Food Process Engineering: The Last 25 Years ... - Wiley Online Library

Food Process Engineering: The Last 25 Years and Challenges Ahead S. Bruin, Th.R.G. Jongen

ABSTRACT: In the first part of this contribution, an overview is given of some of the main developments in food process engineering in the last 25 years of the 20th century. This overview is, of course, colored by the personal experience of the authors, but a sincere effort was made to maintain a general perspective. Topics that will be briefly discussed are: progress in understanding how to control food microstructure formation during processing, separation processes, conversion processes and stabilization processes, progress in flavor technology and understanding of flavor retention during processing and release. In the 2nd part, in our view, the most exiting future developments are briefly discussed. The major items here are: processing requirements for functional foods, integrated process design approaches, application of novel ‘fields’ in food processes, ‘precision’ processing, supply chain approaches to food manufacturing, and more.

Introduction Bernard E. (Bernie) Proctor was the first Department Head of the Food Technology Department at MIT, starting in the 1940s until his passing in 1959. He was one of the charter members of the IFT and served as President from 1952 to 53. He maintained a cutting edge portfolio on food research and set challenging educational standards for the field of food technology. He placed heavy emphasis on integrating industrial microbiology and nutrition into the chemistry and engineering core curricula at MIT. It is an honor for us to present a lecture dedicated to this pioneer on the occasion of the 25th anniversary of the Food Engineering Division of IFT. Food process engineering has seen many developments over the last 25 years. In 1976, the area was just beginning to be established: professional societies started to establish sections or divisions that concentrated on the field, scientific journals devoted to food process engineering started to emerge, early textbooks began to be published (Earle 1966; Charm 1971; Helman 1975; Karel and others 1975; Leniger and Beverloo 1975; Loncin and Merson 1979) and curricula for academic training were beginning to get standardized (Cantarelli and Aylward 1978; Bruin and others 1984).

in the food industry has increased strongly. This is still an ongoing process. The speed of product innovation in the food industry has changed dramatically. The half-life times of product development has decreased from 10 years in 1970 to an estimated 2 years in the year 2000 (see Figure 1). This means that the high bonus on being first with a product innovation of substance has become increasingly difficult to achieve. Speeding up the product/ process development cycle is, therefore, of paramount importance. In the 70s, the emphasis in the food industry was on improving manufacturing and efficiencies. Moving from batch to continuous processing, improving process reliability, reducing waste products, and effective energy utilization were important is-

The food industry itself has seen many changes over the last 25 years

Many mergers and acquisitions took place, so the size and focus of companies 42

Figure 1—Acceleration of innovation time

COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY—Vol. 2, 2003

Figure 2—From “Make” to “Service” to “Care”

sues. In essence, the emphasis was on making products in a profitable way: ‘Make’. The foodservice and catering businesses have seen impressive development, taking care of the consumer trends to ‘eating out’ and ‘eating on the hop’. The food industry focusing on ‘Service’ emerged strongly during the ‘80s. During the late ‘90s, a phenomenon appeared in the fast moving consumer goods business that is indicated with ‘Care’ business (see Figure 2). Instead of making the goods, or providing a single service, a total package of care is delivered to the consumer consisting of various products and services that are made by co-packers or other third parties. This is also happening in the food industry. We will come back to this later. The effective speed of electronic computation by combined hardware and soft© 2003 Institute of Food Technologists

25 years of food process engineering . . . ware development has roughly doubled every year over the past 30 years (Amundson and others 1988). On April 1, 1974, Intel had just introduced the 8080 microprocessor with a clock speed of 2 Mhz and 64 Kb addressable memory for use in traffic light control, and the Altair computer, the first PC. In March 2001, Intel introduced the Pentium III processor with a clock speed of 1.0 GHz and 64 Gb addressable memory for use in portable PCs. As far as we know, this acceleration in computing chip power is expected to continue over the coming decade (see Figure 3 from Moore [Intel 2002]). Similar things can be said about hard-disk-drive capacities. Experts say that around 2010, the magneto-resistive storage technology currently used will have reached a limit with a storage density of 10 to 15 Gb/cm2 against about 0.15 Gb/cm2 today. This means an increase with a factor of about 100 over 15 years (Scranton 1997). After 2010, holographic memory technology may substitute the magneto-resistive technology with potential storage densities of 150 Gb/cm2. Even manipulating individual atoms can be envisioned; that could take storage to even higher densities. These developments have had and will continue to have a profound impact on the way in which process design and manufacturing control will be carried out in any industry, including the food industry, during the coming decade (Bruin 1997). In our presentation, we will first give some highlights of the past 25 years of food-process engineering. Next, we will look at current trends, followed by a more speculative view on possible long-term developments. In particular, we try to discuss the impact that such computer applications are having on process design, process control, and manufacturing in the food industry, identifying advantages and limitations, issues and opportunities. Of course, these views are limited and biased by our personal professional experiences. What has been done: Main achievements of the last 25 years, from Make to Service

In the past 25 years, several new books or updated versions appeared on food process engineering (Heldman and Singh 1980; Toledo 1980; Singh 1996; Fryer and others 1997), including 2 Handbooks (Heldman and Lund 1992; Valentas and others 1997) that summarize much of the progress up to about 1995. Our overview today does not claim to be at all complete; for instance, we will not discuss the important topic of packaging, but instead will cover areas with which we have been associated one way or another. We will use a typology of food processes that is ex-

ration, and glass transitions are essential tools to create microstructure. We highlight some of these in the following sections. Crystallization

Figure 3—Moore’s Law, probably valid until 2017

plained in Appendix A to organize our discussion in this section.

Structuring Processes The man-made structured foods use assembly or structuring processes to build product microstructure. Examples are crystallization, emulsification (for example, margarine, ice cream, sauces, mayonnaise), foaming (for example, whipped cream), extrusion, kneading of dough, baking, and so on. The end product has a complicated multiphase microstructure held together by binding forces between the various phases. This microstructure leads to desired product texture. The mouthfeel related to this texture and to its destruction during mastication is the key to final product quality and appreciation by the consumer. Apart from requirements on microbial stability and delicious flavor, the control of the microstructure of manmade structured foods is the key quality-determining factor. Arguably, this is also the most important category of food products in terms of size of the industry and contribution to turnover of the food industry. In the chemical industry, there are similar product technologies; for example, paints, rubber, plastic composites, agglomerated powders, extruded products, and foams. It is an area where traditional chemical engineering research and teaching, relatively speaking, have not spent much attention (Villermaux 1996). In the past 25 years, substantial progress has been made in the understanding and control of product microstructure, and new ways of achieving them have developed. Some examples are Aguilera and Stanley (1990): extrusion, cooking-extrusion, freeze alignment for protein texturization, and phase inversion processes for low-fat spreads. Phase transitions such as crystallization (ice, fats, carbohydrates), precipitation and liquid-liquid phase sepa-

At Unilever, we have considerable interest in oils and fats. Control of melting and solidification behavior of edible oils and fats is essential for manufacturing products such as margarine, reduced-fat spreads, ice cream, and chocolate. Let us take the example of margarine. As we see in Figure 4, the fat crystal network forms the backbone of margarine. Up until 1990, mainly empirical calculation methods, with limited applicability, were available (Haighton 1976). An important reason was that fats consist of hundreds of different triacylglycerols that can crystallize in 3 forms or modifications: the unstable a-modification, the metastable b’-modification, and the stable b-modification. There are further sub modifications that we will not discuss here (De Jong 1980). At Unilever Research, we have embarked on a program to develop a simple but sound thermodynamic footing to the description of the solid-liquid phase equilibrium for all 3 polymorph forms of fat crystals (Wesdorp 1990). It turns out that liquid mixtures of triglycerides behave as ideal solutions, but the solid phases need to be described with an excess Gibbs free-energy model, for which the Margules equations were used. In Appendix B we give more details on how this was done. Glass transitions

Although the notion of a polymer science approach to glass transitions in foods dates back to about 1965 (White and Cakebread 1966), the past 25 years have seen an intensified research effort by food scientists on glass transition phenomena.

Figure 4—Microstructure of a typical margarine; a fat crystal network of plate-like crystals holding oil and water phase droplets as the dispersed phase. The picture shows (w) the round shape of a water droplet surrounded by a shell of crystals that stabilize the droplet (Pickering stabilization).

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CRFSFS: Comprehensive Reviews in Food Science and Food Safety

Figure 5—Relations between Glassy and Rubbery States (Karel 1993)

models can be divided in models for single-screw and twin-screw extruders. Twinscrew extruders come in 2 versions: co-rotating screws and counter-rotating screws. Models for these various equipment configurations have been developed for newtonian and non-Newtonian rheologies (Janssen 1996; Bruin and others 1978; Harper 1981; Levine 1992; Jager 1991). Kokini and others (Wang and Kokini 1995; Kokini and others 1995a, b; Morales and Kokini 1999) at Rutgers/CAFT have developed the use of state diagrams, as shown in Figure 7a, to characterize the total process history in extrusion, Figure 7b, as well as sound rheological approaches to develop constitutive equations for food proteins (gluten, soy) and polysaccharides.

Figure 7a—State diagram of glutenin (Kokini and others 1995)

Emulsification

Research groups such as Levine and Slade at Nabisco; Karel, Roos, and Kokini at Rutgers; Labuza at Minnesota; and the Unilever team at Colworth (Lillford, Ablett, Izzard) have strengthened the notion that foods can be seen as polymer systems with water as the plasticizer in which glassrubber transitions determine many processability issues and product properties. Figure 5 gives the interrelationships between various states of a biopolymer as given by Roos and Karel. Figure 6 is a state diagram for biopolymers by the same authors. In particular, Karel and Roos (Karel 1993; Roos and Karel 1991, 1992; Roos 1995a, b) showed that collapse or shrinkage of dehydrated porous materials, crystallization kinetics of lactose, dehydration processes, and flavor retention in freeze drying are dependent on the glass transition phenomena, while Levine and Slade (1992) and Ablett and others (1986) showed the importance to baking processes. Extrusion

Extrusion and cooking extrusion are 2 structuring processes that have seen quite a bit of development over the last 25 years. The use of single-screw extruders to cook and expand corn and rice snacks dates back to 1946 in the U.S.A. Although modern control techniques help to regulate the mass flow in single screw extruders, it is, in many cases, advantageous to use extruders with better mixing and steadier flow characteristics. In the mid-70s, the use of twin-screw extruders for the combined process of cooking and forming of food products was introduced. The advantages were mainly that a more even mass flow could be achieved and, probably related to this, extruders that could scale up from a small size were more consistent (Van Zuilichem 1992). Modeling studies of extruders were done by many authors. The 44

Many structured foods are emulsions. Therefore, control of emulsion formation and its modelling have had much attention. Walstra (1983, 1993) gave an overview of the formation of emulsions in various types of flow conditions as encountered in equipment used for emulsification. Narsimhan (1992) gave an overview of the forces between droplets, mechanisms of flocculation, film drainage between colliding droplets, coalescence, and emulsion stabilization mechanisms. In the late 80s and early 90s, 2 consecutive BRITE/ EURAM projects were executed in which ICI, Unilever Research, Imperial College in London, the Institut National Polytechnique de Toulouse, and Computational Dynamics participated. The project objective was to develop validated predictive models for optimal design of multiphase chemical processes (BRITE/EURAM 1985). A CFD code was developed for gas-liquid (aeration) and liquid-liquid (emulsification) dispersion processes. A 2-phase turbulence model based on a k-e 2-equation eddy viscosity closure was implemented and tested experimentally. Appendix C gives more details about this model.

Figure 6—Example of State Diagram (Karel 1993)


Figure 7b—State diagram of glutenin, Kokini and others (1995). A typical extrusion process path is indicated. The following steps are indicated: (1) Wetting, (2) Heating and mixing, (3) Expansion, (4) Drying, (5) Cooling.

For emulsification, the real mechanisms of drop break-up and coalescence in various flow regimes and flow properties of dispersed and continuous phases are now developed up to a state where they can be plugged into a model for an ‘emulsifying unit’ (De Bruijn 1989; Janssen and others 1994, 1997). Figure 8 shows 3 different ways droplets can break up. Figure 9 shows the relations between the critical capillary number Vcr and the viscosity ratio of dispersed and continuous phase (md/ mc) for various types of 2 dimensional flow characterized by the parameter a. Figure 10 shows the relation between the value of a and the type of flow. The use of membranes to generate emulsions started in Japan (Katoh and others 1994) and has been studied by Schubert at Karlsruhe (Karbstein and Schubert 1995, Schroeder and others 1998, Schroeder and Schubert 1999), Trågardh at Lund (Joscelyne and Trågardh 1999), and Boom at Wageningen (Abrahamse and others 2001). Advantages are claimed to be lower energy consumption, more uniform dis-

25 years of food process engineering . . . persions, easy scalability and needs for less surfactant. Both W/O and O/W emulsions have been studied. Biopolymer mixtures

The ability to control formation of microstructure to obtain the right end-use properties of a product is the key to successful product design. For instance, the more successful low-fat products currently on the market (for example, 40% fat or 20% fat spreads) obtain their structure and texture from a mixture of biopolymers such as proteins and carbohydrates, which exhibit phase separation. This phase separation and the way it comes about as a contest

between aggregation/gelling mechanisms and spinodal decomposition mechanisms, Cahn-Hilliard (Cahn 1965), is very important since it allows the creation of product microstructures, which are required to mimic fat-like properties. Although phase separation is a phenomenon controlled by thermodynamics, in many food-processing regimes the microstructures can also be trapped into nonequilibrium microstructures (Clark 1992, 1995; Aguilera and Stanley 1990). The physical chemistry of these phenomena is well developed in polymer science and in biopolymer science, for instance, for sodium alginate-sodium caseinate-water mixtures (Suchkov and others 1981) and maltodextrin-gelatine-water mixtures (Kasapis and others 1993a, b, c, d). Appendix D gives an example of the maldextrin-gelatine-water system. However, the underlying chemical engineering science, for example, the influence of shear and systematic insight in scaling up rules in this area, are less well developed up to now. Villermaux (1996) indicated similar is-

sues in the manufacture of formulated products in the chemical industry and indicated the development of what he called ‘Formulation Engineering’ as a rich area for chemical engineering research. Recently, molecular-thermodynamic models for aqueous 2-phase systems containing polymers, electrolytes, and proteins have been developed that could give more accurate descriptions of such phenomena. For instance, the model developed by Haynes and others (1993) is very interesting. It is based on McMillan-Mayer solution theory with the generalized meanspherical approximation to account for electrostatic forces between unlike ions and the Boublik-Mansoori equation of state for hard spheres coupled with the osmotic virial expansion truncated after the 2nd-virial terms for short-range forces could be a good candidate. This model was used to predict liquid-liquid equilibria, protein partition coefficients, and electrostatic potentials between phases for both polymer-polymer and polymer-salt aqueous 2-phase systems.

