Analysis of Thermal Processing of Table Olives Using Computational Fluid Dynamics A. Dimou, E. Panagou, N. G. Stoforos, and S. Yanniotis

In the present work, the thermal processing of table olives in brine in a stationary metal can was studied through computational fluid dynamics (CFD). The flow patterns of the brine and the temperature evolution in the olives and brine during the heating and the cooling cycles of the process were calculated using the CFD code. Experimental temperature measurements at 3 points (2 inside model olive particles and 1 at a point in the brine) in a can (with dimensions of 75 mm × 105 mm) filled with 48 olives in 4% (w/v) brine, initially held at 20 ◦ C, heated in water at 100 ◦ C for 10 min, and thereafter cooled in water at about 20 ◦ C for 10 min, validated model predictions. The distribution of temperature and F-values and the location of the slowest heating zone and the critical point within the product, as far as microbial destruction is concerned, were assessed for several cases. For the cases studied, the critical point was located at the interior of the olives at the 2nd, or between the 1st and the 2nd olive row from the bottom of the container, the exact location being affected by olive size, olive arrangement, and geometry of the container.

Keywords: computational fluid dynamics, critical point, heat transfer, table olives, thermal processing

The resulting knowledge from the present paper will help in thermal process optimization of table olives which will lead to the production of safe and high-quality product with reduced salt content. In the present paper, only the safety issue, through the determination of the location of the critical point, as far as microbial destruction is concerned, was addressed.

Practical Application:

Introduction According to the Intl. Olive Council (IOC 2004) “Table Olives” is the product prepared from the fruits of the cultivated olive tree (Olea europea L.) “treated to remove its bitterness and preserved by natural fermentation, or by heat treatment, with or without the addition of preservatives,” packed with or without covering liquid, and offered for trade and for final consumption. Based on the preparation process the table olives have been undergone, IOC (2004) categorizes them in the following 5 trade preparations: treated olives, natural olives, dehydrated and/or shriveled olives, olives darkened by oxidation, and specialties. The NaCl concentration, maximum pH, and minimum lactic acidity of the packing liquid, different for each trade preparation, dictate the severity of the heat treatment required for each type of heat treated olives. Thus, for example, for all types of pasteurized olives a minimum of 2% NaCl brine can be used as long as pH does not exceed 4.3. On the same line, for commercial heat sterilized olives there is no minimum NaCl content, as long as maximum pH limit is set at 8.0 (Codex Alimentarius 1987). In a processing/preparation scheme in Greece for natural black Kalamata olives, with 2-y shelf life, aiming in product with reduced salt content, the olives, after fermentation, are randomly packed in jars or cans which are then filled with warm (65 ◦ C) brine (4% NaCl with pH 3.4 to 3.8). The jars are sealed under vacuum and heat treated for 15 to 20 min in hot water of 80 ◦ C, before cooling with 30 ◦ C water.

MS 20130406 Submitted 3/21/2013, Accepted 8/26/2013. Authors are with Dept. of Food Science and Human Nutrition, Agricultural Univ. of Athens, Athens, Greece. Direct inquiries to author Yanniotis (E-mail: [email protected]).

 R  C 2013 Institute of Food Technologists

doi: 10.1111/1750-3841.12277 Further reproduction without permission is prohibited

