thermophysical properties of processed meat and poultry products

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Journal of Food Engineering 88 (2008) 315–322

Thermophysical properties of processed meat and poultry products

Michele Marcotte a,*, Ali R. Taherian a, Yousef Karimi a,b

a Food Research and Development Centre, Agriculture and Agri-Food Canada, 3600 Casavant Blvd West. St. Hyacinthe, Quebec, Canada J2S 8E3b Department of Food Science and Agricultural Chemistry, McGill University, Macdonald Campus, 21,111 Lakeshore Road,

Ste Anne de Bellevue, Quebec, Canada H9X 3V9

Received 6 September 2007; received in revised form 7 February 2008; accepted 13 February 2008Available online 10 March 2008

Abstract

Thermophysical properties of various meat and poultry emulsions were evaluated at four temperatures (20, 40, 60 and 80 �C).Thermal conductivities (0.26–0.48 W m�1 K�1) increased linearly with temperature between 20 and 60 �C. Between 60 and 80 �C, itremained constant for most products except bologna. Curves for thermal conductivity as a function of temperature could be roughlygrouped into two different categories: products containing meat particles and those containing meat emulsions. The application of var-ious models was investigated for thermal conductivity prediction. It was found that a three phase structural based Kirscher model hadthe potential for predicting thermal conductivities with acceptable accuracy. Densities decreased slightly as a function of temperaturefrom 20 to 40 �C. A transition phase was observed from 40 to 60 �C, which was followed by a decrease from 60 to 80 �C. There wasa decrease of about 50 kg m�3 between the density of a raw product at room temperature (at maximum 1070 kg m�3) and the productheated to 80 �C (at minimum 970 kg m�3), due to the gelation or setting of the structure. After a transition period from 10 to 30 �C,the heat capacity increased linearly from 30 to 80 �C, and ranged from 2850 to 3380 J kg�1 �C�1, respectively. Densities and heat capac-ities were strongly influenced by the carbohydrate content (i.e. as the carbohydrate content increased the density decreased). The saltcontent adversely affected thermal conductivity and thermal diffusivity values. However, these parameters increased with moisturecontent.� 2008 Published by Elsevier Ltd.

Keywords: Meat and poultry emulsions; Meat and poultry products; Thermal conductivity; Thermophysical properties; Modeling; Correlation matrix

1. Introduction

Thermophysical properties (specific heat, thermal con-ductivity, thermal diffusivity and density) of food areimportant parameters in describing various thermalprocesses, optimizing the design and the operation of heat-ing, cooking, freezing and cooling systems (Karunakaret al., 1998). Thermal properties are also essential for themodeling and evaluation of food processing operationsinvolving heat transfer, especially when energy costs, foodquality and safety are the main considerations. These prop-erties are especially important to ensure food safety

0260-8774/$ - see front matter � 2008 Published by Elsevier Ltd.

doi:10.1016/j.jfoodeng.2008.02.016

* Corresponding author. Tel.: +1 450 768 3295; fax: +1 450 773 2888.E-mail address: [email protected] (M. Marcotte).

(Unklesbay et al., 1999). As an example, the temperatureat the core of a typical sausage must be above a certainlevel (72 �C) by the end of heating and below certaintemperature (15 �C) at the end of cooling in order toachieve microbiological stability of the product (Akterian,1997). Therefore, there is a need to evaluate the heatingcharacteristics of the products, known as thermophysicalproperties.

There are many methods available to measure thermo-physical properties such as guarded hot plate method,differential scanning calorimeter (DSC) attachmentmethod, capped column test device, line heat sourceprobe method, temperature history and transient hotstrip (THS) method (Baik et al., 2001). While the firstthree mentioned methods are applied at steady state,the last two methods are applied for transient conditions.