Figure 8—Three types of droplet breakup. Top: binary breakup occurs at relatively low shear above critical capillary number V. If V is much higher, breakup in more than 2 drops occurs. Middle: relaxation when shear is suddenly stopped leads to droplet breakup. Bottom: ‘tip streaming’ when high surface active components are present.

Figure 9—Regimes of droplet breakup for different 2-dimensional flow types varying from simple shear (a = 0) to pure elongational flow (a = 1). Plotted is the capillary number against the viscosity ratio of dispersed phase and continuous phase.

Figure 10—The flow parameter a characterizes flow type from pure elongational flow to simple shear. Vol. 2, 2003 —COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY 45

CRFSFS: Comprehensive Reviews in Food Science and Food Safety Fat replacement

In the 80s, the food industry saw an enormous increase in the consumer demand for low-fat products that led to significant research efforts in fat replacement in the food industry and the food ingredients industry (Glicksman 1995). Figure 11 shows that there are 3 ways of replacing fat. Most of the emphasis was on using water and reformulation of the product to keep acceptable mouthfeel or to use a ‘functional filler.’ These functional fillers are protein based, polysaccharide based, or synthetic compounds. Protein-based substitutes are based on milk or egg proteins where small particles are formed under heating and shear (Simplesse®, Trailblazer®). Polysaccharide-based functional fillers are polydextrose, corn maltodextrins (Maltrin®), tapioca dextrins (N-Oil®), or potato maltodextrins (Paselli SA2). The most advanced of the synthetic compounds is undoubtedly sucrose esterified with fatty acids, or sucrose polyesters (SPEs) developed by Procter and Gamble

in their Winton Hill Technical Center. Of the 8 OH-groups in the sucrose molecule, 6 or more are esterified with fatty acids from any edible oil. Procter and Gamble (Lawson and others 1997; Peters and others 1997; Bergholtz 1992) called this ingredient Olestra and got FDA clearance for use in crisps, crackers, and tortilla chips (CFR172.867 1996). A problem associated with the ingestion of SPEs is that, since they are not absorbed, ‘extract’ lipophylic compounds from the contents of the lower digestive tract can also lead to so-called ‘anal leakage’ or ‘oil-incontinence’. The Bernhardt patent (Bernhardt 1987) describes a way of overcoming the 2nd problem by matching rheological behavior of an SPE by admixing to a pseudoplastic rheology with a yield stress. Adding fat-soluble vitamins to SPEs solved the 1st problem, but our own research in Unilever showed that this is not effective (Weststrate and Van Het Hof 1995), and we reported this at the IFT Annual Meeting in Chicago in 1993. The crystallization behavior of SPEs is even more complicated than that of

Figure 12—An amphophylic molecule in water (monoglyceride). The head tries to surround itself with water as much as possible, while the tail is uncomfortable in water.

triglycerides. An important reason is the huge number of isomers that exist even when conversion of the esterification is very high. See Appendix E for details. Other synthetic fillers are the so-called ‘retrofats’, which are esters from polycarboxylic acids (for example, malonic acid, tricarballic acid) with long-chain fatty alcohols. Liquid crystalline phases as microstructure forming agents

Figure 11—Alternative ways for fat replacement in foods 46


A final example of application of phase equilibrium behavior in design of food products microstructures is the use of lipogels in zero/very-low-fat spreads. These products are margarine-like spreads with a fat content of about 3%. As we have seen above, the microstructure of margarine consists of a network of plate-like fat crystals with a hydrophobic surface, enclosing oil, somewhat like a sponge. The idea was to replace this network by a similar one, but with a hydrophilic surface so it could retain an aqueous phase. The concept uses lamellar mesomorphic phases (La-phases) of monoglycerides (Figure 12) as the basic plate-like crystals and builds a network from these building blocks. When heating an aqueous solution of monoglycerides to about 60 °C, a lamellar phase structure is formed (Figure 13) that, when cooled below the transition temperature, transforms into a highly viscous a-gel phase. This a-gel phase is unstable: in the course of a few hours to a few days, this phase turns into a coagel phase in which the surfactant molecules

25 years of food process engineering . . . have crystallized to plate-like crystals with hydrophobic surfaces and are capable of enclosing large quantities of water. To obtain a high water-carrying capacity, surface-active compounds with a small charge are also built into the double layers, between the uncharged molecules. In this way, the water layers can be about 100-nm thick separated by lamellae of about 5 nm. Figure 14 shows an EM image of such a coagel with a striking resemblance to the structure of margarine, as shown in Figure 4. In the actual fat-free margarine Unilever has on the market in the U.S.A., this principle has been applied, while the water phase consists of a microstructure of a continuous phase with a micro-separated disperse gelatine phase along the lines of the previous section.

Separation Processes The second class of processes we discuss is the class of separation processes. Separation processes in the food industry are many and varied. In 1976, it was 5 years previous that Judson King’s textbook on separation processes was published (King 1971a), with many references and examples relating to the food industry. In the years to come, freeze-drying, freezeconcentration, membrane processes, and improving dehydration processes in general were on the agenda of many food research groups (Spicer 1974). Some examples are: Freeze-drying

King reviewed the state of the art in freeze-drying in 1971 (King 1971b). In this review, he gives a discussion on early work about the influence of freezing conditions of liquid foodstuffs (fruit juices, coffee extracts, and so on) on the success of

Figure 13—Liquid crystalline phases formed dependent on the shape factor P of the amphiphilic molecule. V = volume of the molecule, a0 = surface of the head group, and l0 is the length of the hydrophobic tail.

Figure 14—The EM image of a coagel shows a striking resemblance to the structure of margarine. While the fat crystals in margarine form a network enclosing oil, the monoglyceride crystals form a network enclosing water.

the subsequent freeze-drying process. This early work by, among others, Rey and Bastien, Quast, Flink, Labuza, and Karel was about the collapse of pores created by sublimation of ice during freeze drying and had much to do with vitrification/devitrification and, thus, with the glass transition temperature (T’g ) and the onset temperature of ice melting (T’m). One of the major benefits of freeze-drying is the relatively good retention of volatile flavor and aroma components with the process. In those days, there was intensive research on the mechanisms of aroma retention in freeze-drying in particular in the groups of Karel at MIT (Flink and Karel 1970a, b; Flink and Labuza 1972), King at UC Berkeley (Bellows and King 1973; Massaldi and King 1974), and Thijssen at TU Eindhoven in the Netherlands (Rulkens and Thijssen 1972; Kerkhof and Thijssen 1977). It looked initially that there were 2 competing concepts to explain retention: ● Selective diffusion mechanisms ● Micro region entrapment of aroma components It became clear that both concepts are very much related through the glass-rubber transitions theory combined with the WLF theory of prediction viscosity in glassy/rubbery state systems and the free volume theory of diffusion coefficients in such systems (Karel 1993; Kokini and Vildiz 2001). Coumans and others (1994) gave an overview of the theoretical and practical aspects of aroma retention in freeze-drying. Freeze concentration

Considerable effort has been put into

freeze-concentration research for liquid foods in the past 25 years. The main attractiveness of freeze concentration lies in the selectivity of water removal, the low thermal damage to the product, and potentially high retention of volatile aromas. Difficulties to be overcome in freeze concentration processes are the selective separation of the ice from the concentrated solution and the fact that the process is more costly than, for example, evaporation processes. Omran and King (Omran 1972; King 1974) gave a morphological analysis of alternatives for freeze concentration of food liquids, indicating many theoretical alternatives for the process. Research in freeze concentration has focused on 2 issues: (1) How to control nucleation and growth of ice crystals to get large ice crystals, preferably of uniform size. The advantage of large ice crystals is their low specific surface and therefore fewer losses of entrained juice concentrate. (2) How to separate ice crystals selectively from a dispersion with the concentrate. Omran and Stocking at the Univ. of California–Berkeley (Omran and King 1974; Stocking and King 1976) and Huige and Vorstman at TU–Eindhoven (Huige 1972; Huige and Thijssen 1969) studied nucleation rates and growth rates of ice crystals in sugar solutions and liquid foods. The bottom line of this work was that the growth of crystals at higher subcoolings takes the form of dendrites, in contrast to the disk-shaped crystals found at lower subcoolings. The changeover occurs at subcooling of about 0.10 K. This change in morphology suggests operating at subcoolings smaller than 0.1 K because of the favorable specific surface of the disk-shape crystals. Moreover, in this lower subcooling range, the nucleation rate was found to decrease with more than the 2nd power of subcooling. The growth rate is, however, controlled by heat and mass transfer to the ice crystals and, therefore, the growth rate decreases with about the first power of subcooling. The lower the subcooling, the more growth is favored over nucleation and the larger the ice crystals will be. The production rate, however, decreases with lower subcooling, so an optimum must be found. Maintenance of a very low subcooling can be achieved by using an adiabatic crystallization vessel fed with a stream containing very small nuclei/crystals that melt in the crystallization vessel because their size is below the GibbsThomson critical dia (Van Pelt and Jansen 1988). Figure 15 shows the spherical ice crystals obtained in this way in a commercial freeze concentration process. In addition, the nucleation rate is not


CRFSFS: Comprehensive Reviews in Food Science and Food Safety very dependent on the solute concentration, while the growth rate is significantly reduced at higher solute concentration because of the lower diffusion coefficient of water at higher solute concentrations. Therefore, a staged crystallization process where the less concentrated solution is utilized for much of the growth should lead to larger crystal size as well as a higher growth rate. This approach has been patented (Van Pelt and Roodenrijs 1984) and is applied commercially (Van Nistelrooij 2001. Separation of ice crystals from the final concentrated food liquid has seen very interesting developments. The original research of Thijssen’s group at the Eindhoven Univ. of Technology was on a pulsed counter–current wash column (Vorstman and Thijssen 1972, 1973). Later, a continuous version was developed (Thijssen and Van der Malen 1982). These counter current wash columns, in which a packed bed of ice crystals is washed with water obtained by melting ice crystals at one end of the column, gives very low losses of solutes: less than ppm or even ppb levels (Van Pelt and Jansen 1988). Today these wash columns are used in the food industry, in wastewater treatment (as a pre-treatment for incineration of waste streams), and in the chemical industry (for example, production of 99.9% pure paraxylene) (Van Nistelrooj 2001). A typical flow sheet is given in Figure 16. Drying

Dehydration (drying) of foods is one of the main operations in the food industry and has always been an area of research. In the Spirit of 1976, Labuza wrote a paper on the history of drying in the Americas (Labuza 1976) that is still worth reading. At that time, the main themes in drying research were: (1) Mechanisms of water transport and transport of volatile aroma components, (selective diffusion concept, Stefan-Maxwell diffusion formalism).

Figure 15—Spherical ice crystals in freeze concentration process (Photograph courtesy Niro Process Technology) 48

Figure 16—Flowsheet of single-stage freeze-concentration process (NIRO Process Technology)

(2) Modeling of conversion of nonvolatile components during drying (enzymes, degradation reactions) and changes in microorganisms and their metabolic processes. (3) Energy savings potential and pollution control of drying operations. (4) Tailor-made drying processes with more stages (spray drier fluid bed combinations) for specific products. (5) Attempts toward total modeling of spray driers (atomization, drying of drops, flow patterns in spray driers). The state of the art in the late 70s has been reviewed by King and Clark (1977) and by Bruin and Luyben (1980). The mechanisms of water transport in liquid foods based on a multicomponent diffusion approach have been established by now. Chandrasekaran and King (1972a, b) were the first to consequently use the ternary system approach, using the formalism from irreversible thermodynamics (or Stefan-Maxwell) for the individual fluxes of water and aroma component. Meerdink (Meerdink and others 1988, Meerdink and Van‘t Riet 1993) also used this formalism to describe drying of a ternary system of water and 2 nonvolatile components. See Appendix F for more details on the multicomponent diffusion fluxes of aroma components and water in liquid foods. Mechanisms of water transport in capillary-porous materials are much more complicated. In addition to liquid molecular diffusion, water transport by vapor diffusion, surface diffusion, Knudsen diffusion, capillary flow, purely hydrodynamic flow, and internal evaporation/condensation effects complicate the picture (Fortes and Okos 1980, Whitaker 1980). Figure 17a and 17b give an overview of the various modes of transport of water vapor and liq-


uid water in capillary-porous materials (Bruin and Luyben 1980). Crapiste and

Figure 17—(a) Mechanisms for vapor transport in capillary-porous materials, (b) Mechanisms for liquid transport in capillary porous materials (Bruin and others 1980)

25 years of food process engineering . . . others (1988) worked out mass transfer theory for drying of cellular materials and compared them with experimental results. Membrane separation processes

The situation on application of membrane processes in the food industry around 1976 was reviewed by Merson and Ginnette (1972; Delaney and Donnelly 1977). Membrane processes such as reverse osmosis, ultrafiltration, microfiltration, nanofiltration, and pervaporation have received much attention over the last 25 years (Cheryan 1986). Most of the applications involve the selective removal of water from a liquid food as a preconcentration process step. Other applications are to separate molecules on their size, for example, flavors, colorants, pectins, and protein fractions, to purify enzymes, to separate microorganisms, or to keep enzymes in a bioreactor while reaction products migrate through a membrane (Dziezack 1990; Wagner 1980; Cheryan 1977). Desalination of seawater by reverse osmosis has received huge attention for obvious reasons. In the juices and beverages industries, membrane processes have been introduced as process steps to preconcentrate, to remove undesirable components (tannins, polyphenol oxidase), and to adjust alcohol content in wine or beer. In the oils and fats industry, membrane filtration has been researched as a refining step to remove lecithin selectively from liquid vegetable oils. In particular, the dairy industry has looked intensively at ultrafiltration as a preconcentration step before curdling in cheese manufacture, concentration of skim milk and whey (Hiddink and De Boer 1980; Wagner 1980). Process engineering research has focused on modeling flux rates, concentration polarization and seeking ways to prevent it (Lopez Leiva 1979). A way to compare various preconcentration processes on their (bio) chemical quality degradation was given by Thijssen and Van Oyen (1977). The idea is that a certain time-temperature combination leads to certain quality degradation, dependent on the reaction rate constant and its activation energy. Taking 1st order kinetics, Figure 18 was derived, in which evaporation-, membrane-, and freeze-concentration processes were compared. Most chemical degradation reactions have activity energies in the order of 60 to 200 kJ/ mol. The graph shows that, for these situations, membrane-concentration processes and freeze-concentration processes will lead to lower damage than evaporative processes despite the longer residence times of these processes compared to evaporation in, for example, a centrifugal film evaporator.

