (23).

Water content and product yield. The reduction inwater content of chicken breasts cooked for 2 to 10 min atair temperatures of 177 and 200°C is shown in Figure 4a.The loss of water content (change in water content, per-centage wet basis) increased as the cooking time increasedfor both temperatures. The loss of water content in thechicken breasts cooked for 2 to 10 min ranged from 2 to8.8% at 177°C and from 2.7 to 10% at 200°C. As the cook-ing time increased, the temperature of the products in-creased, thereby enhancing the water loss. Water loss of apoultry product is dependent on the mass transfer processduring thermal treatment, and mass transfer due to heattreatment is quite common in food processing. Similar tothe temperature gradient that affected temperature change,the water gradient played an important role in the change

100

90V0

80UVo 700.

60024681012

Cooking time (mm)

(h)

FIGURE 4. Measurements from chicken breast samples cooked inthe air-steam impingement oven at 177 and 200°C for 2 to 10nun. (a) Water content loss (change in water content, expressedas percentage wet basis); (b) product yield.

of water content in chicken breast samples. Murphy et al.(32) indicated that for the heat-treated chicken breast pat-ties, higher porosity was obtained at a higher product tem-perature. Porosity of a product can affect mass transfer dur-ing thermal processing, and increased porosity increaseswater loss from the product (32). The RE of the model forthe prediction of water content of chicken breasts after 2 to10 min of cooking at 177°C ranged from —1.8 to 7.5%,with an MRE of 2.5%. For chicken breasts processed at200°C for the same cooking time, the RE of the modelprediction for water content ranged from —1.1 to 9.4%,with an MRE of 3.7%.

Figure 4b shows the product yield of chicken breastscooked at air temperatures of 177 and 200°C for 2 to 10mm, which ranged from 93 to 74% and from 90 to 70% at177 and 200°C. respectively. Product yield is related to thewater loss during cooking. Increasing the product internaltemperature reduces the product yield, consistent with thewater loss in the product. The RE of the model predictionfor product yield at 2 to 10 min of cooking ranged from—4.3 to 16.1% and —2.6 to 20.7% at 177 and 200°C, re-spectively. The MREs of the product yield prediction were

J. Food Prot.. Vol. 70. No. 3

PREDICTIVE MODEL FOR LISTERJ.4 IN POULTRY THERMAL PROCESSING613

atU-00,0

4 Bacterial presence or absence---------------------------------------------Bacterial count

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FIGURE 5. Survival of Listeria innocua in chicken breast samplescooked fr 2 to 10 miii in the air-stealn impingement oven at airtemperatures o/ (a) 177°C and (h) 200°C Error bars for bacterialcounts after 2 to 5 mm 0! cooking represent ± standard deviationfrom the experimental data. Results were recorded as positive orno growth after cooking for 6 inin or longer, indicating the pres-ence or absence, respectivel y, of L. innocua in enriched samples.

5.4 and 8.4% at 177 and 200°C, respectively, when chickenbreasts were cooked for 2 to 10 mm. Because the productwater content and product yield were obtained by evaluat-ing the mass changes before and after cooking, the irregularpatterns in Figure 4 might be due to drip loss and meat losson the conveyor belt. To increase the model performance,exact physical, thermal, and transfer properties for the samecooking conditions using the same products should be de-termined and used during simulation of the model.

Bacterial survival. The survival of L. innocua incooked chicken breasts processed at 177 and 200°C for 2to 10 min is shown in Figure 5. The observed reductionsin bacteria in chicken breasts cooked for 2 to 5 min were0.3 to 1.4 log CFUIg at 177°C (Fig. 5a) and 0.8 to 1.8 logCFU/g at and 200°C (Fig. 5b). The maximum standard de-viations were 0.3 and 0.6 log CFU/g at 177 and 200°C,respectively. The predicted reduction in bacteria for a cook-ing time of 2 to 5 min ranged from no change to 1.0 logCFU/g and no change to 1.5 log CFU/g at 177 and 200°C,

respectively. The differences between the observed and pre-dicted values may be due to the use of kinetic parametersduring water bath cooking of products other than chickenbreasts and temperature histories used to predict the sur-vival of bacteria. There was a lag period associated withmodel predictions at the initial stage, and this lag phaseresulted from the heating lag period in the temperature andtime to kill bacteria. Destruction of microorganisms by heatis time and temperature dependent.