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Nomenclature

Cp specific heat at constant pressure (kJ m�3

oC�1)E predicted valuef a distribution factorI current (mA)k thermal conductivity (W m�1 K�1)m slope of the linear part of the temperature vs. ln

(time) graphN number of samplesO experimental valueQ heat flux (W m�1)Rth probe resistance (m�1)r2 coefficient of determination

SD Standard errorT temperature

Greek symbols

a thermal diffusivity (m2 s�1)U volume fractionq density (kg m�3)

Subscripts

a airi component i

s solidw water

316 M. Marcotte et al. / Journal of Food Engineering 88 (2008) 315–322

Thermal conductivity is highly temperature dependentespecially over a temperature range where a phase changeoccurs. According to Karunakar et al. (1998), at a lowtemperature range (0–40 �C), the thermal conductivitydoes not exhibit very significant difference between vari-ous temperatures. At high temperatures (>50 �C), itincreases gradually as the temperature increases (Panand Singh, 2001). Both thermal conductivity and heatcapacity are known to increase with moisture contentincrease (Shmalko et al., 1996). Water content will affectthe heat capacity more than other components, the lowerheat capacity values generally occur with the lower mois-ture content values (Unklesbay et al., 1999). Thermalconductivity and density of foods vary with temperatureduring thermal processing due to the changes in textureand/or composition (Karunakar et al., 1998). A decreasein density values will become important due to its effecton other thermal properties (Mohsenin, 1980). Mostchanges in meat products occur during heating, shrink-age, tissue hardening, moisture loss, fat loss and discolor-ation, and are caused by the changes in muscle proteindenaturation (Pan and Singh, 2001). All these changesin the meat will affect the thermophysical properties.

Mathematical models can be used to study and under-stand the relationship between thermophysical propertiesof complex food systems and temperature. For the pre-diction of the thermal conductivities, a large number ofmodels have been suggested in the literature (Gonzo,2002; Wang et al., 2006; Carson, 2006), used for compos-ite or heterogeneous materials based on composition.Most of the proposed models are highly material basedand contain material-specific parameters. Although, somemodels are considered to have a more general applicabil-ity, their parameters are still empirically determined.Generic models have been proposed by some scientists(Gonzo, 2002; Wang et al., 2006; Carson, 2006), wherea set of equations, usually based on a conceptual ‘parent’model, are derived. These models are developed so thatthey can account for variations in composition and struc-

ture, even though they often contain empirical parame-ters. The selection of the appropriate model for a givenfood product is not an easy task. As reported by Carsonet al. (2006), there is a huge disagreement between ther-mal conductivity values predicted from various models.It can be concluded that, to-date, no single model or pre-diction procedure has been found with a universal appli-cability. Therefore, it must be evaluated on a case-by casebasis.

In recent decades, there have been many publishedresearch work on experimental values of thermophysicalproperties of foods and mathematical models to representthese data (Polley et al., 1980; Sanz et al., 1987; Lind,1991; Karunakar et al., 1998; Baik et al., 2001; Gonzo,2002; Wang et al., 2006; Carson, 2006). However, there isvery little work done on food emulsions. The overall goalof this study was to measure thermophysical properties ofvarious commercial meat and poultry emulsions as a func-tion of temperature. A more specific objective was also toinvestigate the ability of mathematical models to predictthe thermal conductivity of these emulsions.

2. Materials and methods

2.1. Products

Five types of meat products were used: fine emulsion ofbologna and wieners, coarse emulsion of pepperoni, turkeyemulsion and flaky ham. Turkey emulsions and the flakyham contained muscle parts. Raw products were collectedfrom a typical industrial plant and measurements weremade the day after. The products were kept at 4 �C coldroom until they were analyzed. All experiments were per-formed three times and with three different batches ofproducts. Thermophysical properties of meat and poultryemulsions were gathered at different temperatures fromraw product to cooked product temperature. The composi-tion of meat and poultry emulsions studied is listed inTable 1.

