High Hydrostatic Pressure Processing Uniformity in the Picture
Tara Grauwet, Cornelia Rauh, Iesel Van der Plancken, Liesbeth Vervoort, Antonio Delgado, Marc Hendrickx and Ann Van Loey
Pressure applied in high hydrostatic pressure processing acts mostly instantaneously and uniformly. Nevertheless this new technique cannot avoid the limitation of compression heat transfer, resulting in temperature gradients in space and time inside the high pressure vessel. When temperature becomes an important variable for process impact achievement, this temperature non-uniformity may result in process non-uniformity. This article discusses different methods to determine the temperature uniformity inside a high pressure vessel.
The Potential of High Hydrostatic Pressure Processing
Since the last decade, consumers prefer minimally processed, additive-free foods with fresh-like characteristics. This has led food scientists to investigate a number of novel processing methods, e.g. high hydrostatic pressure processing (HPP), as a complement or alternative to conventional technologies such as thermal pasteurisation and sterilisation. Several types of applications reflect the success of HPP in the food industry. First, HPP is an effective alternative for traditional thermal processing of high quality food products with a high water content, for which the balance between safety and quality attributes of the processed product is very important. Currently, commercial applications are limited to pasteurised food products (prolonged in shelf life at refrigerated conditions with days to weeks by inactivation of vegetative cells: 400-600 MPa; initial temperature 10-40°C; 1-15 min) including fruit juices, ready-to-eat meals, meat, salsas (San Martin et al. 2002). Second, high pressures combined with high temperatures (prolonging the shelf life at room temperature with months to years by inactivating spores: 500-800 MPa; initial temperature 90-110°C; max. 10 min) is within reach, due to ongoing research developments (Matser et al. 2004).
Third, pressure-induced changes in food functionality due to for instance starch gelatinisation and protein denaturation are well-known phenomena (Ludikhuyze et al. 2003, Bauer and Knorr 2005). For example, labor intensive shucking and meat extraction of seafood can be significantly shortened by high pressure-induced denaturation of muscular proteins responsible for the connection of both the shell and body of seafood (Tonello 2009). The effect of pressure on the stability of enzymes is enzyme dependent and can be dual: pressure can inactivate enzymes, but it can also enhance their action under pressure. The potential of HPP selectively knocking out spoilage or quality degrading enzymes and/or the activation of enzymes enhancing product quality has been described in the literature (Verlent et al. 2007).
Temperature Gradients Inside a High Pressure Vessel
HPP has been presented as a unit operation with uniform impact on the product. However, care should be taken with this statement (Delgado and Hartmann, 2003). The pressure applied to the food can indeed be assumed uniform, but not the temperature (Denys et al. 2000, Delgado et al. 2008, Rauh et al. 2009, Khurana and Karwe 2009). As pressure increases under adiabatic conditions, temperature increases as described by Equation 1:
In this equation, α represents the volumetric thermal expansion coefficient (K-1), ρ the density (kg/m³), cp the specific heat (J/kg.K) at a particular temperature T (K) and pressure p (Pa). The pressure and temperature dependency of these thermodynamic and thermophysical parameters is component dependent. Water, for example, the major constituent of food products relevant for HPP processing, is compressed up to 15 % by volume during a pressure increase up to 600 MPa. The conversion of the work of compression into internal energy results in a temperature increase. During the processing time, heat transfer takes place between components with different compression heating. When, for example, the compression heat of the high pressure vessel wall, of the pressure transmitting medium, of the food packaging, of the food product, or even different constituents of a food product differs, a gradient is established, which causes heat transfer to occur (conduction phenomenon). The resulting density differences within the pressurising medium lead to a downward draft of fluid near the wall (if the walls are colder than the interior) and rising flow in the middle (free convection phenomenon) (Rauh et al. 2009, Hurana and Karwe 2009). In addition, the effect of pressure medium addition in an injection pressurising system as a cause of temperature gradients has been reported in the literature. If the pressure medium heats up due to compression (e.g. T > Tinitial,medium) at the moment when additional pressure medium (= Tinitial,medium) is being injected, medium addition could cause temperature non-uniformities (forced convection phenomenon) (Abdul Ghani and Farid 2007, Khurana and Karwe 2009). The dependency of the compression heating on temperature was shown in Equation 1.