Different process schedules for whole ripe olives, with or without pits are suggested in Lopez (1987). The Intl. Olive Council published minimum required F process values for thermal processing of different types of olives (IOC 2004). The FTz value of a thermal process (or simply F) is defined as the time in minutes at a constant temperature, T, required to destroy a given percentage of microorganisms whose thermal resistance is characterized by z, or, as the equivalent processing time of a hypothetical thermal process at a constant temperature that produces the same effect (in terms of spore destruction) as the actual thermal process. Thus, for olives using heat treatment as an added hurdle to attain microbial stability, a pasteurization process with an equiv5.25◦ C alent F62.4 ◦ C value of at least 15 min is suggested. The above required F process value, termed by IOC (2004) as pasteuriza5.25◦ C tion units (P U62.4 ◦ C ), refers to propionic bacteria, characterized by a z-value of 5.25 ◦ C, as the reference microorganisms. The reference temperature used, equal to 62.4 ◦ C, is indicative of the mild nature of the required thermal process. On the other hand, for those products exclusively relying on heat treatment for their preservation (as for olives darkened by oxidation) a severe thermal process, as those processes designed for commercial sterilization of low acid foods, equivalent to a minimum Fo value of 15 min, is recommended (IOC 2004), where Fo value refers to a z-value of 10 ◦ C and a reference temperature of 121.11 ◦ C (that is, Fo = 10◦ C F121.11 ◦ C ). Process schedules, that is heating time–retort temperature combinations, to achieve the recommended F-values are left to olive processors. Referring to the IOC (2004) F-value suggestions, a vagueness concerning the location inside the container where the F-values should be measured or calculated emerges. Are these F-values for the covering (liquid) brine or the (solid) olives? If we quickly disregard the brine case, which olive becomes the critical particulate Vol. 78, Nr. 11, 2013 r Journal of Food Science E1695

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Abstract:

Analysis of olive thermal processing . . .

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that we must monitor? Which region, within the critical olive(s), represents the “critical point,” that is the region inside the container that receives the least effect, in terms of destruction of the undesirable, target microorganisms, of the heat treatment? Knowledge of the heat transfer phenomena coupled with the appropriate microbial heat destruction kinetics is essential for answering the above raised questions. Computational fluid dynamics (CFD) has been used with increasing rate in food engineering research, in particular during the last decade (Norton and Sun 2007). Problems dealing with thermal processing of liquid or liquid/particulate systems have been successfully approached through CFD, in terms of predicting transient velocity and temperature profiles inside still processed containers. Examples include analysis of natural convection heating and bacterial destruction in canned liquids (sodium carboxy-methyl cellulose [CMC] and water; Abdul Ghani and others 1999a, 1999b), heat transfer coefficient determination during natural convection heating of CMC solutions in cylindrical containers (Kannan and Gourisankar Sandaka 2008), pasteurization of beer (Augusto and others 2010) or milk (Anand Paul and others 2011), and simulation of liquid/particulate thermal processing, such as large food particle in water (Rabiey and others 2007), pineapple slices in juice (Abdul Ghani and Farid 2006), peas in water (Kiziltas¸ and others 2010), and asparagus in brine (Dimou and Yanniotis 2011). Quality degradation during thermal processing, for example, undesirable softening of the olive tissue, must be kept minimum. Thus, precise design and monitoring of the applied process is essential. The objective of the present work was to study the fluid flow and heat transfer phenomena during thermal processing of table olives through CFD. In particular, fluid flow field, temperature evolution, distribution of F-values, and the location of the critical point within the product, as far as microbial destruction is concerned, were assessed for several cases. The effect of the size/shape of the olives in the performance of the process was also investigated.

Materials and Methods Process details In all simulated cases, a uniform initial temperature of 20 ◦ C for both olives and brine was used. Calculations were performed for 10 min heating at 70 ◦ C and 10 min cooling at 20 ◦ C. This does not exactly correspond to a commercial process where the can is usually filled with hot brine (for example, 65 ◦ C). Three table olive geometries, differing in size or shape, were considered. Thus we used 2 different size Kalamata olives (one 25 mm long and 12 mm in diameter, termed hereafter “small” and another 30 mm long and 19 mm in diameter, termed hereafter “large”) and a Conservolea one (30 mm long and 24 mm in diameter). All were designed as close as possible to their natural shape (Figure 1). For every case, a tin can with dimensions of 75 mm × 105 mm filled with brine and the appropriate number of olives, necessary to fill up the container, was used. Eighty small-size Kalamata olives, in 8 rows with 10 olives per row, were orderly placed in the can (8×10 arrangement). Similarly, 48 large-size Kalamata olives (6×8 arrangement) and 48 Conservolea olives (6×8 arrangement) were arranged in the can as presented in Figure 2. The above number of olives approximately represents commercial practices for the can size used. In addition to the 3-dimensional olive arrangement, vertical cross-sections for projection of data in 2-dimensional form are shown in Figure 2, for all cases studied. E1696 Journal of Food Science r Vol. 78, Nr. 11, 2013