Table 1Composition of various meat and poultry emulsions (%)

Foodemulsion

Moisture Fat Salt Ash Protein Carbohydrate

Bologna 61.59 20.31 2.39 4.22 11.49 2.39Pepperoni 57.26 21.72 2.53 3.48 12.32 5.22Wieners 60.51 20.9 2.43 4.19 12.4 2.00Turkey 74.88 1.67 1.54 6.69 15.46 1.30Ham 72.70 6.35 2.78 7.08 11.62 2.25

M. Marcotte et al. / Journal of Food Engineering 88 (2008) 315–322 317

2.2. Thermal conductivity measurements

Thermal conductivity measurements of various meatsamples were performed using the probe method basedon the line-heat source approach developed by Sweat(1974). In the probe method, there was a heater wire insu-lated over its length and a thermocouple at the center ofthis length. The probe was 38 mm long with an outsidediameter of 0.66 mm. It consisted of a constantan heaterwire and a chromel–constantan thermocouple (type E)(Sweat, 1995). The probe was connected to a power supply(Hewlett-Packard, 6236B) and to a multimeter (TES 2610multimeter) in order to read the current more precisely;the multimeter was set to read in mA DC. The thermocou-ple wires were connected to a data acquisition system (DataShuttle by Strawberry Tree) connected to a computer (Baiket al., 1999). The software ‘‘Workbench for Windows ver-sion 3” was used to convert the analog signal of the ther-mocouple into a digital signal, to set the acquisition rateat 1 reading every 2 s. The probe had a theoretical internalresistance of 226.67 X m�1. It was calibrated with glycerol.Values obtained were within 10% of the literature value of0.284 W m�1 K�1 at 20 �C.

Constant temperature baths set in increments of 20 �Cwere used for the experiments: water baths at 20 �C,40 �C and 60 �C, and an 80 �C oil bath to prevent evapora-tion. A copper cylinder (12.7 cm height and 2.54 cm insidediameter with a maximum wall thickness of 0.159 cm) witha high thermal conductivity was used. Emulsions sampleswere inserted using a syringe, whereas the turkey and theflaky ham were introduced by hand. A rubber cover wasplaced at both ends of the cylinder to insulate them andmaintain heat flow only from the side of the cylinder. Aninfinite cylinder was assumed for thermal conductivity cal-culations. Three cylinders were placed in each of the fourtemperature controlled water bath. When the core temper-ature of the samples reached equilibrium with the waterbath, the top rubber cover was replaced by a thinner oneand the probe was inserted in a small hole made at the cen-ter. The probe was placed at the core of the sample. Thedata acquisition was switched on for 8 s to record the initialtemperature. Then the power was turned on to pass a cur-rent of 200 mA for 2 min of data acquisition before beingstopped. The thermal conductivity was calculated by plot-ting the temperature versus the natural logarithm of thetime and taking the slope of the linear part of this graph.

Having the slope, it was possible to calculate the thermalconductivity using the following equations:

Q ¼ RthI2 ð1Þ

k ¼ Q4pm

ð2Þ

where Q is the heat flux (W m�1), Rth the probe resistance(X m�1), k the thermal conductivity (W m�1 K�1), I thecurrent (mA) and m the slope of the linear part of the tem-perature vs. ln (time) graph.

2.3. Density measurement

Densities were determined from the mass of the samplesinserted in the copper cylinder and the volume of thecylinders, which were pre-determined for six cylindersand the average value was considered. The mass of thesamples was measured at the end of the treatment to verifyif there was any weight change during the treatment. Thedensity was calculated as the ratio of the final mass ofthe sample to its volume.

2.4. Heat capacity

A modulated differential scanning calorimeter (MDSC2910, TA Instruments Inc., New Castle, DE) was used witha nitrogen cooling system. MDSC has the ability for applica-tion of two independent heating rates, isothermal and mod-ulated, which is an advantage over conventional DSC thatcarries only isothermal. This allowed determination of theheat capacity at various temperatures. At the start, the cellconstant of the instrument was evaluated by running anexperiment with Sapphire (Al2O3). The ratio of the experi-mental heat capacity to the theoretical heat capacity of Sap-phire gave a cell constant of 1.935. A minimum mass of 200 gproduct was then homogenized using a ‘‘Polytron” (Poly-tron, PT 10–35 by Kinematica) in order to get a representa-tive sample, especially for coarse emulsion, turkey and ham,and to fill up the experimental container which was small incapacity. A sample of 10–13 mg was placed in the aluminiumpan which was then hermetically sealed using an encapsulat-ing press from TA Instruments Inc. An empty pan that hadbeen previously weight-matched with the sample pan wasalso sealed for reference. The method consisted of equilibrat-ing the sample at 5 �C, starting the modulation for a 60-sec-ond period with an amplitude of ±1 �C, keeping itisothermal for 5 min, and heating the sample at a rate of2.0 �C/min from 5 �C to 90 �C. Helium was used as a purginggas at a flow of 25 ml min�1. The instrument automaticallygave heat capacity curves from 5 to 90 �C.