Conversion Processes Reaction kinetics in food systems

Chemical reactions in food systems either are used to generate positive functionality (flavor development, tender cooked textures, baked crusts, shallow frying, deep frying) or occur as a by-product of processing with a negative impact on functionality (vitamin losses, adverse color, taste, and aroma changes). Villota and Hawkes (1992) gave an extensive overview of reaction kinetics aimed at supporting optimum process design. They reviewed kinetic data on deterioration reactions of vitamins, color substances, and nonenzymatic browning. Many of the kinetic data in foods often represent simplified reaction kinetics because of the complexity of the total picture. Modeling of complex reaction webs is beginning in chemical engineering and biochemical engineering. It seems timely to explore possible application in food process engineering, for example, Maillard reactions. Finally, we mention that coupling of reaction kinetics with the diffusional mobility must get more attention. The water concentration dependence of diffusivities of reactants will have a very strong influence on reaction rates, while also the reaction rate constant itself can be a function of the water concentration. The Damkohler-II or Hatta number values then determine reaction rates and concentration profiles of the reactants.

Biotechnology and bioconversions

In the early 70s, many chemical engineering departments and food science departments saw the opportunities in ‘biotechnology.’ The fantastic progress in rDNA technology (Cohen and others 1973), but also the oil crisis of 1973, had triggered the revival of interest in what used to be called Industrial Microbiology. Many jumped on this bandwagon by creating or extending research and teaching in biochemical engineering. In the food industry, biotechnology research groups were restructured to develop in-house rDNA capabilities, aiming both at microorganisms and at plant-biotechnology. Process engineering aspects of research in biotechnology were: (1) Transport phenomena in fermenters, like mass transfer of oxygen in aerobic fermenters of various geometry (Van’t Riet and Smith 1975; Nagel and others 1977), scale up-scale-down approach to optimization (Sweere and others 1987), 2-liquidphase fermenters (Lilly 1982; Van Sonsbeek 1992), rheology of biomass suspensions (Charles 1978), hydrodynamic conditions for pellet growth of mycelia (Metz 1976). (2) Modeling growth kinetics, from simple Monod/Michaelis-Menten kinetics via grey-box models to modeling with the essentials of the pathways included. In particular, yeasts (for example, for production of a-galactosidase [Verbakel 1991]) and lactic acid bacteria as potential ‘cell-factories’ got much attention.

Figure 18—Comparison of various preconcentration processes on thermal damage for different activation energies. Reference is 1st order kinetics with k (100 [C]) = 10–4[s–1]. Slopes for 4 activation energies are given. Equal thermal damage are lines with parallel slopes. For activation energies > 40 kJ/mol Membrane processes and freeze concentration give lower thermal damage than evaporation processes (Thijssen and others 1977). Vol. 2, 2003 —COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY 49

CRFSFS: Comprehensive Reviews in Food Science and Food Safety (3) Downstream processing to separate the desired components from fermentation broths (Somers and others 1989). (4) Starter culture processes that produce dry powders with high viability counts, for example, for cheese manufacture and sourdough processes for bread (Lievense and others 1992, Linders and others 1996). Membrane reactors for enzyme catalysed processes

In the last 15 years, solid-state fermentation got more attention for enzymes, aroma compounds, and biologically active secondary metabolites (Pandey and others 2000; Mitchell and others 2000; Nagel and others 2001; Han and others 2001). Interesterification of triglycerides catalysed by lipases in solvent-free processes (Luck and Bauer 1991a, b). In the late 70s and the 80s, public opinion turned against ‘chemistry’ or additives in foods, which led to an opportunity for more ‘natural’ foods with natural additives or endogenous functionality. These 2 trends are supported by opportunities offered by the spectacular advances in biotechnology and, in particular, plant biotechnology. The control of functionality of foods is shifting, as indicated in Figure 19, from synthesis of additives to raw material modification. The new knowledge circle will be based on knowledge in the food industry, biotechnology, and agriculture. Food process engineering should therefore also shift its attention toward raw material understanding and assuring mild process conditions that maintain or enhance endogenous functionality of raw materials. The big challenge for the food industry and agriculture/plant breeding is to better control functionalities that matter to the consumer: nutritional value, taste and aroma, structure, and color.

Stabilization Processes Thermal processing

Many food processes are built around heat-transfer processes. Thermal processing to stabilize foods is the mature workhorse of the food industry. Still, there is much work being undertaken in the following areas: (1) Investigation on fouling of heated internal equipment surfaces; (2) Modeling heat transfer, fluid flow and microbial inactivation and growth; (3) Measurement of liquid particle heat transfer coefficients in flowing systems; (4) Measurement of residence time distributions of complex fluid flows; (5) How to cool rapidly. One area of attention still is the question 50

Figure 19—Shift from nature-identical ingredients to indigenous functional ingredients

of whether the 1st order death kinetics with Arrhenius (or Z-value) temperature dependence is justified. One of the problems is that a microbial cell can become irreversibly nonviable (or ‘dead’) by a multitude of routes, such as the breaking of the cell envelope, denaturation of some protein or enzyme, or nucleic acid (Chase 1997). Tails and shoulders in plots of log (N/No) compared with time are often observed (Cole and others 1993, Chiruta and others 1997), and many explanations have been given, such as clumping of cells, lack of homogeneity in the population, change of resistance during treatment, and inactivation of a number of critical sites in the cell. There are various attempts to move models away from the log–linear (that is, 1st order reaction kinetics) behavior. Some of them assume an nth-order reaction, or simply a time-dependent reaction constant (Mattick and others 2001); others use log– normal (Smelt and Patterson 1999), logistic (Cole and others 1993), or log–logistic (Smelt and Patterson 1999) curves, which also imply a time dependent reaction constant. In Appendix G, we give some examples of how considering the death of microorganisms as a sequence of irreversible and reversible reactions can describe shoulders and tails in the plots of log (N/ No) compared with time. Since other contributors in this symposium will cover thermal processing, we will not discuss the progress made over the last 25 years in this area, but instead focus on the alternative stabilization processes that


are not yet widely used (Mertens and Knorr 1992): UHP Pasteurization and Sterilization, Pulsed electric fields, Natural antimicrobials. In 1998, The Food and Drug Administration signed a 5-year contract with IFT for IFT to provide scientific review and analysis of issues on food safety, food processing, and human health. In this context, a report on Task Order Nr 1 (“How to Quantify the Destruction Kinetics of Alternative Processing Technologies”) appeared as a supplement to the Journal of Food Science in 2000. Both UHP processing and pulsed electric fields processing are covered in this supplement. UHP pasteurization and sterilization

Experiments made at the West Virginia Univ. Agricultural Experiment Station in 1914 showed that high pressure will kill microorganisms (Bridgman 1949, Hoover 1993), and that different organisms differ greatly in their resistance to the action of pressure. In the same year, Bridgman found that: “. . . Egg white and proteins of meat may be coagulated by the action of pressure of 6000 kg. [bar] at room temperature or lower . . .” (Bridgman 1948, 1914). Ultra-high-pressure processing of foods has attracted much attention over the last 15 years or so. Pressures in the range of 103 to 104 bar can be used for preservation because microorganisms and enzymes can be inactivated (Cheftel 1992; Knorr 1993). The advantage, compared to

25 years of food process engineering . . . thermal treatment, is that nutritional and sensory quality (in particular, flavor and color) is only slightly affected by high pressure, although tenderizing of meat, gelatinization of cornstarch suspensions, and melting of gelatine gels have been reported. Process engineering issues in UHP processing are: More meaningful kinetic data (and theory) on UHP- and T- induced changes in foods. Data on inactivation of enzymes, microorganisms (Sonoike and others 1992; Smelt and Hellemons 1998; Wouters and others 1998), and spores (Reddy and others 1999; Mills and others 1998; Crawford and others 1996; Rovere and others 1996), color, and texture are needed in order to derive kinetic models. Recently Brul and others found that intracellular membrane damage is the most likely initial target in HP treatment of yeast cells (2000). Work on enzyme inactivation and color is underway in the group of Hendrickx at Leuven (Ludikhuyze and others 2000, 1999, 1998; Weemaes and others 1999a, b, 1998, 1997; Van Loey and others 1998; Indrawati and others 1998; Hendrickx and others 1998) and others (Heinisch and others 1995). Process design methods for thermal processes need to be extended to UHP/T processing. The concept of activation volume may be helpful here (Cheftel 1992; Tauscher 1995; Knorr 1997). If the temperature is not constant during the UHP process, and there is no reason to doubt this because of adiabatic heating, then the entropy term in the Gibbs free energy change needs to be incorporated. In Figure 20, typical death rate contours are given for Lactobacillus casei in a P-T diagram given by Sonoike and others (1992). Pulsed UHP is suitable for sterilization processes (Meyer and others 2000). Modeling the effect of a combination of pressure pulses and the resulting adiabatic heating and cooling on spore destruction and enzyme inactivation is an interesting topic. Equipment construction concepts to further reduce cost are needed (Meyer and others 2000; Zimmerman and Bergman 1993; Mertens and Deplace 1993). Farkas and Hoover (2000) summarized UHP in the JFS supplement mentioned earlier, while Smelt (1998) gave an overview of microbiological aspects of UHP processing. Pulsed electric fields

Pulsed electric fields (PEF) consists of applying short (microsecond) electric pulses of field strengths from 25 to 70 kV/cm to the product that is placed between 2 electrodes. Cumulatively 20 to 100 electric

Figure 20—Typical constant death rate contours are given for Lactobacillus casei in a P-T diagram given by Sonoike and others (1992). Death rate as defined in figure.

pulses are given in treating, for example, fruit juices. PEF is based on generating nonrecoverable pores in the membranes by dielectric breakdown. This phenomenon is known as electroporation. PEF technology can be used for the nonthermal inactivation of vegetative microorganisms. Whether the technology can also inactivate spores is still under investigation. PEF technology was recently reviewed by Jeyamkondan and others (1999) and by Barsotti and others (1999). Process engineering issues in PEF processing are: More validated kinetic data are needed on mechanisms of inactivation of microorganisms (Rowan and others 2000) and synergy with nisin as a natural antimicrobial that weakens the microbial cell membrane (Pol and others 2000; CalderonMiranda and others 1999). Prediction of microbial inactivation as a function of process conditions in the treatment chamber: voltage across the capacitors, number of capacitors, inter-electrode gap, number and frequency of pulses, food resistivity, and food flow rate (Wouters and Smelt 1997; Sensoy and others 1997; Martin and others 1997; Zhang and others 1995). The model of Holsheger and others (1981) is often used to relate the survivor number to treatment time (Jeyamkondan and others 1999). Peleg (1995) derived an alternative model in which no time dependence appears. (1) Process conditions for spore inactivation (Pol and others 2001) (2) Design of optimum treatment chambers (Qin and others 1995), conditioning, and postprocessing (Qiu and others 1998). Barbosa-Canovas, Pierson, Zhang, and Schaffner (2000) summarized PEF in the JFS supplement mentioned earlier. Natural antimicrobials

Instead of inactivation of microorganisms, inhibition of their growth can be uti-

lized as a preservation process, for example, chilling, freezing, lowering water activity, fermenting, lowering pH, adding organic acids, addition of enzymes and other proteins: lysozyme, lactoperoxidase, lactoferrin. Often more of these techniques are applied simultaneously in ‘combination preservation’ or ‘hurdle’ technologies. In the past 25 years, the concept of hurdle technology has been developed further from its empirical origins. However, the last 10 years have seen a shift toward more physiologically based approaches that aim at interfering with the homeostasis mechanisms that evolved in microorganisms in order to combat the effects of the environment in which they have to survive (Hould 1995). Many of the antimicrobial substances such as essential oils and related substances from oregano, thyme, dill, and chives tend to be hydrophobic and have greater or lesser membrane-disrupting properties, thus inhibiting growth (Brul and Coote 1999). Most antimicrobial peptides from nature have membrane disrupting and pore-forming properties; details of these effects are not clear yet except very recently for nisin (Breukink and others 1999).