Effect of Temperature Gradients on Process Uniformity
Depending on the temperature sensitivity of the kinetics of the change in target attributes (e.g. safety, quality), the temperature non-uniformity described above can result in process impact non-uniformity (Denys et al. 2000, Hartmann and Delgado 2002, 2003, Van der Plancken et al. 2008). Therefore, the food industry should be aware of this temperature non-uniformity in high pressure equipment and its consequence for each type of product treated. Since pressure is the predominant process parameter for the inactivation kinetics of vegetative cells in high pressure pasteurisation applications, non-uniformity of the process field is limited. However, when studying the inactivation kinetics of spores under high pressure – high temperature conditions, temperature has proved to become a determining variable (Ju et al. 2008, Zhu et al. 2008, Barbosa-Canovas and Juliano 2008, Juliano et al. 2009).
Irrespective of the circumstances, the in situ method can provide direct information on the target attributes of interest by measuring these characteristics before and after the treatment. In the context of process optimisation, the impact of a process on safety and quality attributes should be verified respectively in the lowest and highest impact points of the vessel. As antagonistic or synergistic effects of pressure and temperature on kinetics have been described, the detection of the point of lowest (or highest) impact is not always straightforward. Opposite to conventional thermal processing, the point of lowest impact (i.e. of lowest temperature in thermal processing) is not necessarily in the core of the product (typically the geometric center in case of conductive heating). In HPP, the point of lowest impact only coincides with the minimum of the temperature field in the case of a synergistic effect between temperature and pressure. In practice, the measurement of microbial counts, texture, vitamin content, etc, is time-consuming, laborious, expensive and, in some cases, even impossible because of the detection limit.
Demonstrating Temperature Uniformity
To overcome the hurdles related to the in situ method, other methods to ‘picture’ the temperature uniformity have been developed or are currently in a developing stage: (i) direct monitoring of the temperature profile, (ii) the use of pressure-temperature-time indicators (pTTI), and (iii) numerical simulation of the temperature distribution. When, in addition to the temperature distribution and pressure level, the kinetic parameters (k(T,p) and kref variables representing the rate of inactivation at temperature T and pressure p and for reference conditions, respectively) of the target attribute are known, the impact (F-value) can be estimated by Equation 2:
Direct monitoring of the temperature profile
Figure 1: Direct monitoring of the temperature-time history at different coordinates of a high pressure vessel is the most obvious method to ‘picture’ the temperature non-uniformity.
An example of temperature profiles recorded by 3 thermocouples type K (Figure 1) at 3 different radial positions in a laboratory scale (volume of 0.5 L), vertically oriented, single vessel system (EPS International, SO.5-7422-0, Belgium) is depicted in Figure 2. Profiles are recorded in the pressure medium (60% DowcalN in deionized water; Dow Chemical Company, Switzerland) (Figure 2). In general, the temperature increase from 15°C to maximally 38°C due to the pressure build-up (600 MPa) is followed by a temperature decrease due to heat exchange with the surrounding through the inert vessel wall and ultimately, due to the pressure release. Temperature history curves were position dependent: (i) in a coordinate more closely to the vessel wall, the temperature increase due to compression is lower in comparison to the center of the vessel due to fast heat exchange through the vessel wall during pressure build-up, (ii) depending on the distance to the vessel wall, the rate of heat loss during the holding phase is different.
Figure 2: Temperature profiles (black lines) recorded during a HP treatment (initial temperature 15 °C; holding pressure 500 MPa; holding time 7 min) at different positions in a laboratory-scale, vertically oriented, single vessel system (0.5 L): radial center (bold, continuous line), near the vessel wall (thin, dotted line), between the latter (thin, continuous line). Pressure profile registered (grey, dashed line).
Nevertheless, some hurdles are associated with the direct monitoring of the temperature. To demonstrate temperature fields completely, temperature sensors should be positioned across the whole volume of the HP vessel. Up to now, only wired systems are available. Because of the high pressures involved, this requires special attention to the sealing of the thermocouples’ passage through the vessel wall. It is important that the thermocouples do not affect the free movement of the flow (e.g. the pressure medium, food product) inside the vessel. Due to these requirements, the direct monitoring of the temperature at different coordinates inside a high pressure vessel becomes technically too complex on an industrial scale.
Use of a pressure-temperature-time indicator (pTTI)
Figure 3: To overcome the problems related to the direct temperature monitoring, the use of small, wireless pressure-temperature-time sensitive devices has been put forward for demonstrating the thermal non-uniformity in a high pressure vessel.