Furthermore, the heat capacities (Cp) of the emulsionswere related to temperature (T) using following linearregression equation:

Cp ¼ aþ bT ð3Þwhere a is the intercept and b the slope of the regressionline.

Table 3Thermal conductivity of food components as a function of temperature(�40 �C < T < (150 �C)

Component Thermal conductivity (W m�1 K�1)

Protein 1.7880 � 10�1 + 1.1958 � 10�3 T � 2.7178 � 10�6 T 2

Fat 1.8071 � 10�1 � 2.7604 � 10�4 T � 1.7749 � 10�7 T 2

Carbohydrateash

2.0141 � 10�1 + 1.3874 � 10�3 T � 4.3312 � 10�6 T

Ash 3.2962 � 10�1 + 1.4011 � 10�3 T � 2.9069 � 10�6 T 2

Water 5.7109 � 10�1 + 1.7625 � 10�3 T – 6.7036 � 10�6 T 2

Air 2.3820 � 10�2 + 6.75 � 10�5 T

Adapted from Ramaswawy and Marcotte (2006).


2.5. Thermal diffusivity calculation

The thermal diffusivity values were calculated from theexperimental values of density, thermal conductivity andspecific heat using the following equation:

a ¼ kqCp

ð4Þ

where a is the thermal diffusivity (m2 s�1), k the thermalconductivity (W m�1 K�1), q is density (kg m�3), Cp is spe-cific heat at constant pressure (kJ m�3 �C�1).

2.6. Thermal conductivity modeling

A generic three phase model, known as Kirscher’s model(Maroulis et al., 2002; Wang et al., 2006), was selected andused in this study. Kirscher’s model is one of the mostwidely used, and it is basically a combination of the predic-tion of the Series and Parallel models (Carson et al., 2006)containing a weighting parameter (f) (also named distribu-tion factor) which has to be adjusted for different food sys-tems. The model was formulated as follows:

ke ¼1

ð1�fkpþ f

kseÞ

ð5Þ

kp ¼ /sks þ /wkw þ /aka ð6Þ

kse ¼1

/s

ksþ /w

kwþ /a

ka

ð7Þ

where kw, ks, ka are the thermal conductivities of water, sol-ids and air, respectively, f is a distribution factor and Uw,Us, Ua are the volume fractions of water, solids and air,respectively. Thermal conductivities of pure food compo-nents are available in the literature as a function of temper-atures. The volume fractions (Uw, Us, Ua) are dependent onthe material state.

Since the compositions of meat and poultry emulsionswere determined and presented on a weight basis, the vol-ume fraction of each component was evaluated using itsdensity. The densities of various components, as a functionof temperature, are shown in Table 2 (Ramaswamy andMarcotte, 2006). The thermal conductivity of the solidphase was calculated by combining the thermal conductiv-ity of each individual component (i.e., protein, carbohy-drate, ash, and fat) using the procedure suggested by

Table 2Density of food components as a function of temperature(�40 �C < T < (150 �C)

Component Density (kg m�3)

Protein 1329.9–0.5184 TFat 925.59–0.41757 TCarbohydrate 1599.1–0.31046 TAsh 2423.8–0.28063 TWater 997.18 + 3.1439 � 10�3 T – 3.7574 � 10�3 T 2

Air 1.2847–3.2358 � 10�3 T

Adapted from Ramaswamy and Marcotte (2006).