Direct plating of cooked chicken breast samples re-sulted in no detectable growth of L. innocua for cookingtimes of 6 min or longer at both oven air temperatures.Therefore, to determine the presence or absence of bacteriain these cooked samples, an enrichment procedure was fol-lowed in which whole samples were incubated and platedfor microbial survivors. The number of chicken breast sam-ples positive for L. innocua after cooking times of 6 to 9min was reduced from five of five samples to two of fivesamples at 177°C and from three of five samples to one offive samples at 200°C. If any of the five cooked sampleswas positive for L. innocua, the result was reported as pos-itive growth or presence of L. innocua at that cooking time(Fig. 5). For a cooking time of 10 min at both temperatures,no survivors were detected in any of the cooked chickenbreast after enrichment, verifying that cooking for 10 mmat both temperatures eliminated L. innocua by a reductionof 106 CFU/g (Fig. 5). The endpoint product center tem-perature obtained after cooking of chicken breasts for 10min at both temperatures also resulted in fully cooked meat.

Description of the user-friendly interface. The user-friendly component is composed of three graphical user in-terfaces: finite element analysis programs for meat thermalprocessing (main window), simulation inputs for meat ther-mal processing, and simulation results for meat thermalprocessing. On the input window, product type, productshape, product size, heated air conditions, product initialconditions, initial number of microorganisms in the prod-uct, and processing end conditions can be selected, and ap-propriate values can be entered to run the simulation. Underproduct type, a list of different product types was providedbecause this simulation program was developed for usewith all major meat products, including chicken, pork, andbeef. At the current stage, emphasis is being placed onchicken meat, especially chicken breasts. Under productshape, the possible cross-section of a cylinder, rectangle,sphere, or ellipsoid may be chosen. A two-dimensional axi-symmetric finite element grid of a quarter cross-section ofa sample in the cylindrical coordinates was used in the sim-ulation because of the symmetric cross-section of an ellip-soid. For the poultry processing simulation, because the fi-nite element computation was based on two-dimensionalaxisymmetric configuration of a chicken breast, an averageof the thickness and width was considered during simula-tion. The heated air conditions include humidity, tempera-ture, and airflow rate, and the product initial conditions in-cluded initial product water content and initial product tem-perature. Processing end conditions are the total processingduration or product center temperature at the end and must

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614PRADHAN ET AL. Food Prot., Vol. 70, No. 3

• 1 Simulation Results For Meat Thermal Processing . • T •________

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FIGURE 6. Model output window during simulation of thermal processing for poultry products.

be specified to focus the processing time or product centertemperature.

The output window during simulation at a processingcondition is shown in Figure 6. The graphic in the resultwindow shows the temperature contour, which gives thetemperature distributions in a quarter cross-section of achicken breast during simulation. In this result window,there are several items in the lower right scroll box that canbe selected to observe the curves or graphics, such as thefinite element grid, mean temperature of the product, meanwater content of the product, surviving microorganisms,product yield, temperature and water content gradient, andthe comparison assessment of product yield, center tem-perature, and surviving microorganisms to optimize theprocess. When a selection is made in the selection box, thegraphics or curves in the window change to comply withthe selection. A summary of the simulation results is alsogiven below the graphic window at the left side.

To ensure the safety of fully cooked meat and poultryproducts, the performance standards require processors toevaluate the efficacy of their cooking processes. The appli-cation of predictive models to analyze temperature, watercontent, and pathogen lethality depends on thermophysical,transport, and kinetic parameters, which should relate to theproduct and process to be evaluated. The developed modelscan be used to predict temperature, water content, productyield, and thermal lethality of Listeria spp. in chickenbreasts cooked via air-steam impingement oven. The per-formance of the models can be improved with the incor-poration of dynamic process parameters similar to the pro-cessing conditions of related products. The methodology in

this study can be used by processors for evaluating thermalprocessing schedules during cooking of poultry productssuch as chicken breasts that are processed in commercialcooking systems via an air-steam impingement oven.

ACKNOWLEDGMENTS

This research was supported in part by USDA ARS—National Alli-ance for Food Safety and Security project 1935-42000-041-06S and theFood Safety Consortium. The authors thank Dr. Helen Brown (TysonFoods, Inc.) for poultry samples. Dr. Canchun Jia for modeling work,Rodney Wolfe for pilot plant facilities, and Betty Swem and Lisa Cooneyfor their help in microbiological tests.

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