Such devices should ideally show a pressure-temperature-time dependent, irreversible read-out upon treatment, which should, preferably, be easily and accurately measurable. Furthermore, the sensors should not disturb the actual process, e.g. by influencing the heat transfer. Finally, in order to be used for process impact evaluation on a specific target attribute, the pressure-temperature sensitivity of the indicator should match as closely as possible the pressure-temperature sensitivity of the target (Van der Plancken et al. 2008).
Since many enzymes are characterised by a pressure-temperature-time dependent and a (mostly) easily measurable loss of activity, these biomolecules show great potential to be used as a pressure-temperature-time indicator (pTTI) for HPP. In addition, many enzymes display a pressure-temperature sensitivity similar to microorganisms, which generates the possibility for process impact evaluation. For the development of an extrinsic, isolated pTTI, a stepwise, kinetic approach with corrective feedback actions can be employed as depicted in Figure 4, starting with the selection of a candidate indicator system based on previous know-how (e.g. literature data) (Step 1), followed by a screening study on the pressure-temperature sensitivity (Step 2). If the pTTI shows a clear process response and still detectable read-out in the relevant pressure-temperature domain, the indicator’s kinetics are calibrated under isobaric-isothermal conditions (Step 3). At Step 2 and 3, solvent-engineering (purposively changing the solvent conditions to obtain the desired indicator characteristics) can be used as a corrective feedback action. Before the system can actually be used, the kinetic model has to be validated under dynamic pressure-temperature-time conditions, similar to those in industrial conditions (Step 4). In a final stage, the indicator is optimized further and can be implemented in several applications (Step 5) (Grauwet et al., 2009). For example and as explained above, a pTTI can
Figure 4: Stepwise approach for the development of an extrinsic, isolated pTTI. The dashed lines illustrate potential corrective feedback actions (Grauwet et al, 2009).
be used for process impact evaluation on a specific target attribute. In addition, a pTTI can be a useful tool in translating process impact from one HP equipment design to the other. In the literature, many different equipment designs are used, leading to the results reported. Depending on the characteristics of these designs (vessel volume, orientation, type of pressure medium, thickness of the vessel wall, …) however, it is very likely that, for a particular pressure level, build-up rate and initial temperature of the pressure medium and vessel wall, different temperature gradients are playing. As discussed above, different temperature gradients can have an effect on the process impact. Obviously, this knowledge is important in the context of scaling up.
Finally, and as it is exemplified in Figure 5, such a temperature sensitive device can be used to demonstrate temperature uniformity inside a HP vessel. In Figure 5, the potential of a Bacillus subtilis α-amylase based pTTI to demonstrate temperature non-uniformity is shown (Grauwet et al. 2009). By positioning the sensor at different axial and radial positions (Figure 6 A and B, respectively) in a vertical, single vessel system, it was demonstrated that indicators located at the bottom of the vessel and more closely to the vessel wall are less affected (Figure 5 A and B, respectively).
Figure 5: Effect of the axial (A) and radial (B) position inside a vertical, single vessel system (500 mL). Residual activities (A/Aunt, %) of Bacillus subtilis α-amylase (1 g/L – MES 0.05 M pH 5.0) treated at 500 MPa for 2 min at different initial temperatures: 20 °C (unfilled); 25 °C (filled); 30 °C (shaded) (Grauwet et al. 2009).
Figure 6: Positioning pTTI’s at different axial (A) and radial (B) coordinates in a lab-scale, vertical-oriented, single vessel system (500 mL; cylinder-piston) to investigate the process uniformity. For this specific experimental set-up, the first observation can be attributed to the injection stream of cold pressure transmitting medium coming from the bottom of the vessel and the downwards directed flow of cooled pressure medium close to the vessel wall. The second observation was explained by the pronounced compression heat transfer during compression and pressure holding time from the center of the pressure vessel to the colder vessel wall. Of course, detection of a temperature history at a given coordinate using an indicator will only be possible for processing conditions that are coupled with a distinct temperature sensitivity of the pTTI. In indicator development, a balance has to be found between the system’s detection limit and its sensitivity to small changes in processing conditions. For the former, the kinetics of the indicator should be sufficiently slow in order to maintain a detectable read-out within the timeframe of the process. On the other hand, changes in process parameters (p,T) should affect significantly the rate of changes of the indicator to allow discernable read-outs (Van der Plancken et al. 2008). By analogy with direct monitoring of the temperature, the exact positions of the indicators have to be known in order to represent the temperature distribution correctly.