Computational and theoretical model The system under investigation consisted of a stationary cylindrical metal can (75 mm in diameter, 105 mm in height) filled with solid particles (table olives) and a covering liquid (brine) initially at rest and at uniform temperature, heated at a medium with constant temperature for a predescribed period of time followed by cooling at (also) constant medium temperature. We further considered infinite heat transfer coefficient between heating or cooling medium and external can wall, negligible resistance to heat transfer of the metal wall, and no slip at the container’s wall. Mass transfer from or to olives was neglected. Only heat transfer from the can to the brine and from the brine to olives was considered. Temperature evolution and flow patterns during natural convection heating for the system under investigation were calculated by solving the governing partial differential equations for mass, momentum, and energy conservation (Bird and others 1960) with the above-mentioned initial and boundary conditions. Since an analytical solution to those equations is not feasible, they were solved numerically as a system of algebraic equations applied in finite volumes that the system was discretized. Software details The software FLUENT 6.3.26, 2006 with 3D, double precision, pressure-based, laminar flow was used to solve the system of partial differential equations. The use of laminar flow in the model was justified since the Rayleigh nr (based on the length of the olive) was less than 107 during the heating and cooling cycle. A personal computer with Intel Core TM 2, CPU 660 @ 2.40 GHz, 2048 MB RAM and Microsoft Windows XP Professional Version 2002 Service Pack 2 operating system was employed. A preset convergence limit of 1×10−3 for continuity and momentum equations and 1×10−6 for the energy equation was used. The time step used was equal to 0.5 s, the Courant nr was equal to 0.5, the algorithm of pressure–velocity coupling was “Coupled,” and the model used for the discretization of pressure was “PRESTO!”. For the discretization of momentum and energy equations, the model “Second Order Upwind” was selected. In FLUENT nomenclature, the internal surface of the can, as well as the external surface of the olives, was defined as wall. The volume of the olives was considered as solid .The volume between the olives and the can, occupied by the brine, was considered as fluid. The volumes of the brine and the olives were designed in Gambit 2.3.16. The shape of the grid was “Tet/Hybrid – TGrid” for both olives and brine volumes with the option of 10% “Shortest edge” of the software. In this case of small Kalamata olives, 180982 cells in total (for both olives and brine) were used for the descretization and solution of the governing equations, while for the large Kalamata and the Conservolea olives 130226 and 158559 cells, respectively, were employed. The above scheme was considered adequate, since a finer grid, with about double the cells just mentioned for each case, that was also tested produced similar results. For such calculations, the computational time was about 5, 2, and 3 d, for the case of the small Kalamata, the large Kalamata, and the Conservolea olives, respectively. C

R

R

R

Physical properties Knowledge of the thermal and rheological properties of the brine, as well as their temperature dependence, is essential in describing the motion of the brine and consequently the rate of temperature rise during the thermal processing. The thermophysical

Analysis of olive thermal processing . . .

R

Small Kalamata olives

Table 1–Physical and thermal properties of liquid and solids used in simulationsa . Brine (4% NaCl) ρ (kg/m3 ) μ (Pa·s)

Cp (J/kg·K)

k (W/m·K)

a

1.0334 × 103 − 2.0517 × 10−1 × T − 2.4685 × 10−3 × T 2 (R2 = 0.9999) 1.7087 × 10−3 − 3.6550 × 10−5 × T + 3.4048 × 10−7 × T 2 − 1.1334 × 10−9 × T 3 (R2 = 0.9981) 3.9630 × 103 + 7.3626 × 10−2 × T + 5.3846 × 10−3 × T 2 (R2 = 0.9999) 5.6592 × 10−1 + 1.7906 × 10−3 × T − 6.5734 × 10−6 × T 2 (R2 = 0.9999)