Process Synthesis The primary goal of process design is to identify the optimal equipment units, the optimal connections between them, and the optimal conditions for operating them to deliver the desired product at optimum yields, at the lowest cost, with the highest production line efficiency and minimum waste generation. Two complementary types of activity characterize process design: process analysis and process synthesis (King 1974)(see Figure 21). In process analysis, an existing process is broken apart, usually through mathematical and/or empirical models of its constituent elements. The aim of the exercise, of course, is the recombination of the subsystem models into a total process model to predict the previously unknown performance or output of the processing system as a whole. These total process models are used as building blocks in “smart manufacturing” software systems discussed earlier. Process synthesis, on the other hand, implies the creation ab initio of the process itself, presumably from specification of the desired products, from the specification of desired inputs, such as raw materials and ingredients and from a basic notion of what sequence of treatments will be necessary in the process. In this area, considerable research activity is ongoing, in addition to research on how to adapt methodologies developed for the chemical and petrochemi-


CRFSFS: Comprehensive Reviews in Food Science and Food Safety cal industries to structured products such as foods (Meeuse and others 1999). The key concept of unit operations enabled chemical engineers to study processes independent from the particular branch of the chemical industry for which the process was intended. Of course, this concept has also been extended from chemical processes to food processes. Each of the 4 broad categories of processes mentioned earlier has a number of typical unit operations. A thing to keep in mind, however, is that there are more than 100 unit operations in food processing, excluding packaging operations (Farkas 1977). This number is considerably larger than in chemical processes where the number is, for example, about 30. Synthesis of food processes would likewise be simplified by reducing it to making an appropriate selection and sequencing of unit operations from the ‘standard repertoire’ and analyze the process created in this way using the process analysis mode. The drawback of using the unit operations ‘building block’ approach is, however, that opportunities for process innovation may be missed. This approach will, in fact, only be able to create processes that are new sequences, or networks, of existing unit operations. In the past 25 years, there have been a series of attempts to approach process synthesis in a more creative way that we will dwell on for a moment. Some early examples of systematic process synthesis for food processes are due to Judson King’s group at Berkeley that he summarized in his AIChE Institute Lecture of 1974. Earlier work included innovating freeze drying with Clark (Clark 1968, King and Clark 1968) by applying alternating layers of food and layers of desiccant in order to break the limitation of the very low water uptake capacity of a gas when used in convective freeze drying at higher pressures. In this way, a heat transfer limitation could be reduced by the fact that the gas is reheated in the desiccant layer by the heat of sorption of the water vapor taken up by the desiccant. This concept was generated by looking critically to the URIF model and by assessing limiting rate factors of freezedrying (Sandall 1968, Sandall and others 1967). Another example from the Berkeley group is the morphological analysis of freeze-concentration of liquid foods by Omran (1972, Omran and King 1974). In addition, an attempt to develop a computer based systematic synthesis of separation schemes for multicomponent mixtures stems from those days (Thompson and King 1972). The decisions incorporated into the programs are on algorithmic and heuristic levels. Between 1970 and 2000, much effort has been put in trying to develop a system52

Figure 21—Two complementary types of activity characterize process design: process analysis and process synthesis (King 1974)

atic framework for process synthesis in the chemical industry and pollution control. Some references are: (Rudd and Watson 1968; Rudd and others 1973; Mac Berthouex and Rudd 1977; Douglas 1985, 1988; Biegler and others 1997, Siirola 1996). Some examples for the food industry are also worth mentioning (Singh 1995). The generation of process alternatives in these methods is done in either of the following ways: (1) Superstructure optimization or algorithmic search method (2) Hierarchical decomposition (3) Heuristic assembly methods (4) Evolutionary methods The 1st method begins with defining a superstructure in which the optimum design is embedded. By mathematical optimization techniques, all configurational alternatives are searched and thus the optimum is generated, see Grossman (1996). This method seems less suitable for application in the food industry. The 2nd methodology decomposes the design problem into several smaller hierarchical design problems or levels. In each level, the entire process is considered while more detail is added to the design. The method was developed by Douglas (1985, Douglas and Stephanopoulos 1995). The underlying thinking in the methodology is, however, limited to the chemical industry because products are implicitly assumed to have no microstructure that is important to their functionality. However, it looks adaptable. This approach has been modified to processes that produce structured products such as a variety of food products (Meeuse and others 1999, Dhingra and Malone 2001). Figure 22 gives an indication of the 6 levels used in the methodology. In Level 2, the ingredients are grouped together in


composite streams that have specific functions in creating a microstructure. These composite streams then are combined dependent on a number of heuristics. (Heuristics are rules of thumb that, in fact, generalize on many observations. For instance, in selection of separation processes, some heuristics are: “Obtain large separation factors” or “Favor equilibrium processes over rate processes”. Other examples in dressings manufacture: “If emulsifiers cannot withstand high temperatures, do not add the emulsifier before process blocks that will use heat treatment”). These determine the connectivity of the structure blocks in the overall flowsheet. In Level 3 (Transformations and Tasks), for each structure block the transformations or functions needed to convert inlet streams into outlet streams have to be identified. This is done by morphological analysis. Then the functions are subjected to a functional analysis. Figure 23 explains what is meant with morphological analysis and functional analysis. It is in these 2 levels that creative new process designs can emerge. It is interesting to note that the community of practice in the area of Computer Aided Process Design see processes that produce structured products/materials as a key target for future research (Douglas and others 1999).

Transport Phenomena and Computational Physics The classical Unit Operations approach will only work when sufficient data are available. Necessary data are: thermodynamic properties, data on kinetic phenomena, values of transport coefficients, and rheological parameters. Either these data should be known or sufficiently reliable estimation methods must be available. Also sufficiently detailed models of the various unit operations must be available in terms of the conservation equations at the right level of integration. Basic data on phase equilibria, transport properties, and rheological parameters are usually quite incomplete and indeed not easy to determine experimentally, due to the very complex composition and microstructure of many foods and food ingredients. It takes a formidable effort for a food company to establish effective data banks and so on on such data for their key raw materials, ingredients, and the ‘process streams’ they are handling. An example from Unilever is the basic data for oils and fats and their crystallization behavior (equilibrium as well as kinetics) where we have made major progress over the last year but at considerable research expenditure, see, for example, Wesdorp (1999). The Handbook of Food Engineering (Held-

25 years of food process engineering . . . man and Lund 1992) gives a good basis of transport properties of foods, reaction kinetic data, and thermodynamic data (melting points of sugars, sugar alcohols, oils, and fats, sorption isotherm constants). Phase equilibria play a very important role in food technology, although this may not always be realized (Bruin 1999). Some examples where phase behavior plays a key role to obtain quality food products are: (1) Solid/liquid phase equilibria of triglycerides (see Appendix B) or water-controlled microstructure formation in aqueous mixed biopolymer systems (see Appendix D); (2) Liquid crystalline phases as microstructure forming agents as already discussed above; (3) Aroma retention and release from food products. Binary systems of water and nonvolatiles as carbohydrates, polyalcohols, and proteins can often be described with a Margules equation. Aqueous solutions of sugars up to 30% wt follow Raoults law; at higher concentrations it becomes necessary to incorporate an activity coefficient

for water. Le Maguer (1992) gave a good overview of the more recent developments in phase equilibria in aqueous food solutions. For carbohydrates in aqueous solutions, Le Maguer showed that the UNIQUAC equations with some modifications in defining the building blocks of carbohydrate molecules and the way to calculate the Rj and Qj values of the building blocks were remarkably successful in description of both the excess enthalpy and excess entropy of simple carbohydrate solutions (mono- and disaccharides). For electrolyte-containing solutions, the approach of Bromley (1973) is usually followed, that is, a corrected Debije-Hueckel equation. The equations of Cruz and Renon (1978) are attractive because they can be used for a wide range of ionic strengths, for partly dissociated electrolytes, and to volatile electrolytes. These equations are based on the Debije-Hueckel theory, the Born model for corrections for the decrease in the dielectric constant of the solution when the electrolyte concentration increases, and an NRTL contribution for the undissociated species in the solution. The extension of UNIQUAC

equations to ionic solutions by Christensen and others (1983) seems also to be very useful for the description of food solutions. The model of Xiao-hua Lu and Maurer (1993), which combines solvation equilibria with a Debije-Hueckel plus local-composition approach (UNIQUAC model) to describe deviation from ideal mixing, could also be of interest. Renon (1996) gave an overview of the predictive models for excess properties of electrolyte solutions for process design purposes. One should keep in mind that foods are edible, contain nonelectrolytes (carbohydrates, proteins, fats), and electrolyte concentrations are not high. Equations specifically developed for electrolyte-only solutions and/or high electrolyte concentrations are probably not required to describe the phase equilibria of food systems.

What’s Cooking: The Immediate Future On the road to Care: Functional foods, the second generation

In the U.S., the National Academy of Sci-

Figure 22—Six levels in conceptual process design methodology of Douglas and others (1999)

Figure 23—Morphological analysis and functional analysis (King 1974)


CRFSFS: Comprehensive Reviews in Food Science and Food Safety ences defines functional foods as foods in which concentrations of one or more ingredients have been modified to enhance their contributions to a healthful diet (Datamonitor 1997). The concept is a logical historical evolution from the more traditional views from nutritional and biomedical science that “balanced total diets are important for health”. In that philosophy, products low in fat, low in cholesterol or sugar, and high in vegetable and fruit intake were stressed. More and more compounds that promote health are being identified in foods, and this has led to general acceptance that individual foods can promote health. The functional foods market thus developed. The aspect that distinguishes between a functional food and a conventional food is that functional foods make specific demonstrable and quantifiable health benefit claims that can be attributed to the product as eaten and are caused by the presence of an active ingredient. This class of foods is expected to grow, based on current trends, from an estimated $22 billion in 1996 to about $45 billion in 2005 in the U.S.A., Japan, and Europe. In addition to the $22 billion functional foods market in 1996, there was a $15 billion market for dietary supplements. Consumers believe that functional foods can offer health benefits for special diseases or undesirable conditions, such as cardiovascular diseases, hypercholesterolaemia, overweight, colon cancer, osteoporosis, arthritis, breast cancer, prostate cancer, and Alzheimer’s disease (Childs 1997). Cardiovascular health, natural immunosystem improvement, and micronutrient fortification are 3 opportunities for the immediate future. Good examples are McNeil’s Benecol™ spread and Unilever’s Take Control™ and Becel ProActiv™ spreads. Linneman and others (1999) presented a model to translate consumer perception and food preferences to technological developments and priorities for the future that has recently been adapted to the functional foods scene (Plaami and others 2001)(see Figure 24). In particular, when the active compounds have to be present at levels of about 5 to 10% in the carrier food, we can expect that food-processing issues will emerge. One has to consider questions such as: (1) Availability of sufficient quantities of the active ingredient at desired purity or form (for example stereo-isomer). (2) Can the raw material containing the functional ingredient be pretreated to increase concentration or bioavailability or processability? (3) Can the active ingredient be made in the food process itself (for example, fermentation)? 54

Figure 24—Linneman and others (1999) presented a model to translate consumer perception and food preferences to technological developments and priorities for the future that has recently been adapted to the functional foods scene.

(4) Does the process need to be changed in order not to degrade the active ingredient (for example, oxidation of PUFA, losses of flavonoids or vitamins, chelation of minerals)? (5) Does the active ingredient change texture, taste, or color of the product? If so, what can one do about it, in either formulation or process? For instance, many functional ingredients taste bitter, astringent, or acid. (6) How to physically build the active ingredient into the products, for example as a separate phase, encapsulated or not. (7) How to ensure stability of the active ingredient in the product in shelf life and food preparation. (8) Is controlled release in the gut necessary or not? If so, the active ingredient has to be ‘packed’ in a suitable manner. Structuring processes, separation processes (Singh and Singh 1996), (bio-) conversion processes, and stabilization processes will have to provide the answers to such questions. In particular, separation processes and encapsulation techniques will be of importance. Enantiomer selective separation processes can also come into play here. Perfecting the “Make”: Smart manufacturing and process design

The more the food industry moves to include Service and Care, the more effective its “Make” sector must become in order to


keep economic margins. This implies very effective supply chain management. The term “smart manufacturing” has been coined to indicate supply chain-related activities involved in developing manufacturing plans, executing these plans, and using up-to-date information to evaluate the performance and adjust the plan for improvements in a following production cycle: that is, a continuous learning circle (Aspen Technology, Inc., PlantelligenceTM products and services, Cambridge, Mass., U.S.A.). As shown in Figure 25, these systems have 2 interfaces: 1 with the control systems of the actual plant and 1 with the “enterprise resource planning” systems (ERP). The knowledge bases incorporated in the system are process know-how, process know-why, and plant operations knowledge. Important factors are the specific structure of the value addition in successive parts of the food supply chain, how to incorporate consumer preferences, which process choices to make, and how to incorporate process understanding in these systems, in particular with respect to perishable products and variable sales demand (product mix and volume). Computers and computational methods have advanced to the point where they have a significant impact on the way in which food process engineers can approach problems in design, control, and manufacturing operations. In essence, they

25 years of food process engineering . . . allow “smart manufacturing” practices developed in other industries to be adapted to food manufacturing. Food companies that operate in the Make segment are moving fast to achieve greater competitive advantage from optimization of their supply chains. Enterprise resource planning (or ERP) systems are key enablers for companies seeking to optimize their supply chain. These systems are usually designed to cope with manufacturing processes that have production efficiencies of 98+%, while the food industry often has production efficiencies of 80+%. The financial potential of supply chain optimization is often very big, but to reap the full benefits often requires a profound change across the whole company: better buying, lower product complexity, improved manufacturing technologies, lower stocks and fewer stock-keeping units (SKUs), introduction of vendor-managed inventory with customers, greater market sensitivity, and better use of information technology (Unilever 1999). There are several synergies between the benefits just mentioned. For instance, lower product complexity and fewer SKUs mean that leaner manufacturing becomes possible and that buying power for raw materials increases. ERP systems and supply chain management systems are fast-growing areas for software and hardware vendors. AMR Research Inc. estimates an aggregated 5-year annual growth of 32% to a value of $66.6 B for ERP systems.The same company estimates of 48% growth to a value of $18.6 B for supply chain management systems in 2003 (see Internet site: http://www.advmtg. com/press/files/99/12/asp). The top 5 sup-

ply chain management system providers in 1998 are: i2 Technologies Inc. (Dallas, Tex., U.S.A.), Manugistics Inc. (Rockville, Md., U.S.A.), IBS (International Business Systems, Stockholm, Sweden), IMI (Industri-Matematik International, Stockholm, Sweden), and EXE Technologies Inc. (Dallas, Tex., U.S.A.), while the ORSI Group (now Siemens Automation and Drives, Genoa, Italy) and ASPEN Technology Inc. (Cambridge, Mass., U.S.A.) cover similar areas such as open control systems and smart manufacturing. The key to success of smart manufacturing will be the intrinsic reliability of the supply chain itself without loss of flexibility. There are, however, different interpretations by various professional disciplines. The mechanical engineer, the process engineer, and the systems engineer have, for instance, different biases toward the concept of smart manufacturing. The mechanical engineer tends to think of programmable manufacturing operations and machines/equipment with electronic instead of mechanical controls designed with a mechatronics design approach in mind, that is, flexible manufacturing systems/flexible assembly systems and automatic guided vehicles based-process operations (for example, Shuttlenet ex JGC). The process engineer tends to think of software systems that enable process simulations models to be used online in control of manufacturing operations. The systems engineer tends to think of total supply chain models used to optimize the chain and link it to the ERP systems. Industrial practice needs all 3 and in the right balance. Almost every food manufacturing plant has a bulk processing part