Carson et al. (2006). The component thermal conductivitywas determined using the functions presented in Table 3.Since the Kirscher’s weighing factor was not available formeat and poultry emulsions, it was adjusted to get the bestmodel performance i.e. by matching predicted and experi-mental values.

In order to examine the performance of the appliedmodel, the correlation between thermal conductivity valuespredicted by the model and those calculated from theexperimental data were evaluated using the coefficient ofdetermination (r2), standard errors and standard percent-age errors. The standard error was obtained by the follow-ing equation:

SD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðEi � OiÞ

N

rð8Þ

where Ei is the estimated value of thermal conductivity; Oi

is its observed (experimental) value; and N is the number ofsamples. The relative standard error is defined as the ratioof standard error to the average experimental value of ther-mal conductivity and expressed as a percentage.

3. Results and discussion

3.1. Thermal conductivity

As shown in Fig. 1, the thermal conductivity valuesincreased with temperature for all products. For a finebologna emulsion, the thermal conductivity increased from0.304 ± 0.028 W m�1 K�1 at 22 �C to 0.459 ± 0.100W m�1 K�1 at 80 �C; and for a fine wieners emulsion, thevalues varied from 0.281 ± 0.022 to 0.415 ± 0.066 Wm�1 K�1. For a coarse pepperoni emulsion, the thermalconductivity varied between 0.272 ± 0.019 to 0.402 ±0.044 W m�1 K�1 from 22 to 79 �C. In the case of productscontaining muscle parts, it was found that the thermal con-ductivity of turkey product varied between 0.332 ± 0.040and 0.482 ± 0.086 W m�1 K�1; and that for the ham pro-duct varied from 0.339 ± 0.037 to 0.437 ± 0.058W m�1 K�1. Measurements were performed using samplesfrom three different production batches. Average values arereported since there was a good agreement between theresults of these batches. Two sets of measurements wereperformed on each batch of products to establish repeat-

0.20

0.25

0.30

0.35

0.40

0.45

0.50

20 30 40 50 60 70 80 90Temperature (ºC)

Ther

mal

Con

duct

ivity

(W m

-1K

-1)

BolognaHamPepperoniTurkeyWieners

Fig. 1. Thermal conductivity of meat and poultry emulsions.


ability. Differences between values were found to be of thesame order of magnitude as the measurements undertakenon two individual batches indicating that our methodologywas highly repeatable. The observed differences betweenbatches were probably due to variation in the compositionof each batch, which may occur during production of eachbatch at industrial level.

3.2. Density

The density changes observed during cooking werevery small. There was no significant mass loss for theseproducts. Gelation or setting of the solid structure caused

960

980

1000

1020

1040

1060

1080

20 30 40 50Tempe

Den

sity

(kg

m-3

)

BolognaPepperoniWienersTurkeyHam

Fig. 2. Density of meat a

a small increase in the volume of the sample during theheating stage of the emulsions. At the start of heating,a slight increase in density, associated with all the testedemulsions, was observed which may be due to the fatcontent changing phase from solid to liquid. Neverthe-less, the densities decreased slightly with temperature ingeneral (Fig. 2). For each product, the density of the finalproduct heated to 80 �C was lower by approximately50 kg m�3 when compared with the initial raw productkept at room temperature. The density of the raw pep-peroni and ham products were 1031 and 1037 kg m�3

and for the cooked products were 969 and 1014 kg m�3,respectively.

60 70 80 90rature (ºC)

nd poultry emulsions.

2700

2800

2900

3000

3100

3200

3300

3400

3500

0 10 20 30 40 50 60 70 80 90

Temperature (°C)

Hea

t cap

acity

(J k

g-1 k

-1)

BolognaPepperoniWienersTURKEYHam

Fig. 3. Heat capacity of meat and poultry emulsions.