Olive 1035 ± 5

Teflon 2240 ± 2.5

–

–

3200 ± 100

1050 ± 1

0.40 ± 0.05

0.42 ± 0.001

Temperature, T, in equations is in ◦ C.

dry wood) was used for the kernel (compared to 1.2×10−7 m2 /s determined and used for the flesh). Simulations show that for the case where olive flesh and kernel were treated with the same properties (Case I) the product was heating and cooling slightly

Large Kalamata olives

Conservolea olives

Figure 1–Olive shape and dimensions used in simulations. (A) Small Kalamata olives, (B) large Kalamata olives, and (C) Conservolea olives.

A

B

C

Figure 2–Three-dimensional arrangement of solid particles (olives) in a vertical metal can and related vertical cross-sections for projection of data in 2-dimensional form in subsequent figures. (A) Small-size Kalamata olives in 8 rows with 10 olives per row (8×10 arrangement), (B) large-size Kalamata olives (6×8 arrangement), and (C) Conservolea olives (6×8 arrangement).

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properties of 4% (w/v) NaCl aqueous solution obtained from literature (Irvine 1998) were used for the brine. More specifically, each property was expressed as a function of temperature (T, in ◦ C) through a 2nd- or 3rd-order polynomial equation (derived in Microsoft Office Excel) based on the temperature dependence values, for that particular property, obtained from the literature database (Irvine 1998). Thus, for a temperature range of 10 ◦ C to 130 ◦ C, the input equations to FLUENT for density (ρ), specific heat (cp ), thermal conductivity (k), and viscosity (μ) for the brine are shown on Table 1. Density, heat capacity, and thermal conductivity of olives were measured experimentally at 20 ◦ C (at least 5 replicates). The method of water displacement was used to measure olive density. Heat capacity was measured by Differential Scanning Calorimetry (model Q100, TA Instruments, Inc., New Castle, Del., U.S.A.) whereas the thermal conductivity was measured by a Thermal Properties Analyzer (model KD2, Decagon Devices Inc. Pullman, Wash., U.S.A.). The mean values shown on Table 1 for olives, that is constant values (temperature and variety independed), were used as input data to FLUENT for all runs. The default FLUENT values for steel properties were used to describe the properties of the container. Both flesh and kernel were treated as a whole with the same thermal properties. Preliminary runs were made using different properties between flesh and kernel. A thermal diffusivity value equal to 0.8×10−7 m2 /s (corresponding to low thermal diffusivity

Analysis of olive thermal processing . . . faster compared to the case where different kernel properties (Case II) were considered due to differences in thermal diffusivity values. Differences in temperatures between the 2 cases were rather small. For example, after 8 min of heating at 70 ◦ C of a can with 48 Conservolea olives, the temperature at the slowest heating point (SHP) for Case I was 64.1 ◦ C and for Case II the corresponding value was 63.6 ◦ C. Including cooling, the accumulated F-value at the critical point for Case I was 5.2 min and for Case II 4.7 min. After 10 min of heating at 70 ◦ C, the corresponding temperature values were 66.9 ◦ C and 66.6 ◦ C, for Case I and II, respectively,

while the accumulated F-value at the critical point for Case I was 19.4 min and for Case II 18.1 min. Due to the calculated small temperature differences between the 2 cases and the uncertainties in kernel properties (which by itself is not homogeneous) it was decided to not take into account any differences between flesh and kernel properties.