(where “chemical engineering” dominates) at the beginning followed by a dosing/ packing part (where “mechanical engineering” dominates). These 2 parts need to be tuned to each other in order to cope with process disturbances and inevitable raw materials variability. Typical aspects of such systems are, according to Aspen Technology Inc. (see Internet site: http://www.aspentech.com/tp/ tplantelligence3.htm): (1) Integrated recipe management and multivariate control functionality. This automates transition between batches. (2) Unified batch modeling and management capabilities enabling the feeding of real-time plant data into off-line modeling systems. (3) Combination of steady-state simulation and process information management capacity enables the direct feeding of operating data into simulators for ‘what-if’ analyses. (4) Linking to enterprise systems enables instantaneous exchange of data on materials requirement, planned orders, optimized production schedules, and final products. (5) Competitive pressures have forced consumer goods manufacturers to consider combinations of “lean” (that is, highly automated), “agile” (highly flexible), and “virtual” (3rd party-sourced) manufacturing (Pearson 1997). This also holds for the food industry. “Lean” manufacturing operations are high volume, single product operations where low-cost production is achieved through high levels of automation and high operating efficiencies and long production runs. “Agile” manufacturing operations are low volume, wide variety products where quick, reliable changeover between product families is required. “Virtual” manufacturing operations are sensible when the added value and technical exclusivity of the manufacturing process is low or when investment in plant for new ventures or markets is too risky. Food companies operating on a global scale will have to optimize their regional operations depending on the structure of the local markets and, therefore, will have a mix of the 3 typical manufacturing operations just mentioned. Natural selective solvents: Nearcritical and supercritical extraction with CO2 as green solvent or antisolvent

Figure 25—Supply chain systems including smart manufacturing systems. ERP = Enterprise Resources Planning

Supercritical carbon dioxide is used in the food industry mainly for the extraction of plant materials. Figure 26 indicates the supercritical region in the phase diagram of carbon dioxide. The most pertinent examples are decaffeination of coffee and tea


CRFSFS: Comprehensive Reviews in Food Science and Food Safety and the extraction of hop for beer brewing. In a recent paper, Marr and Gamse (2000) report 77 literature references to extraction of flavors, spices, and essential oils from plant materials. Reverchon (1997) reviewed the process engineering and process modeling aspects of supercritical fluid extraction in the food industry. The interest in supercritical extraction with carbon dioxide has increased again over the last year because of legal limitations on solvent residues and solvents that can be used for foods, beverages, and pharmaceuticals. One attraction of carbon dioxide as a solvent is that the solvency and selectivity can be changed by either temperature or pressure changes. Separation of carbon dioxide from the extracted compound(s) can be done by pressure release, temperature change, or adsorption. Carbon dioxide can also be used as an antisolvent in liquid-solid extraction processes when the extracted component is only slightly soluble in carbon dioxide (see Figure 27). There has recently been much interest in deterpenation of citrus peel oils using su-

percritical carbon dioxide (Sato and others 1995, 1996; Budich and others 1999). Various phase equilibria have been measured and correlated with equations of state (Iwai and others 1994a, b; De la Fuente and Bottini 2000; Gamse and Marr 2000; Viereia and others 1999; Weber and others 1999; Adrian and others 1999). Figure 28 is an example of carbon dioxide-methylacetate fitted with the PengRobinson equation of state. Solubility of solids in supercritical carbon dioxide can be predicted with the method of Bush and Eckert (1998) that is based on an activity coefficient model for solids and a linear solvation energy relationship. Parameters for 63 solid compounds are given, including components relevant for food systems (cholesterol, b-carotene, caffeine, glucose, fatty acids, theobromine). Espinosa and others (2000) give an analysis of the optimization of such deterpenation processes. The problem is treated as a ternary system linalool-limonene-carbon dioxide. The phase equilibria were described with an equation of state. Operating at 95 bar and 333 K, a 40-stage extraction system with

reflux purifies linalool to 99% on a carbon dioxide-free basis with a recovery of 94%. How to embody 40 equilibrium stages at 95 bar into equipment was, however, not mentioned. A similar deterpenation process for spearmint oils to purify L-carvone from Llimonene has been researched (Kim and Hong 1999). The fluid phase equilibria of vegetable oil deodorizer distillates with supercritical carbon dioxide also get attention. The presence of components such as fatty acids, fatty acid esters, sterols (stigmasterol in soy bean oil), tocopherols, and squalene are the triggers (Araujo and others 2001, Araujo and Meireles 2000, Brunner and others 1991). Fractionation of edible oil mixtures by supercritical carbon dioxide was reported by Ruivo and others (2001). Finally, milkfat fractionation with supercritical carbon dioxide has been studied to obtain butter flavor extracts rich in d-lactones (De Haan and others 1990) or to lower saturated fatty acid content and cholesterol and enrich in b-carotene (Rizvi and Bhaskar 1995). With the increasing interest in functional foods discussed earlier, mild extraction techniques of physiologically active compounds from biological raw materials will become increasingly interesting. Application of CFD and related numerical computation techniques

Figure 26—The supercritical region of carbon dioxide. Isotherms were calculated with the Peng-Robinson equation of state.

56


Computational Fluid Dynamics (CFD) obtains the solution of the governing transport equations for flow of fluid and, depending on the particular application, solves additional equations involving multiphase, heat and mass transfer, turbulence, and other relevant processes. The corresponding partial differential equations are discretized into a set of algebraic equations, which are solved on a computer using suitable algorithms. For this, the vast majority of CFD techniques are based on the use of a mesh that defines the geometry and flow domain of interest. Appropriate boundary and initial conditions are applied on this mesh, and the distributions of quantities such as velocity, pressure, turbulence, temperature, and concentration are determined iteratively at every mesh point covering the domain. General-purpose CFD software first became commercially available in the early 1980s, followed by a period of rapid expansion. By now, the market is very competitive with well in excess of a dozen of commercial software packages offering similar capabilities. From its beginnings in “high-tech” industry (for example, aerospace, automotive, turbo-machinery, nuclear, and so on), the use of CFD has spread widely into more traditional sectors, for ex-

25 years of food process engineering . . . ample, metallurgical and food processing, architecture and building services. Applying CFD to industrial flow problems in the past has been of limited value, mainly because of the complexity of the flow geometries. Today’s CFD solvers have been designed to deal with geometries of any arbitrary shape and structure, which makes this simulation technique a very valuable and powerful tool for solving complex industrial problems. It should be remarked, however, that despite the continuous increase of computer power and storage capacity, the meshes are still too coarse to resolve all the length and time scales present in many industrial applications. Fully turbulent flows or dispersed multiphase flows are very common examples of flow situations where the spectrum of scales contributing to the dynamics is so

broad that a “direct numerical simulation” of all the details of the flow (up to the smallest energy-dissipating eddies or the smallest drops) is out of reach for the foreseeable future. Additional turbulence or multiphase models, such as the one described in Appendix C are therefore still necessary to account for these effects. However, the scientific and technological challenges faced by the food processing industry are still some of the most demanding, with the need to account for many additional complexities such as: (1) Non-Newtonian and visco-elastic materials (for example, biopolymer mixtures); (2) Quasi fluid/solid substances (yieldstress materials); (3) Moving boundaries (for example, free-surfaces, moving bodies);

(a)

(b)

(c)

(d)

Figure 27—Schematic flow sheets of supercritical extraction processes with 4 different ways of removing extracted components: (a) Pressure swing, (b) Temperature swing, (c) Adsorption, (d) Gas Anti-Solvent use to separate extracted component from solvent.

Figure 28—Example of P-x-y diagram of CO 2–methylacetate (fitted with PengRobinson equation of state)

(4) Simultaneous analysis of fluid flow and solid stresses (for example, fluid-structure interaction); (5) Combined heat, mass and momentum transfer phenomena (for example, drying, baking, frying); (6) Health and safety issues (for example, microorganisms, chemicals); (7) Product taste and aesthetic appearance (for example, flavor, color). Although CFD has been used for the prediction of heat, mass, and momentum transfer in industrial processes since the 1970s, with some effective applications made to problems arising in food processing, the use of CFD for such problems has not become widespread, because, up to now, there have been several obstacles in the way of its development in this area: (1) Quality and material properties of foodstuffs are closely linked to their microstructure, with the complexity of description of food materials being much greater than the materials to which CFD have been conventionally applied (air, water, combustion gases, and so on); (2) Lack of basic insight into “local” kinetic processes at the small scales (droplet, liquid film, crystal, fiber, cell wall, micro organism scales); (3) Importance and complexity of (micro) biological processes, with the scarcity of models for those which are compatible with CFD; (4) Nature of the food-processing industry and its relation to CFD, with the difficulties linked to the cost of purchasing or leasing the software, the cost of the highpowered computer hardware, and the scarcity, cost, and managerial difficulty of adequately trained and experienced personnel. However, CFD has already proven useful in predicting the detailed flow behavior for a wide range of food engineering applications, leading typically to an improved


CRFSFS: Comprehensive Reviews in Food Science and Food Safety equipment and process design. These simulations provide new insights and detailed physical understanding of food processing problems, and they do so through nonintrusive predictions. Using CFD to identify the right operating conditions, food-processing industries can reduce expensive building and testing through plant trials, with a much greater range of “what-if” investigations. Overall process and product design times can therefore be significantly shortened. Typical examples of applications range from the determination of product thermal signatures and analysis of heating and cooling processes such as ovens, dryers, refrigeration equipments, and so on, to scale-up simulations of new processes and products, including mixing systems, pipes, dies, extruders, and so on, and their performance predictions. Modeling of food process engineering problems is more and more using computational software such as CFD codes (PHOENICS [CHAM], FLUENT/FIDAP, POLYFLOW, STAR-CD, CFX-4/5 [AEA Tech]) and MATLAB/SIMULINK and so on (Scott and Richardson 1997). Some examples are: (1) Modeling of a continuous sterilization process (Jung and Fryer 1999) with FIDAP. (2) Natural convection heating of canned food and food in a flexible pouch (Ghani and others 1999b, 2001) with PHOENICS. The finite volume method was

used. The model predicts natural convection patterns in cans that confirm early experiments by Hiddink and others (1976). Ghani and others (1999a) also modelled the inactivation of bacteria in canned liquid foods. (3) As another illustration, we mention the thermal processing of liquid foods containing solid particulates. Here we have the situation of moving bodies in a continuum. The continuous processing of such foodstuffs though pipes is limited by the presence of particles that have to receive enough heat to guarantee commercial safety of the product. The design and set-up of the processing line (holding tube length, holding temperature) is mainly determined by the residence time of the particulates and the heat transfer between the surrounding carrier liquid and the particulates. Unfortunately, these parameters are extremely difficult to measure experimentally because of the dynamic nature of the phenomena. Figure 29 shows an instantaneous velocity distribution of fluid and particulates within a section of a tubular heat exchanger (Duchanoy and Jongen 2001). By performing a large number of such simulations, new insights in particle motion in various pieces of equipment can be gained. Residence time distributions of particles can be correlated with any other set of relevant parameters (particle density, liquid phase rheology, equipment design, throughput). Figure 30 shows another example of flow with dispersed particles with

heat transfer, from which detailed and targeted heat transfer correlations can be obtained for a range of relevant process configurations. In this example, we used CFX as the simulation tool. We will come back to this particular example later. (4) In rotary batch retorting processes, the thermal treatment is strongly influenced by the presence of a headspace in the jar, which may enhance (through buoyancy-induced mixing) or limit (as a thermal buffer) the heat transfer, depending on a complex interplay between material properties (thermal and rheological), jar characteristics, and operating conditions (for example, rotation speed). Here we have a situation where a free surface has to be handled. Figure 31 illustrates a typical result of a simulation of such a process, where the headspace is taken into account. (5) Modeling of airflow patterns in a spray drier without a spray (Kievit 1997; Kietvit and others 1977) with FLOW3D. (6) For modeling of the temperature and humidity pattern in a spray dryer (Kievit 1997), a Euler/Lagrange method was applied. The airflow pattern is calculated first, then the particle phase is calculated by tracking a number of individual particles through the airflow pattern. Along the particle trajectories, the exchange of mass, energy, and momentum with the continuous phase is calculated. These transfer terms are then added to the source terms for the momentum equa-

Figure 29—Instantaneous particulate distribution within a tube bend with boyant particulates. Colors indicate local velocity magnitude. 58


25 years of food process engineering . . . tions of the airflow calculation. A k-e model was used to describe turbulence. A coupled solution for the 2 phases is obtained by repeating this cycle a few dozen times. This method is called the Particle Source in Cell method of Crowe (Crowe and others 1977). (7) A model for spray drying developed by NIZO Food Research called NIZODrySim (Straatsma and others 1999a, b) is particularly suited to de-bottlenecking spray dryers in the dairy industry and assessing stickiness and insolubility indices dependent on operation conditions. (8) A model for pneumatic drying of food particles using MATLAB together with SIMULINK (Pelegrina and Crapsite 2001). (9) A 2-D axisymmetric model using the

finite element method for convection cooking of poultry products (Chen and others 1999). MATLAB was used to code the model. The model predicts cooking yields and center temperatures as a function of cooking time. (10) A model for an electrical forced convection oven for heating of foods in batches of up to 40 gastronorm units (Verboven and others 2000a,b) using CFX. (11) A 3-D model that predicts crystallization from seeds during processing of thin sugar films on a porous support during drying of a bed of pieces (for example, corn flakes) in a through-flow dryer (BenYoseph and others 2000). A homemade C++ computer code did the job. (12) Cooling rate and weight loss of

Figure 30—Instantaneous temperature distribution of particulates within a tube with heat transfer. Colors indicate local temperature in the fluid.

cooked ham during air-blast chilling (Hu and Sun 2000) using CFX. (13) Simulation model for combined simulation of heat transfer and enzyme inactivation kinetics in vegetables of cylindrical shape (Martens and others 2001). A research finite element package (CHAMPSPACK) using MATLAB and FORTRAN routines was used to simulate peroxidase activity in broccoli and lipoxygenase activity in asparagus subject to heat treatments. (14) Flavor release from oil in a “sauce pan” (Jousse and others 2002) as shown in Figure 32. (15) Membrane emulsification modeling of an oil drop forming at a cylindrical pore (Abrahamse and others 2001) is shown in Figure 33a and 33b. The droplet is deformed by the water flowing past the orifice of the pore and eventually breaks away from its umbilical cord. CFX4.2 was used as the CFD package. Modeling of flow patterns in the often complex geometries of food processing equipment handling, rheologically speaking, extremely complex liquids, pastes, dispersions, or dough using advanced CFD techniques has only relatively recently begun. Results of such computations have to be summarized in characteristic yardsticks such as spatial distributions of energy dissipation intensity, axial dispersion coefficients, or total rate of strain histories of fluid elements that have passed through a piece of equipment.