Numerical simulation of the non-uniformity of high hydrostatic pressure processing
Figure 7: Numerical simulation features the advantage that it is able to calculate the complete temperature and velocity fields within the vessel and food product and not only values at distinct points. Based on this information, the pressure and temperature dependent impact distribution of the high pressure process can be obtained.
The simulations are based on the conservation equations of mass (3), momentum (4) and energy (5) and the transport equation of chemical substances (6) (Delgado et al., 2008).
Herein the variable ρ names the density, t the time, ui, uj, uk the velocity, xi, xj, xk the Cartesian coordinate, p the pressure, τij the stress tensor, μ the dynamic viscosity, δij the Kronecker delta, gj the gravity, ht the total enthalpy, (ρi)A the surface based heat flux density, ρv the volumetric heat sources, w the mass fraction of chemical substances, D the diffusion coefficient and S the source term of production/ destruction of chemical species.
In high pressure processes, the numerical simulation computes, based on these balance equations, the temperature changes due to added work of compression and conductive and convective heat transfer processes. This is coupled to the calculation of thermofluiddynamic phenomena such as the resulting free and forced convection. The transport of the fluid through regions of different temperatures results in a treatment history of the fluid (e.g. the food) during the process. The effect on the process impact is simultaneously described by the transport equation of the safety and quality attribute in question. However, the numerical simulations need experimental information about the pressure and temperature dependent thermophysical properties (i.e. thermal conductivity, viscosity, density, thermal capacity) of the treatment media (e.g. pressure transmitting medium, food product). In industry, water is commonly used as the pressure transmitting medium. Databases (e.g. the International Association for the Properties of Water and Steam, National Institute of Standards and Technology) of the thermophysical properties of water under high pressure are available. Furthermore, several studies have been carried out to determine the thermophysical properties of a range of food (model) systems, ranging from oils, meat, vegetable and fruit products to eggs. Nevertheless, the knowledge on the combined effect of temperature and pressure on the thermal and physical properties of food systems is still far from complete. Therefore, model validation of the numerical simulations is based on experimentally obtained results in the specific food products. For this purpose, the above mentioned techniques in Figures 1 and 3 have to be applied. Vice versa, the validation of the kinetic models under dynamic pressure and temperature conditions of the pTTI’s includes numerical simulations. In addition, numerical simulation demands high computational power and is case-dependent, thus requiring re-evaluation if, for example, the load of the pressure vessel is changed.
Numerical simulations prove to be an essential technique for process design, optimisation and evaluation (Denys and Hendrickx 1999, Forst et al. 2002, Werner et al. 2007, 2008) as their results provide direct access to the process uniformity during HPP and based on this measures for uniformity improvement and process control can be proposed and checked (Hartmann and Delgado 2002, Baars et al. 2007; Rauh et al. 2009). As an example of this predictive power, the numerical simulation of a water-based food model in a cylinder-piston high pressure system is presented. The simulated process features a very fast pressure increase up to 700 MPa (400 MPa/s) (initial temperature 50°C) with a pressure holding time of 2 min. During the pressure build-up, the temperature increases as much as under adiabatic conditions in almost the whole vessel volume, except regions directly at the wall. In contrast, during the holding time, temperature non-uniformities arise systematically. Downwards directed flow of cooled pressure medium close to the vessel wall and upwards directed in the center (Figure 8) explain the development of significant thermal layering until the end of the holding time (Figure 8). The effect of this non-uniform temperature distribution field on the process impact on the inactivation of an enzyme β-glucanase is demonstrated in Figure 8 (middle). Simultaneously with its inactivation, the enzyme catalyses the degradation of β -glucan (Figure 8 right). Because of the free convection in the vessel and the synergetic effect of pressure and temperature on the kinetic of β -glucanase in the studied processing condition, the simulated temperature field (Figure 8 left) results in a maximum of the residual activity (Figure 8 middle) and the concentration of formed product (Figure 8 right) in the center of the vessel.
To extend these simulations to industrial processes, one should also model the effect of the presence of the product on the temperature field and take into account the equipment design (piston versus injection system; horizontal versus vertical positioned vessel).
Figure 8: Numerical simulation of high pressure treatment of water-based food. Influence of temperature distribution (left) (ΔT = T-T0; ΔTad= Tad-T0; T0 initial temperature, Tad temperature after compression under adiabatic conditions) and fluid flow (arrow) on uniformity of inactivation of enzyme β -glucanase (A residual enzyme activity; A0 initial enzyme activity) and degradation of β-glucan ([P] product concentration; [Pmax] maximal product concentration) in a cylinder-piston high pressure system with a very fast pressure build-up (400 MPa/s) at the end of the pressure holding phase of 2 min at 700 MPa (T0=50 °C).