F-value calculation A User Defined Function (UDF) was written and imported to FLUENT in order to calculate the F-value of the process through Eq. 1, based on the classical thermobacteriological approach (Ball and Olson 1957).  t T−T ref 10 z d t (1) FTzr e f =

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0

Equation 1 is based on 1st-order microbial reduction kinetics and linear dependency of the logarithm of decimal reduction time on temperature. Although deviations from 1st-order kinetics have been observed and sophisticated models have been reported in the literature (Sapru and others 1993), the 1st-order model is generally accepted as an engineering tool for thermal process calculations (Pflug 1987) and has a successful record of applications in the thermal processing industry. It is worth to notice that Eq. 1 is also applicable for nth order reactions with any limitations originating from the definition of the z-value (Stoforos and others 1997) although in these cases the F-value does not directly represent logarithmic reduction of microbial population. As far as the temperature dependence of thermal destruction rates, for the rather short range of temperatures, where inactivation takes place, a number of models, including the z-value approach, are considered adequate. F-values were calculated at every point inside the container where temperature values were available, that is, at every node of the grid, through numerical integration of Eq. 1, using the trapezoidal rule, based on a z-value of 5.25 ◦ C (indicative to propionic bacteria) and a reference temperature of 62.4 ◦ C, appropriate for pasteurization processes. Thus, the distribution of the F-value could be assessed.

Figure 3–Comparisons between experimental (Exp I and Exp II) and simulated (CFD) temperature data for large Kalamata olives (6×8 arrangement) in 4% (w/v) brine in a stationary metal can during heating and cooling in water. TW represents the internal can wall temperature. (A) Brine temperature at a point with x = 1.8 mm, y = –6.5 mm, and z = 0 coordinates, (B) temperature at the center of an olive located in the middle of the 2nd row from the bottom (x = 6 mm, y = –2.9 mm, and z = 0 mm), and (C) temperature at the center of an olive located in the middle of the 2nd row from the top (x = 0 mm, y = 16.6 mm, and z = –6 mm).

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Model validation An experimental procedure was employed in order to evaluate the performance of the proposed model. For this, experimental temperature data for large Kalamata olives (6×8 arrangement) in 4% (w/v) brine in a stationary metal can (with dimensions of 75 mm × 105 mm) during heating and cooling in water were collected at 3 points: (1) in a point in the brine, between the 3rd and the 4th olive row, and specifically, at the point with x = 18 mm, y = –6.5 mm, and z = 0 coordinates; (2) at the center of a model olive located in the middle of the 2nd row from the can bottom (x = 6 mm, y = –29 mm, and z = 0 mm); and (3) at the center of a model olive located in the middle of the 2nd row from the top of the can (x = 0 mm, y = 16.6 mm, and z = –6 mm). The origin, x = 0, y = 0, and z = 0 is at the geometric center of the can, x and y (which coincides with the axis of the cylinder) are at the plane of the paper, with positive x to the right and positive y to the top, and positive z coming out of the plane of the paper toward the reader as shown in Figure 2. The 46 out of the 48 olives that the can was filled with during the validation experiments were actual Kalamata olives. The other 2 olives, for which temperature data were obtained, were model olives, made from Teflon (glassy type, bought and handcrafted by a local supplier

Analysis of olive thermal processing . . .

Results and Discussion Model validation For the model validation trials, comparison between experimentally determined temperatures and corresponding CFD predicted values was satisfactory (Figure 3). Variation between replicate experiments, as far as temperatures at the center of the (model) olives is concerned, was higher than the differences between predicted and experimental values (Figure 3B and 3C). Differences between predicted and experimental values for the temperature measurements in the brine were higher compared to those in the Teflon olives; it is expected that such differences attenuate as we proceed along the center of the olives. Based on the observed agreement between experimental and predicted data, the selected parameters and procedures for the CFD calculations through the FLUENT software were considered appropriate and were retained in all subsequent runs. It is worth to say here, that early trial runs with different time steps (0.01, 0.1, and 0.5 s) produced similar results (less than 0.5% momentary differences on temperature values and about 0.3% differences on F-values—data not shown) and therefore, the biggest time step of 0.5 s, which considerably saved calculation time (45, 7, and 2 d

Small Kalamata οlives

for time steps of 0.01, 0.1, and 0.5 s, respectively, for the case of large Kalamata olives), was used.