Figure 31—Headspace motion during in-jar rotary retorting. Vol. 2, 2003 —COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY 59

CRFSFS: Comprehensive Reviews in Food Science and Food Safety As an illustration of this, the kneading of bread dough is considered. There is a large body of evidence suggesting that the development of a dough (its gluten network) is not only dependent on the total mechanical energy transferred during kneading, but also on the type of deformation it has been subjected to. Kneading equipment design should therefore be driven by the need to preferentially and efficiently provide to the dough the right deformation history required for its optimal development. Figure 34 shows the computed instantaneous spatial distribution of the deformation type that a dough material would be subjected to during kneading in a specific mixer (Farinograph), (Jongen and Dekker 1999, Jongen 2000). It is evident that a large amount of material close to the blades is subjected to a purely rotational motion that does not contribute to the deformation of the dough. Figure 35 shows the distribution of the same quantity in a different kneader (Do-corder type). As is apparent, the nondeforming zone “attached” to the blades in the Farinograph has been replaced by the boat-shaped blades in the Do-corder, which leads to a much more efficient kneading action. The nature and capabilities of CFD have been changing rapidly in recent years, and have begun to make an impact in the food processing industry. However, despite the overwhelming amount of possibilities and advantages of the present CFD commercial codes, the role of this tool should not be exaggerated. The results of the calculations represent a flow-model obeying the physics and boundary conditions imposed by the user. Proper physical modeling of the phenomena investigated is therefore a critical step in preparing a CFD simulation, and it will dominate the relevance and accuracy of the results obtained later. The rate-limiting factor is now the painstakingly slow progress in the material sciences of food products and changes in material properties during processing (either desired or undesired). This requires solid knowledge and justification of the models of all physical and chemical processes taken into account in the computations. In addition, it is very easy to compute a solution that is total physical nonsense! Although vendors of commercial CFD software claim that their codes do not require specialized knowledge of CFD, some knowledge is essential. Acquaintance with physical modelling and numerical techniques is absolutely necessary in order to set up a proper simulation and judge the value of its results, while taking into account the capabilities and limitations of CFD. The last bottleneck in CFD will probably be neither the mesh generation, nor the necessary computer power of the CFD 60

solvers, nor the availability of suitable physical models, but to find the people who really can do the full job.

namic equilibrium and the total molar concentrations of phases j and k:

Understanding flavor retention and release

Flavor is a total sensory impression made up of the elementary taste sensations (salt, sweet, sour, bitter, and umami) that are registered by the taste buds and of volatile aroma components that are registered by receptors in the olfactory epithelium in the nasal cavity. When glass/rubbery state transition effects do not impair their diffusion rates, the flavor components distribute over the various phases that make up the food microstructure, dependent on their distribution coefficients and/or adsorption coefficients. Often the volatile aroma components are present at very low concentrations so the distribution coefficients, mi, between the phases j and k in the food can be related to activity coefficients at infinite dilution and thermody-

Figure 32—Acetone concentration in the oil phase during flavor release after 50, 300, and 600 s, respectively, starting from a uniform concentration of 1 at t = 0. The ‘sauce pan’ is heated at 130 °C, and the airflow is 300 mL/min (Jousse and others)


The distribution between the food-phase k and the vapor phase in contact follows from:

Activity coefficients can in principle be calculated from correlations, for example: (1) Pierrotti, Deal, and Derr, correlations for water as solvent (Ried and others 1977); (2) UNIQUAC equations (Prausnitz and others 1986, 1980); (3) UNIFAC equations: (Prausnitz and

25 years of food process engineering . . . Upon eating the food, flavor components are released. The overall sensory appreciation is very much influenced by the way the components were distributed over the different phases and the diffusion kinetics of flavor release and transport of the volatiles to the olfactory epithelium. Knowledge in this area is of key importance for development of, for example, reduced fat products. The transport phenomena take place in ill-defined hydrodynamic fields of ‘eating a food’, but it is important to develop methods to characterize these ‘in-mouth’ kinetics (Overbosch and others 1991). Here, CFD-techniques can again play a useful role.

others 1986; Fredenslund and others 1977; Gmehling and others 1982, 1993, 1998; Lohmann and others 2001; Lohmann and Gmehling 2001) and on the Internet (see http://www.uni-oldenburg.de/ tchemie/consortium). Application of these correlations implies that the food systems are seen as aqueous solutions. In food materials, water is usually the dominant component indeed and acts as a solvent system for sugars, salts, hydrophilic carbohydrates, gums, and proteins. Phase equilibrium models for the activity coefficients of aroma components are, in principle, available but not tailored to the common bulk phases present in

foods (for example, triglycerides, aqueous solutions of sugars, carbohydrate/protein thickened or gelled water phases, without or with ionic species, and so on). Again, the model developed by Haynes and others (1993) could offer good perspectives in this respect. When the aqueous phase contains dissolved solids, the activity coefficient and relative volatility of aroma components can be strongly influenced and deviate from the values in binary aqueous solutions. The influence can be an increase as well as a decrease. Experimental data are available for a number of systems (Chandrasekaran and King 1972a, b).

Figure 33—Membrane emulsification: formation of oil droplet at pore in water flowing past the surface (Abrahamse and others 2001)

Figure 34—Sequence of instantaneous distributions of the flow-type parameter in the Farinograph. Legend for contour colors is as follows: Blue: rotation; Green: shear; Red: elongation. Vol. 2, 2003 —COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY 61

CRFSFS: Comprehensive Reviews in Food Science and Food Safety What Has to be Prepared: Further Ahead, the Gleam in the Eyes The changes occurring in science and technology and in the food industry are summarized in Appendix I and Appendix J, respectively. The changes and trends mentioned there will lead to a rapidly changing environment, in which the food industry must find its way. In this section, we will discuss some challenges that we think are almost inevitable and thus must be faced. Linking scales

The well-known Amundson Report (Amundson and others 1988) on Chemi-

cal Engineering Frontiers of 1988 selected 8 high priority topics for research in the U.S.A. The report characterizes the new paradigm for Chemical Engineering as follows: “. . . The focus of chemical engineering has always been industrial processes that change the physical state or chemical composition of materials. The traditional level of size and complexity at which they have worked on these problems might be termed the mesoscale. Examples of this scale include reactors and equipment for single processes (unit operations) and combinations of unit operations in manufacturing plants. Future research at the me-

Figure 35—Sequence of instantaneous distributions of the flow-type parameter in the Do-corder. Legend for contour colors is as follows: Blue: rotation; Green: shear; Red: elongation. 62


soscale will be increasingly supplemented by studies of phenomena taking place at molecular dimensions—the microscale— and the dimensions of extremely complex systems—the macroscale. Chemical engineers of the future will be integrating a wider range of scales than any other branch of engineering. . . Thus, future chemical engineers will conceive and rigorously solve problems on a continuum of scales ranging from microscale to macroscale . . .”. The Amundson Report also stated in the Summary that, “. . . (it had) reluctantly chosen to pass over food processing, a multibillion dollar industry where chemical engineering finds a growing variety of applications . . .”. At the 5th World Congress of Chemical Engineering, in 1996 in San Diego, the late Professor Jacques Villermaux gave a brilliant plenary address, to which he gave the title “New Horizons in Chemical Engineering” (Villermaux 1996). I was so lucky to be in the audience and hear him speak. What fascinated me most was his thinking about what he called the ‘3rd Paradigm’ of Chemical Engineering. Chemical Engineering’s 1st paradigm was the concept of Unit Operations, usually contributed to Arthur D. Little in a Visiting Committee Report to the President of MIT in 1915 (Wei 1996). The 2nd paradigm is the concept of Transport Phenomena with its unified approach to momentum transport (viscous flow), energy transport, and mass transport so powerfully laid down in 1960 by Bird, Stewart, and Lightfoot (1960). What could that 3rd paradigm of Chemical Engineering then be? Villermaux suggested in the San Diego conference that the understanding of organization at levels of increasing complexity, with many scales of length and time (in a way similar to the renormalization group approach developed in quantum physics [Wilson 1979]), might perhaps be the 3rd paradigm. In the class of structuring processes of the food industry, an interesting factor to note is that the product microstructure manifests itself at a scale that is almost 107 smaller than the size of the equipment that has to form the structure. The challenge is to reduce this gap (“process intensification”) and to understand how phenomena at a smaller length scale relate to properties and behavior at a longer length scale. An area of particular interest is the computation of the behavior of biomolecules, ensembles of molecules or larger colloidal particles in our products and processes. This area has developed significantly, the challenge being to compute the behavior of particle systems where the individual particles have very different properties (in-

25 years of food process engineering . . . teractions, shape, size). Since many products are structured on a scale, the mesoscopic scale, that is much larger than molecular dimensions, linking the atomistic length and time scales and the mesoscopic length and time scales is a challenge. A very interesting development in this area is the dissipative particle dynamics (DPD) approach developed by Hoogerbrugge and Koelman (1992, 1993) and further improved by various workers such as Español and Warren (1995) and Groot and Warren (1997) (see also Groot and Madden 1998; Groot and others 1999; Groot 2000; Wijmans and others 2001), who used it in simulations of water/oil/surfactant systems and microphase separation of block-copolymers. The long-term challenge is to combine the thermodynamics and physics of local structure-forming processes such as network formation, phase separation, agglomeration, nucleation, crystallization, sintering, and so on, with multiphase CFD. In food process engineering design for manmade structured food products, there is significant scope for such approaches, where, again, linking of length and time scales is the issue. Wilson (1979) has pointed out that events characterized by large differences in scales have often little influence on each other and that almost all theories in physics and (chemical) engineering depend on isolating a limited range of length scales and time scales. Important exceptions are behavior near the critical point and turbulent flow as pointed out by Van Deemter (1982). This linking game is, however, not easy. Quoting Villermaux: “. . . Each scale corresponds to an upper level of organization, but we do not know exactly how to establish the laws governing phenomena at a given scale, from those of the preceding scale, without the details of the lower levels . . .”. If the details of the lower levels have to be taken into the modeling of the higher levels, computation-power becomes the limiting factor and will stay so for a long time to come. For example, if molecular dynamic simulations are necessary to simulate particular essential behavior of micelles, only simulations of several nanoseconds could be performed in, say, 1-wk CPU time. Experimentally, it is known that relevant timescales range from 10–8 s (time for a surfactant to enter or leave a micelle) to 10–2 s (fusion of 2 micelles) (Smit and others 1990; Smit and Karaborni 1994). A million-wk CPU time is long indeed! Tildesley (1997) speaks of ‘the 20 nanosecond barrier’ to indicate that atomistic calculations are restricted to intervals shorter than this time scale in a reasonable CPU time of about 1 mo (Dominic Tildsley: “Molecular dynamics time steps are roughly 2.10–15 s to follow

the fastest bond vibrational modes of the molecule accurately. Therefore, a simulation of 20 ns takes 20.10 6 time steps. This is a massive amount of computing time on any workstation [perhaps a month of CPU time]. On a modern massively parallel machine, one might be able to reduce this by an order of magnitude depending on the system size. If you can find funding to get full-time access on a massively parallel machine, you could perform a simulation of protein folding. The world record is held by Peter Kollman in the U.S.A., who simulated a protein for approximately a microsecond. This took an enormous dedicated resource for 9 mo. The question is what simulations can you perform routinely to give you interesting science, and I believe that the 20 ns barrier is a good marker . . .”). The DPD approaches in mesoscopic physics therefore are so fascinating because they can simulate much longer real times within reasonable CPU simulation times on very fast computers. Integrated process design platforms