Table 4Correlation of heat capacity with temperature for various emulsions

Emulsion Heat capacity (J kg�1 K�1) r2

Bologna 2836.8 + 4.306 T 0.996Pepperoni 2719.1 + 3.760 T 0.978Wieners 2962.4 + 4.282 T 0.990Turkey 3003.3 + 4.567 T 0.998Ham 2940.3 + 4.234 T 0.996


3.3. Heat capacity

It is well known that the heat capacity varies with tem-perature, which was also confirmed by our results shown inFig. 3. The heat capacity values of meat emulsions at con-stant pressure increased linearly between 35 �C and 82 �Cwith a gradient of 4.34 J kg�1 K�1. Amongst the emulsionsstudied, the lowest heat capacity was found for the Pepper-oni emulsion: 2812 J kg�1 K�1 at 10 �C and 3027 J kg�1

K�1 at 82 �C. This emulsion showed a peak of 3160J kg�1 K�1 at 26 �C. For the bologna emulsion, the heatcapacity was found to be around 2933 J kg�1 K�1 at10 �C and 3191 J kg�1 K�1 at 82 �C. The heat capacitycurve for this emulsion also indicated a maximum valueof 3055 J kg�1 K�1 at 20 �C. The minimum was 2933J kg�1 K�1 at 10 �C. The heat capacity curve for wienershad a similar shape compared to bologna, but the valueswere approximately 150 J kg�1 K�1 higher over the curvelength. For the turkey product, the curve was almost linearover the entire range of temperatures studied, i.e. 10 to82 �C with a slope of 3.9 J kg�1 K�1 and an ordinate of3050 J kg�1 K�1. The ham product had a maximum heatcapacity of 3287 J kg�1 K�1 at 82 �C and a minimum of3031 J kg�1 K�1 at 10 �C, this curve also included a peakat 20 �C (3224 J kg�1 K�1). The average standard deviationfor all curves was 115.5 J kg�1 K�1. It has been reportedthat the composition of product (i.e. fat, protein, waterand salt contents) will influence the values of these proper-ties (Zhang et al., 2007). The peak values observed varieddirectly with fat content, suggesting that samples contain-ing higher fat content absorb more thermal energy forchanging the fat from solid to liquid state.

In order to be able to predict heat capacity of the emul-sions, the values of heat capacity were related to the tem-perature in the range 34–82 �C. Table 4 shows the

regression parameters along with the coefficient of determi-nation (r2) values. High values of r2 indicate that heatcapacity and temperature are noticeably related.

3.4. Thermal diffusivity

Thermal diffusivity is another property that changeswith temperature (Fig. 4), since it is related to all three ther-mophysical properties described above: conductivity, heatcapacity and density. The thermal diffusivity curves werevery similar to thermal conductivity curves because otherproperties are not as sensitive to temperature. The thermaldiffusivity values of the emulsions varied from 8.30 �10�8 m2 s�1 (for pepperoni and wieners) at room tempera-ture to 1.36 � 10�7 m2 s�1 (for pepperoni) at 80 �C.

3.5. Correlation between composition and thermophysical

properties

Values of thermophysical properties were measured fora variety of meat and poultry emulsions. Significant differ-ences were found that may be attributed to the meat andpoultry compositions. Average values of thermophysicalproperties were therefore, correlated to the chemical com-position. Table 5 shows the correlation matrix. A strongproportional relationship would be indicated by a value

8.000E-08

9.000E-08

1.000E-07

1.100E-07

1.200E-07

1.300E-07

1.400E-07

1.500E-07

1.600E-07

20 30 40 50 60 70 80 90

Temperature (ºC)

Ther

mal

diff

usiv

ity (m

2 s-1

)

BolognaPepperoniWienersTurkeyHam

Fig. 4. Thermal diffusivity of meat and poultry emulsions.

Table 5Correlation matrix for thermophysical properties vs. emulsion composition

k q Cp a Moisture (%) Fat (%) Salt (%) Protein (%) Carbohydrate (%)

k 1.000q 0.383 1.000Cp 0.744 0.734 1.000a 0.959 0.155 0.525 1.000Moisture (%) 0.960 0.530 0.743 0.899 1.000Fat (%) �0.978 �0.387 �0.668 �0.957 �0.986 1.000Salt (%) �0.652 �0.099 �0.488 �0.638 �0.450 0.503 1.000Protein (%) 0.776 �0.055 0.491 0.799 0.569 �0.654 �0.927 1.000Carbohydrate (%) �0.655 �0.828 �0.972 �0.419 �0.678 0.580 0.471 �0.393 1.000


close to 1 or �1; and a value close to 0 indicates weakcorrelation.