Velocity profile The movement of the brine in the can was a typical, natural convection, motion. During the heating cycle, the heated brine near the wall is moving upward, toward the top of the container due to buoyancy forces, forcing the brine sitting on the top of the container to move downward at the interior of the can through the layers of olives. This circular flow is repeated until the brine approaches the temperature of the heating medium (approximately after 7 min of heating, Figure 3A). The presence of the olives substantially affects the flow of the brine. Figure 4 shows such a pattern after 30 s of heating for the different cases examined. The simulation of the flow field, as exported from the CFD model, is in agreement with literature results (Hiddink and others 1976; Datta and Teixeira 1988; Kumar and others 1990). Thirty seconds after the onset of heating, maximum calculated velocities were 2.627 cm/s, 2.619 cm/s, and 3.065 cm/s for the case of 4% brine filled with small Kalamata, large Kalamata, and Conservolea olives, respectively. When the cooling phase starts, the direction of brine movement is changing. Now the brine is moving downward near the wall because of the fluid’s cooling and upward in the interior can space. Thirty seconds after the onset of cooling, maximum calculated velocities were 2.558 cm/s, 2.113 cm/s, and 3.017 cm/s for the case of 4% brine filled with small Kalamata, large Kalamata, and Conservolea olives, respectively. Temperature profile Typical temperature contours for large Kalamata olives (arranged in 6 rows with 8 olives per row—6 olives at the perimeter and 2 olives at the center of the container) in 4% brine at different heating and cooling times are shown in Figure 5. The olives located at the 2nd row from the bottom and at the center of the can were the slowest heating olives within the container. The interior of these olives represented the slowest heating region of the system under investigation. Due to the brine motion, these same olives were the fastest cooling olives during the cooling cycle of the process, as it is evident from Figure 5, after 3 min of cooling. Thus, the critical region (or critical point), that is, the region that receives

Large Kalamata οlives

Conservolea οlives

Figure 4– Velocity profiles of 4% brine (initially held at 20 ◦ C) in a stationary cylindrical can filled with table olives after 30 s of heating at 70 ◦ C (velocity in cm/s).

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with properties shown in Table 1). The substitution of 2 actual olives with model olives was necessary, since variation on olive size and uncertainty on the precise location of the thermocouple inside the olive, that was tried initially, caused variations on collected temperature data. Thus, 2 model Teflon olives, made to similar shape and size as the actual olives, with thermocouples fixed at their center, were used to collect temperature data during thermal processing. Temperature data were collected through a temperature recorder (OMEGA ENGINEERING, OM-220) with type K thermocouples 0.5 mm in diameter in replicate experiments. The can and its contents were initially held at 20 ◦ C, and thereafter they were heated in water at about 100 ◦ C for 10 min and cooled in water at about 20 ◦ C for 10 min. CFD simulations of the above experimental setup were performed, with the 2 Teflon model olives (and their properties) been used at the appropriate positions inside the can, instead of the actual Kalamata olives. This was done only in the validation runs.

Analysis of olive thermal processing . . .

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the least effects, as far as microbial destruction is concerned, of the heat treatment, should lie at the interior of these olives. The exact location of the critical region will be presented later on, when the distribution of the F process values will be discussed. Similar results, as far as temperature distribution is concerned, were obtained for the small Kalamata olives (arranged in 8 rows with 10 olives per row—7 olives at the perimeter and 3 olives at the center of the container) and the Conservolea olives (arranged in 6 rows with 8 olives per row—7 olives at the perimeter and 1 olive at the center of the container). The 3 olives located at the 2nd row from the bottom and at the center of the can for the small Kalamata olives, and the olive located at the 2nd row from the bottom and at the center of the can for the Conservolea olives were the slowest heating and the fastest cooling olives within the container for each case. The exact rectangular coordinates (x,y,z) in mm for the SHP for the case studied were: (11.5,–33.0,0.0), (–8.5,–33.0,–6.7), and (0.9,–33.0,10.8) for the small Kalamata olives and (± 9.0,– 29.0,0.0) and (0.0,–24.5,0.0) for the large Kalamata and the Conservolea olives, respectively. Taking the risk of being redundant, but for correct interpretation of the coordinates given above, and in relation to Figure 5 (or other similar 2-dimensional figures) we must note that: (1) the origin, x = 0, y = 0, and z = 0 is at

the geometric center of the can; (2) positive x is measured as we move from the center to the right on the 2-dimensional plane; (3) positive y is measured as we move from the center to the top on the 2-dimensional plane; (4) positive z is measured as we move from