In order to keep up with the increasing competition, food companies have to be able to respond quickly to the market, providing better quality products meeting consumer wishes. At the same time, the supply chain has to be more cost-effective and flexible, and innovation times have to be reduced. In order to meet these objectives, it is crucial to enable a close dialog between the R&D function and the other business functions, and in R&D, to integrate the various scientific disciplines into a truly innovative and global perspective. The way forward is to compress development time by having a number of phases in parallel execution: “concurrent engineering”. The development time needs to be compressed further than a normal network analysis would give as the minimum time needed to complete a project (Bruin 1993). With the advent of sophisticated computer tools and communication technology, we will see that it is now becoming possible to import expert knowledge into the product’s design and manufacturing cycle, enabling a much larger degree of integration and synergy between the different scientific areas, and between a large community of people with diverse skills and levels of expertise. In the ideal situation in the future, an Integrated Process Design environment can be envisioned in which the various software packages for the techniques mentioned above are linked in such a way that they can directly be used in an interactive fashion. The ideal integrated design environment for “smart product/process de-

sign” would consist of an interlinked system of: (1) A physical and biochemical properties database system, including property estimation correlations, phase equilibrium models, and the like. (2) Models for growth of microorganisms, both for growth stimulation (fermentation) and for killing (pasteurization, sterilization). These will be highly structured models expressing the biochemical pathways crucial to the specific metabolisms. The rapid developments in bio-informatics and metabolomics will enable deep insight in mechanisms of microbial growth, secondary metabolites, and how microorganisms are affected by antimicrobials and environmental stress, and how they defend themselves. (3) Basic transport phenomena models of local physicochemical processes in terms of constitutive equations, flux equations, and rate equations for, for example, enzymatic reaction kinetics, structure formation, flavor formation, off flavor development, crystallization kinetics, cleaning kinetics for CIP operations, and so on. (4) Simulation software packages for solving nonlinear partial differential equations and integral equations of multiphase heat-, mass- and momentum transfer in complicated geometries, including free boundaries (CFD-systems). (5) The classical library of food process unit operations in terms of models linking inputs and outputs: heat-, mass- and momentum balances will be replaced by a total conceptual design approach enabled by sophisticated modelling, in which multidisciplinary insights prevail. (6) Discrete event simulation software that enables the study of optimum integration of the designs of the process line producing the product and the packaging line that packs them. Here we touch the interface between process engineering and production engineering. (7) A best-proven practice equipment database, hygienic design being one key factor. (8) CAD plant engineering software packages. (9) Modular adaptable plant designs (process + production unit operations) capable of immediate customization to produce products by remote programming and supervision. This integrated platform would guide the product- and process developers through the design operation, bringing in relevant knowledge, identifying important gaps in the knowledge, and providing access to the necessary modeling and simulation tools. Final process and equipment specifications would be consolidated via computer aided design techniques.


CRFSFS: Comprehensive Reviews in Food Science and Food Safety In the operating mode of such an ideal process design environment, a high degree of concurrent design and engineering could be realized, with minimization of critical path times as a result. Because all the different disciplines involved in the design can interact very frequently using always the most recent updates on the state of design information, such an environment would enable a drastic reduction of the total design time needed. It would also minimize the risk of making errors due to lack of communication or to use of outdated information that have to be corrected too late in the development cycle at high costs and at the cost of precious time. The product/process design environment just painted can become a very effective tool in the competitive struggle indeed. We have recently developed a computer tool that is a very 1st step towards a partial IPDS for the heat treatment of products with particulates. At the basis lies a CFD simulation model for process lines as we discussed earlier (see Figure 36, 37, and 38).

ture than the current ones and open novel business concepts.

merce on the web, such as on-line supermarkets.

From Service to Care

Rethinking the supply chains

The trend to move to total packages of services mentioned earlier will continue. Drivers for this are trends in society such as the wish to use free time in a quality manner rather than to do domestic chores such as housecleaning and cooking. Opportunities for this are offered by E-com-

Scenarios such as the one just mentioned trigger the need to rethink the supply-chain structures of the future. Figure 25 gave a schematic picture of the operations in the current common supply chain according to AMR Research in the U.S.A. We already made the point

Functional foods: The 3rd generation

One can expect fascinating developments in the functional foods area when the progress by the fast-moving science of proteomics and metabolomics has found out which genes are responsible (see Figure 39 for certain characteristics or physiological disorders in humans). This is an area of extremely intensive research, and first results could be expected by 2004 or 2005. It is an area that is fed by the rapid growth in microtechnology and microelectronics, as shown in Figure 40. The interest of consumers in how what they eat will influence their immediate and longer-term health will strongly increase by these developments. The future could be that, in the 2nd half of the current decade, 1st functional food ingredients will have been identified, based on knowledge of the human genome, with clear-cut health benefits. These ingredients can then be employed in delicious tasting foods personalized to the individual consumer, with his or her individual risk profile for chronic diseases and health problems. A highly interactive communication pattern between our foods business and its end consumers may then emerge with which our current situation simply does not compare. For instance, personalized diets could be constructed and monitored so that, weekly or monthly, the adherence to an individual optimal diet can be controlled via suggested menus from a palmtop computer used during shopping or teleshopping on the Web. This would need a rather different supply chain struc64

Figure 36a—Schematic picture showing how heat transfer simulations, residence time distributions, and mixing theories are incorporated via the IPDS to a nonexpert user interface.

Figure 36b—The structure of an Integrated Process Design Platform (IPDS)


25 years of food process engineering . . . that the major challenge to a foods business is to deliver to customers branded products that satisfy consumer’s needs through the various stages of an optimum supply chain. This means a supply chain with short lead times to new product introductions or product changes in response to changes in customer/consumer demands, with lower stocks and minimum off-spec products and minimum waste/byproducts. The ‘Make’ part of the future foods business will certainly have to do all it can in these things in order to keep a reasonable margin. E-commerce will be applied to the hilt in order to achieve this ‘optimum’.

Playing this optimization game, one should realize that consumer food products are sold in consumer units of typically 100 to 500 g and produced in the ‘Make’ part of the food industry at rates of about 400 to 1000 units per min. Therefore, product assembly and packaging processes in food manufacturing account for approximately 50% of the capital and occupy some 70% of the workforce (Pearson 1998). The performance and flexibility of these operations are, therefore, a major additional factor in product/process design. However, real optimization of staged systems is classically done by a technique known as dynamic programming (Gott-

Figure 39—Genomics: a fast developing science area (Craig Venter, Influenza bacterium)

fried and Weisman 1973, Bellman 1957), where one works backwards from outlet to inlet of the staged system in successive optimization steps. This means that optimization has to start at the consumer/customer end of the supply chain. The main factors determining food choice are: taste, healthy ingredients, no additives, price, structure, and convenience (Figure 41). Figure 42 gives some typical aspects of consumers ‘on-line behavior’. It can very well be that parts of the food industry will move to a far more distributed manufacturing mode in order to satisfy the consumer’s needs and wishes; in other words, to ‘Service’ and ‘Care’ for the consumer. The dynamics of the supply chain changes triggered by Ecommerce are illustrated by the shift from the classical ‘brick and mortar’ structure of Figure 43 to the mixed structures of Figure 44.

Figure 37—An IPDS embedded between Best-Proven Practice and Process and Ingredient know how

Figure 38—An IPDS as a living Knowledge Management system

Summary and Conclusions Utilization of “smart manufacturing” software systems and integrated supply chain management systems for their core product areas are conditions for survival of food companies in the ‘Make’ segment in the coming years. Adapting the systems proven in other industries, such as pipeless process plant, process intensification approaches, maximum energy utilization, and flexible automation systems, offers many opportunities. Reliable process models for the manufacturing processes need to be developed in order to have a realistic basis for “smart manufacturing” software systems. Product assembly and packaging processes need to be included in these models. Programs in materials sciences leading to better prediction methods of material properties and programs that increase our understanding of micro-scale processes are rate-limiting in achieving full-fledged integrated design platforms for food processing.


CRFSFS: Comprehensive Reviews in Food Science and Food Safety Progress will be considerably enhanced by imaginative use of computer-based modeling techniques (for example, CFD of food processing steps and cuisine-cooking, dissipative particle dynamics to link scales, bio-informatics, biochemical systems analysis to model biochemical reaction webs in bacteria, yeasts, and plants). Food engineering curricula need to increase modelling skills of their students. Understanding the functionality of ingredients and processes will lead to better designs and true precision processing. The functional foods area will develop fast, pushed by proteomics and metabolomics. This push results in deep understanding of causes of physiological disorders and aging in humans and of how food can help. This will, in turn, increase the awareness of the consumer on the Food-Health relation and, thus, widen the market for functional foods. Increasing the quality of life of the elderly must be a powerful driver. At the same time, this push will lead to the understanding of plants and how to influence metabolic pathways to produce the goodies that help to combat physiological disorders in humans. Food process engineers will need to orient themselves in these 2 directions to develop the systems and the processes for this 3rd generation of functional foods. Complete rethinking of supply chains will be an important tool to achieve com-

petitive advantage alongside product innovation in a rapidly changing society. It can very well be that parts of the food industry will move to a far more distributed manufacturing mode in order to satisfy the consumer’s needs and wishes; in other words, the industry will move to ‘Service’ and ‘Care’ for the consumer. Food process engineering will be the integrating force behind all these teamwork activities and opportunities. Engineers have the intransigence and persistence to provide solutions to problems for which, in fact, not enough knowledge is yet available. That is the beauty of our profession!

D ij elements of the multicomponent diffusivity matrix, [m2.s–1] D ij Maxwell-Stefan diffusion coefficient of the binary pair i, j, [m2.s–1] [D] multicomponent diffusivity matrix, [m2.s–1] g gravity vector, [m.s–2] G Gibbs free energy, [J/mol] H f enthalpy of fusion, [J/mol] J i diffusion flux relative to average volume flow [mol.m–2.s] J si diffusion flux relative to solids flow,

Symbols Latin

Ai,j binary interaction parameter, [J.mol–1] A(l) n+1 concentration of living bacteria [count.m–3] A00 concentration of living bacteria [count.m–3] Bij element of the reciprocal diffusivity matrix, [s.m–2] [B] matrix of reciprocal diffusivities, [s.m–2] c molar concentration species i, i [mol.m–3] c total molar concentration, [mol.m–3] t C p heat capacity, [J.mol–1.K–1]

Figure 41—Main factors determining food choice

Figure 42—Consumer behavior on the internet (The Economist 2000)

Figure 40—Drivers leading to 3rd generation functional foods 66


Figure 43—The classical structure of the supply chain: ‘brick and mortar’ businesses (The Ecomonist 2000)

25 years of food process engineering . . . [mol.m–2.s] K`i air/food partition coefficient, [-] k turbulent kinetic energy , [m2.s–2] k j 1st order reaction rate constant, [s–1] k +n+1 forward reaction rate constant at stage n, [s–1] – backward reaction rate constant at kn+1 stage n, [s–1] mj,ki distribution coefficient between phases j and k, [-] n ji amount of constituent in phase j, [-] N total number of isomers, [-] N i molar flux of component i, [mol.m– 2.s–1 ] N t total molar flux, [mol.m–2.s–1] p,P pressure, [Pa] rA reaction rate [units Aj.m–3.s–1] j R gas constant,[J.mol–1.K–1 or Pa m3.mol–1.K–1] S (m) n Stirling number of the second kind, [-] t time, [s] T absolute temperature, [K] u velocity vector [m,s–1] V¯i partial molar volume of component i, [m3.mol–1] V¯t total molar volume, [m3.mol–1] xi mole fraction of component i, [-]

Figure 44—Dynamics of the supply chain structure: a mixture of ‘pure play’ and ‘click and mortar’ businesses (The Economist 2000)

d unit tensor, [-] d ij Kronecker delta, [-] « energy dissipation

Greek

a

proportionality constant, ratio between reaction rates at stage j+1 and j [-] } a k time averaged volume fraction of phase k, [-] b ratio of backward reaction constant and forward reaction constant , stage n, [-] g activity coefficient, [-] Gij thermodynamic correction factor for binary mixture pair i, j, [-] [G] matrix of thermodynamic factors, [-]

m mt mi

P Pi s F V

rate, , [m2.s–3] dynamic viscosity, [kg.m–1.s–1] turbulent viscosity, [kg.m–1.s–1] thermodynamic potential of component , [J.mol–1] fluid mixture density, [kg.m–3] density of species i, [kg.m–3] turbulent Schmidt number, [-] total amount of a phase in system, [mol/mol total] capillary number [-]

Indices, superscripts ` at infinite dilution E excess function f number of fatty acids h number of OH-groups i component L liquid phase S solid phase n number of constituents in mixture N number of phases in system Operators = Nabla operator fã time averaged value of f uã density weighted average velocity, [m.s–1]


CRFSFS: Comprehensive Reviews in Food Science and Food Safety Appendix A: Typology of Food Processes The food industry is a highly diverse one, so we need to characterize it into several categories. The processes of the food industry can be divided into 4 broad categories based on the type of end product made: Man-made structured foods use assembly-, structuring-, or texturizing processes; for example, emulsions processes (margarines, sauces, mayonnaises, ice creams), foaming (whipped creams), extrusion processes, dough making, baking, and so on. The end product often is a complicated microstructure of dispersed phases held together by binding forces and a continuous phase. This microstructure leads to desired product texture and the mouthfeel related to this texture, and its destruction during mastication is usually the absolute key to final product quality. The 2nd category of processes in the food industry is the class of disassembly or separation processes where an agricultural or horticultural raw material is split into valuable intermediate products that are often used as raw materials for end-product food processes. Separation processes, for example, diffusional extraction processes (isolation of fractions such as oil seed extraction, beet or cane sugar extraction, concentration of fruit juices or extracts, oils and fats fractionation, milk fractionation), but also a host of mechanical separations where mixtures or slurries of particulates are separated into fractions (for example flour milling), are closest to the chemical process industry (large tonnage, bulk products, often continuous processing). Naturally structured foods often use preservation or stabilization processes where the main aim is to eliminate microbial, enzymatic, or chemical spoilage of the raw materials that usually are food tissues (fish, meat, vegetables). (Bio) converted foods use often highly complex conversion processes where either chemical or biochemical conversions are applied to raw materials yielding ingredients, flavors, fermented products, roasted products (black tea, coffee), and the like.