Strategies for Obtaining Homogeneous Conditions of Pressure and Temperature
It would be desirable to have homogeneous conditions of pressure and temperature during HP treatments to guarantee the quality of products from an organoleptic and safety point of view. Different suggestions have been made in the literature, such as equipping the vessel with a stirrer stimulating the heat exchange. However, this would be technically too difficult under pressures higher than 300 MPa. Second, isothermal conditions can be reached by using a slow pressure build-up rate allowing a fast transfer of the compression heat to the high pressure vessel wall. Nevertheless, in commercial applications, a controlled, slow pressure build-up rate is not straightforward as it is difficult to control. In addition, this slow pressure build-up is not interesting from an economical point of view (Tonello 2009). Otero et al. (2007) studied the effect of the vessel filling ratio on temperature heterogeneity. When the filling ratio is reduced, thermal re-equilibrium is reached sooner. Of course, the economical feasibility of the process decreases. Hartmann et al. (2004) noted that the use of low viscosity liquids led to a substantial decrease in temperature non-uniformity. They also suggested the thermal insulation of the HP chamber inner wall as the key to high degree uniformity of an efficient process cycle. When high pressure-high temperature conditions are intended, it is suggested to minimise temperature gradients by pre-setting the temperature of the incoming pressure medium and the temperature of the high pressure vessel wall at different temperatures. In particular, the temperature of the vessel wall could be pre-set at the temperature that the pressure medium attains at maximum pressure. This results in limited gradients, near-isothermal conditions and thus in a uniform impact on the food product (Rauh et al., 2009). Today, industrial equipment to be used in high pressure-high temperature conditions with control of both the temperature of the vessel wall and the incoming pressure medium are being developed (Tonello 2009).
In order to allow optimisation and control, but also viability and market acceptance of a food processing technology, adequate evaluation of the process impact on both safety and quality attributes of foods subjected to a specific pressure-temperature-time profile is essential. If the temperature sensitivity of the impact on targets (e.g. safety, quality) is significant, it is very likely that this temperature inhomogeneity results in a non-uniform impact field. In high pressure pasteurisation applications, pressure is the predominant process variable. When studying the kinetics of change of target attributes under high pressure-high temperature conditions, however, temperature has proved to become a determining variable. In this paper, different methods to ‘picture’ the temperature field inside a high pressure vessel are discussed with regard to their principle, major advantages and disadvantages. By developing these methods, one is not only able to determine the temperature field, but also to detect ways to discontinue temperature gradients during processing by varying different process factors or by changing equipment design. Due to these ongoing developments, uniform temperature distribution in high pressure-high temperature applications start to become a future reality.
This study has been carried out with financial support from the K.U.Leuven and the Commission of the European Communities, Framework 6, Priority 5 ‘Food Quality and Safety’, Integrated Project NovelQ FP6-CT-2006-015710. For more information on this project, aiming to remove hurdles to the application of novel food processing methods, the reader is referred to www.novelq.org.
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A shorter version of this article has been published in New Food Digital (www.newfoodmagazine.com), Issue 2, 2009: High hydrostatic pressure processing uniformity in the picture, by Tara Grauwet, Cornelia Rauh, Iesel Van der Plancken, Liesbeth Vervoort, Antonio Delgado, Marc Hendrickx and Ann Van Loey.
Tara Grauwet was selected as the 1st prize winner of the Charles R. Stumbo Student Paper competition at the 28th Annual Conference of the Institute for Thermal Processing Specialists (IFTPS), 4-5 March 2009, San Antonio, Texas, USA, for her paper entitled 'Investigating the potential of Bacillus subtilis alpha-amylase as a pressure-temperature-time indicator for high hydrostatic pressure pasteurisation processes'.
Tara Grauwet, Iesel Van der Plancken, Liesbeth Vervoort, Marc Hendrickx and Ann Van Loey are with the Laboratory of Food Technology, Leuven Food Science and Nutrition Research Centre (LFoRCe), Department of Microbial and Molecular Systems (M²S), Katholieke Universiteit Leuven, Belgium (E-mail: email@example.com); Cornelia Rauh and Antonio Delgado are with the Institute of Fluid Mechanics, Friedrich-Alexander University, Erlangen-Nuremberg, Germany.