From a compositional point of view, the proportion ofmoisture was inversely correlated to the amount of fat.The proportion of salt was also inversely related to the per-centage of protein in these products. The density and theheat capacity were strongly influenced by carbohydratecontent (i.e. as the carbohydrate content increased, thedensity decreased). As the moisture content increased, thethermal conductivity and diffusivity increased. As the fatcontent increased, the value of k and a decreased. Contraryto the expectations, the proportion of salt did not have anysignificant effect on the density, heat capacity, thermal con-ductivity and diffusivity. This was probably due to the lim-ited amount of salt (approximately 2%) added to theemulsions. The proportion of proteins did not significantlyinfluence these properties as correlation coefficients weresmaller than 0.9.

3.6. Thermal conductivity modeling

Meat and poultry emulsions can be considered to beporous media containing a significant amount of gas (usu-

ally air) on a volume basis. These products are therefore,three phase systems containing air (a), water (w), and solids(s). To predict an effective thermal conductivity of the meator poultry emulsions, the applicability of various twophase-based models such as: Series model, Parallel model,Kopelman model, Maxwell model, Levy’s model, EMTmodel (Carson et al., 2005) was investigated. None of thesemodels was found to be adequate (results not shown).

The thermal conductivities of the food products werecalculated using Kirscher’s model (Eqs. (5)–(7)). The distri-bution factor f was adjusted for each product so that pre-dicted and experimental values matched. The f valuesvary from 0.35 (for pepperoni) to 0.45 (for Bologna andturkey). These values are much lower than the value of0.743 reported for dried apple by Maroulis et al. (2002)where the samples had very low moisture content. Howeverthey are comparable with the values reported by Hamdamiet al. (2003). The value of the distribution factor dependson the moisture content and decrease with the increase inmoisture content as has been reported by Hamdami et al.(2003). A comparison of predicted and experimental ther-mal conductivities demonstrated a good agreement. Statis-tical parameters of the comparison are summarized in

Table 6Comparison of statistical parameters for predicting thermal conductivitiesusing Kirscher’s model along with the weighting factor (f)

Foodemulsion

Weightingparameter(f)

Coefficient ofdetermination(r2)

Standarderror

Relative standarderror (%)

Bologna 0.45 0.916 0.0234 4.89Pepperoni 0.35 0.984 0.0047 1.39Wieners 0.40 0.986 0.0043 1.24Turkey 0.45 0.993 0.0033 2.10Ham 0.40 0.906 0.0187 3.43


Table 6. Low values of the standard error along with thehigh r2 values for all products indicated that the model per-formed well, where the highest and lowest values of r2 were0.993 and 0.906 for turkey and ham, respectively. Therelative standard error was less than 5% for all products.

4. Conclusions

Thermophysical properties are important because oftheir influence on the thermal exchanges between smoke-houses or cookers and meat and poultry emulsions, sincethe long cooking–cooling process relies heavily on conduc-tion heating. It is also important to identify the movementof heat through the product since proper cooking–coolingcycles are generally established using core temperaturemeasurement that must reach a pre-determined legalrequirement at the end of the process. Results of this paperhave shown that there are significant differences betweenthermophysical properties of various emulsions. The corre-lation matrix revealed that these differences may be attrib-uted to the composition of the emulsions. To predict thethermal conductivity of the product different models wereapplied and it was found that a generic three phase struc-ture based model is best suited to predict thermal conduc-tivities. Therefore, it is possible to formulate meat andpoultry emulsions with optimal thermophysical propertiesin order to maximize efficiency of cooking-cooling cycles.Moreover, these properties can also be used as an inputfor modeling of heat transfer during the cooking–coolingprocess.

Acknowledgments

The authors would like to thank the Canadian MeatCouncil for their support.

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