Figure 6–Comparison of temperature profiles at the slowest heating zone during thermal processing of table olives in 4% brine in stationary cylindrical metal cans (heating in water at 70 ◦ C for 10 min before cooling at 20 ◦ C in water).

0.5 min of heating

1.5 min of heating

3 min of heating

0.5 min of cooling

1.5 min of cooling

3 min of cooling

Figure 5–Typical temperature contours for large Kalamata olives (6×8 arrangement) in 4% brine in a stationary cylindrical metal can, initially held at 20 ◦ C, at different heating and cooling times during heating (at 70 ◦ C) and cooling (at 20 ◦ C) in water.

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the center out of the paper. Thus, the (0.0,–24.5,0.0) coordinates interior of 3 olives) while for the large Kalamata olives 2 positions given for the Conservolea olives locates the SHP at the center axis, where the SHP is located. 28 mm from the bottom of the container. Also note that for the The temperature profiles for the slowest heating zone during the small Kalamata olives there are 3 positions (corresponding to the thermal process for the cases studied are presented in Figure 6. For

Figure 7–F-value distribution within the olives at the end of a thermal process consisting of heating in water at 70 ◦ C for 10 min before cooling in water at 20 ◦ C in a stationary cylindrical metal can.

Figure 8–Location of the critical point of a thermal process consisting of heating in water at 70 ◦ C for 10 min before cooling in water at 20 ◦ C in a stationary cylindrical metal can. Illustration includes appropriate side (1st row) and top (2nd row) views at the planes where the olives containing the critical point are located.

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the conditions examined, the size of the olives was the determined the SHP, due to the lethal contribution of the cooling cycle. However, from a practical point of view it should be pointed out parameter on the temperature of the SHP. that differences in F-values were not important with respect to the F-value distribution safety precautions taken in designing thermal processes. 5.25◦ C The distribution of the F62.4 Due to the nonsymmetrical olive arrangement (Figure 2), the ◦ C process values for the olives is presented in Figure 7. An extensive differentiation of F-values, 3 olives located at the 2nd row from the bottom and at the center based on olive size, location of the olives inside the container and of the can, for the small Kalamata olives where the critical points the point of interest inside the olive, is evident. For the thermal were located (Figure 8A), did not exhibit exactly the same F5.25◦ C treatments applied, process F-values ranged approximately from values. Specifically, the exact F62.4 ◦ C values at the 3 critical points 70 min (for the interior of the olives located at or close the SHP) for the small Kalamata olives were 64, 64.3, and 65.2 min. These to 160 min (at the surface of the olives located at the top of the can) values were notably different from the minimum F-values achieved for the case of small Kalamata olives. The corresponding ranges at the 7 surrounding olives, ranging from 68.9 to 71.4 min, with for the large Kalamata and Conservolea olives were 37 min to the olives on the bottom row having minimum F-values from 130 min and 20 min to 145 min, respectively. 73.2 min to 77.2 min, and the olives on the 3rd row from the The point, the critical point, that should be monitored, in order bottom having minimum F-values from 70.2 to 77.6 min. 5.25◦ C to assess the effectiveness of a given process, should be precisely Similarly, for the large Kalamata olives, the exact F62.4 ◦ C values known. At the end of the entire process, the exact coordinates at the 2 critical points were 38.8 and 39.0 min. The minimum (x,y,z) in mm, for the critical point for the cases studied were: F-values achieved at the 6 surrounding olives ranged from 47.8 to (9.2,–33.0,–5.2), (–8.5,–33.0,–6.7), and (0.9,–33.0,10.8) for the 48.2 min, the olives on the bottom row having minimum F-values small Kalamata olives and (± 7.0,–30.5,0.0) and (0.0,–35.0,0.0), from 42 min (at the central axis) to 54 min (at the circumference for the large Kalamata and the Conservolea olives, respectively of the can), and the olives on the 3rd row from the bottom having (Figure 8). Note that the critical points did not coincide with minimum F-values from 47.5 to 57.8 min.

Figure 9–Special case of processing of large Kalamata olives (7×8 arrangement) in 4% brine in a stationary rectangular metal can. (A) Three-dimensional arrangement of olives, (B) velocity profiles of brine (in cm/s), (C) and (D) location of the critical point.

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Analysis of olive thermal processing . . .

Conclusions Fluid flow and heat transfer phenomena during thermal processing of table olives in brine, in a stationary tin can, were successfully simulated through CFD. Consequently, the distribution of temperature and F-values and the location of the slowest heating zone and the critical point within the product, as far as microbial destruction is concerned, were assessed. For the cases studied, the critical point was located at the interior of the olives at the 2nd, or between the 1st and the 2nd olive row from the bottom of the container. The critical points did not exactly coincide with the points of the slowest heating zone, due to the lethal contribution of the cooling cycle. A number of parameters need to be determined and

studied further, for example, the effect of olive placement (orderly or randomly), container orientation (vertical or horizontal), and shape (cylindrical or rectangular), as they might affect the brine flow and the exact location of the critical point. In analogy to the work presented in the current investigation, one can calculate quality degradation at the end of a thermal process given the key quality parameter and the kinetics of its thermal degradation. Different time–temperature schedules, leading to safe product, can be evaluated in terms of their impact to product quality, through an optimization scheme, and select the one that will lead to the production of safe product with the least quality degradation.

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Vol. 78, Nr. 11, 2013 r Journal of Food Science E1703

E: Food Engineering & Physical Properties

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5.25 C Finally, for the Conservolea olives, the exact F62.4 ◦ C value at the critical point was 20.0 min. For this case, the Conservolea olives at the central axis were slightly pressed one on top of the other, so that there was some overlapping between the volumes of the 2 separate olives (Figure 7). The critical point was located between the 2 central olives located at the last 2 rows (Figure 8C). The minimum F-values achieved at the 2 olives, above and bellow the critical point, were 21.0 and 21.5 min, respectively. The olives at the circumference of the can at the bottom row had minimum Fvalues from 27.2 to 28.9 min, while the olives at the circumference of the can at the 2nd row from the bottom had minimum F-values 32.6 min. Based on the quantitative data presented in the previous paragraphs, the identification of the critical point as presented in Figure 8 is justified. Asymmetrical heating gives rise to slight differences on minimum F-values for the critical points presented in Figure 8, but these points are clearly distinguished from the points with minimum F-values at the rest of the olives. In all cases studied, the brine around the central axis and toward the bottom of the container represented the cold fluid volume of the system. Particle size (as it mainly influences heat flux by conduction) as well as particle orientation, arrangement, and packing density (as it influences brine motion and therefore heat transfer from the brine to the particles) will affect the exact location of the critical point. Thus, for example, for some particular case, olives away from the can central axis can contain the critical point. Such a case is illustrated in Figure 9. Large Kalamata olives (arranged in 7 rows with 8 olives per row) in 4% brine are processed in a rectangular tin container (82 mm in length, 57 mm in width, and 121 mm in height) as shown in Figure 9A. The same time–temperature conditions, as for the cases in the cylindrical can presented so far, were applied. Compared to the previous cases, fluid velocities were more uniform for this case and rather high between the olives located at the center of the container, probably due to the availability of free space between the olives (Figure 9B). As a result, the critical point was located at the interior of the olives located away from the central plane as illustrated in Figure 9C and 9D.