Appendix B: Example of Triglyceride Structuring Capability Solid-Liquid equilibrium thermodynamics (Prausnitz and others 1986, Wesdorp 1990) leads to the following equations describing equilibrium between phases. There are n components in N phases, of which one is liquid and indicated as phase N and the others are coexisting solid phases. Fugacities of all components i in all phases equal:

Mass balance and stoichiometry requirements:

For N phases and n components, this results in N(n+1) equations with N(n+1) unknowns: the Nn mole fractions x and the number of phases N itself. Liquid oils and a-triglyceride crystals were proven to be ideal mixtures, while b- and b9- modifications were nonideal and could be fitted by a 2- or 3-suffix Margules equation for the excess Gibbs free energy and, thus, the activity coefficients gi for all solid phases f. A multicomponent form of the 3 suffix Margules was chosen where it is assumed that the contribution to the excess Gibbs free energy in the multicomponent mixture is the same as in the binary mixture at the same relative concentrations (Wesdorp 1990):

and:

In order to solve these equations we need to know: (1) Values for the pure component properties: the heats of fusion (DHf,i) and the melting points (Tf,i). (2) The activity coefficients in the liquid phase and the solid phase. (3) A computational framework to solve the set of equations. Reliable relations were developed by Wesdorp (1990) that give the melting point and melting enthalpy as a function of the carbon number, chain length differences, and degree of unsaturation for the a, the b9, and the b-modifications. The activity coeffi-

68


25 years of food process engineering . . .

cients in the liquid state do not deviate significantly from unity if the differences in carbon number between the molecules are less than 15 to 20, for higher differences the deviations from ideality in the liquid phase can be completely attributed to the excess entropy of mixing. The activity coefficients in the solid phases were determined from a single complete DSC melting curve of a ternary system consisting of 2 crystallizing and 1 liquid triglyceride. The measured curve was compared to simulated curves and the Margules constants used as fitting parameters. The apparent heat capacity measured in the DSC apparatus can be approximated with:

The 2 partial derivatives are obtained from 2 flash calculations for each point of the DSC curve. Figure B-1 gives an example of theoretically possible DSC curves for a ternary mixture of triglycerides calculated in this way. Figure B-2 is an example of a fit with the 2-suffix Margules equation of an experimental DSC curve in a ternary mixture. Finally Wesdorp (1990) developed a calculation method that gives the total number of phases that coexist in equilibrium using the tangent plane criterion for phase stability to the multicomponent, multiphase situation for triglyceride mixtures where a, b, and b’ - phases can coexist. It is an extension of Michelsen’s (1982a, b) stability test algorithms to multiple solid liquid equilibria. Some results of this method are given in Figure B-3 and B-4. Finally, Figure B-5 gives 2 graphs for fat blends in the form of solids content against temperature: the a line and the b’ line that are used in design of margarine process lines. A reasonably good fit with experimental solid contents is achieved.

Figure B-1—Theoretically possible DSC curves of a ternary mixture (25% PSP, 25% SOS, 50% OOO), 3 suffix Margules, Wesdorp (1990)



Figure B-2—DSC curve of myristic/palmitic/myristic (25%MPM) + stearic/stearic/oleic (25% SSO) in trioleate (50% OOO) fitted to the 2 suffix Margules, A21 = A 12 = 2.1

Figure B-4—Binary mixture of tripalmitic (PPP) and palmitic/oleic/palmitic (POP) with 2 suffix Margules equation

Figure B-3—Binary mixture of stearic/palmitic/stearic (SPS) and palmitic/stearic/palmitic (PSP) with 3-suffix Margules equation

Figure B-5—Solid content as a function of temperature for two multicomponent fat blends in the a modification and in the b9 modification

Appendix C: Modelled Continuity and Momentum Equations The equations of continuity for the continuous and dispersed phase are respectively:

The subscript k is ‘d’ for the dispersed phase and ‘c’ for the continuous phase. The momentum equations read:

70



for the continuous phase and

for the dispersed phase. In these equations, the following additional relations and definitions hold:

The working equations for k (the turbulent kinetic energy) and e (the energy dissipation rate) are:

The model constant is taken to be unity. If the density gradients in dispersed and continuous phases are negligible (incompressible liquids), then:

Appendix D: Maltodextrin-Gelatine-Water Phase Diagram Figure D-1 gives an example of the phase behavior of maltodextrin derived from potato starch (Paselli SA6) and gelatin in water. Three regions can be distinguished. Close to the concentration axes, there is a single-phase region. This region is next to a region where a liquid phase coexists with a precipitate phase that is rich in maltodextrin, predominantly consisting of molecules with a higher degree of branching (Kasapis and others 1993b). When concentrations are further increased, we enter a region with 2 coexisting liquid phases, one rich in maltodextrin, the other in gelatin. Also indicated in the figure is the transition from the gelatin-rich phase, being the continuous phase, to the maltodextrin-rich phase, being the continuous phase. This is a relatively steep straight line passing smoothly through the binodal curve. It will be clear that the shear moduli of mixed gels will depend strongly on which phase is continuous, the distribution of solvent (water) between the 2 phases and the shear moduli of the individual phases. The sensory properties of these mixed gels are, therefore, strongly influenced by the interplay of phase behavior


CRFSFS: Comprehensive Reviews in Food Science and Food Safety and the kinetics of gelation. This kind of knowledge is obviously very important in product and process design of low-fat spreads and other food products. Another example is the phase behavior of gelatin-amylopectin in water (that is, D2O) (Narasimhan and others 1983, Tseng and others 1987, Durani and others 1993). Measurements at 50 0C of tie lines were done using IR spectroscopy. Clark (1998) analyzed these data with the Flory-Huggins theory. He analyzed each tie line individually by fixing 2 of the 5 parameters (the Flory-Huggins -value for gelatin/water and the experimentally determined gelatin molecular weight) at independently obtained literature values, and he then obtained those unique values to the other 3 Flory-Huggins parameters, which give an exact fit to the tie line. This is repeated for each available tie line, and overall averages of the 3 fitted parameters are taken. Using the final set of parameters, the tie lines were backcalculated from the Flory-Huggins equations. Figure D-2 and D-3 give some results of this approach. Clark does not propose Figure D-1—Phase behavior of maltodextrin (Paselli SA6) the Flory- Huggins approach as a rigorous description of and gelatine (LO-2) in mixed solutions at 45 °C. Tie lines biopolymer mixtures but as a tool to identify important influ- are indicated (Morales and Kokini 1999). ences on general phase behavior (tie line slope, position of binodal, and so on). This has helped to identify complex events associated with polymer charge and salt screening (Clark 1998).

Appendix E: Number of Isomers in Sucrose Polyesters The problem we address is to calculate the number of isomers when we react sucrose, with 8 OH groups, with fatty acid methyl esters originating from methanolysis of a triglyceride source. We indicate the number of fatty acids with f. When we indicate the number of unesterified OH-groups by h, the number of isomers becomes:

in which the following definition is used:

Figure D-2—Flory-Huggins analysis of tie lines (thick lines from Durrani and others 1993) for a gelatin-hydrolyzed amylopectin system in D2O at 50 °C. Gelatin parameters fixed as shown. Best fit values for remaining parameters also shown (Clark 1998). Broken lines are theoretical tie lines based on this fit.

This equation can be rewritten in several ways. The number of different (8-h) esters is:

The total number of isomers is:

Also:

Figure D-3—Flory-Huggins analysis as in Figure D-2, using a higher molecular weight amylopectin sample (M = 72000) (Clark 1998).

where Sn(m) is the Stirling number of the 2nd kind (that is, the 72



number of ways in which a set of n elements the 8 positions along a sugar molecule) can be partitioned in m nonempty subsets.

Appendix F: Stefan-Maxwell Multicomponent Diffusion Diffusion in multicomponent mixtures is often described by the Stefan-Maxwell equations. These equations can be interpreted as a balance between the driving force on species i in a mixture, usually a gradient in the thermodynamic potential m i, and the sum of the friction forces between species i and the other species j (Wesselingh and Bollen 1997, Krishna and Wesselingh 1997):

where Dij is the Maxwell-Stefan diffusivity with dimension [m2/s]. The J’s are molar fluxes of a component, in [mole m–2s–1], relative to the average molar reference velocity u of the mixture. When we express the gradients in thermodynamic potentials in mole fractions, we get (Krishna and Wesselingh 1997):

Substituting this result in the Maxwell-Stefan equation, we get the generalized Fick equation for the multicomponent mixture:

So the diffusion coefficient is replaced by a diffusion coefficient matrix [D] of dimensions (n-1)x(n-1) for an n-component system. In many cases, the diffusion is equivolumetric. Because the molar volumes of the components may differ much and the same holds for the densities on mass basis, it is useful to define a diffusion flux relative to the average volume stream:

where the average volumetric flow is defined by:

The following relation between the 2 diffusion fluxes holds:

Let us now consider a ternary mixture of water (subscript w), an aroma component (subscript a), and nonvolatile solids (for example, a carbohydrate) (subscript s). Then we have the following equations for diffusion of aroma component and water:

where Daa,Daw,Dwa, and Dww are the 4 elements of [D] defined above. We now change the reference frame of the diffusion to the flow of the solids because this can handle shrinking phenomena: Vol. 2, 2003 —COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY 73


We eliminate the fluxes of solids in these 2 equations by using the relation:

Neglecting terms in which the concentration of aroma components appear, because these are very small we get finally:

These flux equations are used in modeling of drying of food liquids.

Appendix G: Modeling Thermal Death Kinetics There seem to be 3 basically different approaches to quantifying thermal death kinetics: (1) Using probability functions to describe the impact of heating stress on populations of microorganisms. The resistance of the microorganisms against heat is then considered to be normally, log-normally , logistic, log-logistic, Gompertz, Weibull, or otherwise distributed. (2) Assume that inactivation of microorganisms follows reaction kinetics, probably caused by irreversible inactivation of critical sites in the cell, which determines the viability of the cell. (3) Use population balances to describe the inactivation/growth kinetics of populations of microorganisms with subpopulations with unequal resistances against heat. For engineering purposes, a preference is often expressed for the 2nd approach because the kinetics can be readily built into conservation equations as source/sink terms and chemical reaction engineering concepts, such as reaction kinetic constants (kr) and activation energies (DE) can readily be applied. As an example of how considering networks of 1st order reactions can describe non-log-linear behavior, consider the following reaction model for the killing of microorganisms:

We assume all reactions to be 1st order:

At time zero, only Ao (living bacteria) are present at concentration Aoo. The bacteria inactivate in a number of successive stages j. An+1 symbolizes ‘dead’ and ‘near-dead’ bacteria, some of which may ‘resuscitate’ because they have sublethal damage. The conservation equation for bacteria that are in stage j then becomes:

We now assume the killing to take place in a perfectly mixed batch reaction vessel at constant temperature T. This means that all the ‘concentrations’ Aj are only a function of time, no concentration gradients exist. In this case, the set of differential equations for the Aj’s reads:

74



. .

Solving the set of differential equations with the initial condition described above gives for the fraction of survivors:

Figure H-1—Comparison of fit with the log-logistic model of Cole and others (1993), fitted with the values shown above

We simplify this equation by taking kj +1 = ak j and k–n+1 = bk +n+1, so we have 4 parameters (k 1, a,b,n). The final equation then becomes: Figure H-2—Comparison of fit with the log-logistic model of Cole and others (1993), fitted with the values shown above

We compared the behavior of this equation with the log-logistic model of Cole and others (1993) for thermal inactivation of Listeria monocytogenes. Figure H-1 to H-3 show fitting results of the present equation compared to a typical result mentioned in the paper by Cole and others for 3 of choices of n. The higher the value of n, the better shoulders are predicted. The model is, however, not good in predicting initially upward concave curves.

Figure H-3—Comparison of fit with the log-logistic model of Cole and others (1993), fitted with the values shown above

Appendix I: Trends in Science and Technology Computation technology and micro electronics

Computers and computational methods have advanced to the point where they have a significant impact on the way in


CRFSFS: Comprehensive Reviews in Food Science and Food Safety which food process engineers can approach problems in design, control, and operations. The advances in microelectronics are fabulous. In the coming decade, an increase in computational speed by combined developments in hardware and software is estimated to be at least a 1000 fold. This means that the speed of handling and analyzing information, linking vast information sources, modeling of molecular interactions of microstructure formation, modeling of fluid flows, transport phenomena in process equipment, process systems control, manufacturing operations, and so on, will increase tremendously in the coming decades. IBM’s planned new supercomputer Blue Gene represents the first major revolution in computer architecture since the 1980s. A radical cellular SMASH (simple, many, and self-heal- Figure I-1—The relative values of broadcasting networks ing) concept makes it not only more compact but 500 times (Sarnoff’s Law), telephone networks (Metcalfes’s Law) and faster than the most powerful computers used today. Blue the internet (Reed’s Law) Gene will be self-healing, that is able to isolate and remedy a fault in any of its 1million processors. These will deliver petaflop scale performance. Simulation testing of the concept has already begun and a small test version was tested in 2001. The machine will be ready by 2004. IBM does not expect to sell the Blue Gene, but it pioneers the computer of the future. In 2001, more than 400 m people worldwide surfed the Web’s 4 billion pages and spent half a trillion dollars (U.S.) on goods and services in the process. Microsoft has very much pushed the open industry standard language XML (Extensible Markup Language) that could replace the current HTML used on the Internet, which would transform the web dramatically; in the words of Bill Gates: “Tomorrow’s web page won’t be a passive picture but your personal, interactive database. . .” The mobile phone and its links to the Internet lead to a very interesting phenomenon that can be described in the mathematics of networks (Anderson 2001). In the classical “one-to-many” network of broadcasting, the Value (V) of a network is proportional to the size of the audience, N (Sarnoff’s Law). In a “many-to-many” network such as the classical telephone system, the Value is different. With N people connected, the number of possible connections is N2-N (Metcalfe’s Law). Now the Internet will add something extra. Internet users can form groups in a very easy way (discussion groups, auction groups, protest groups, chat rooms, and so on). If you have N people, they can form a total of (2 N-N-1) different groups, and the Value of this system is proportional to this number (Reed’s Law). The extraordinary power of the web to form spontaneous groups will constitute a lot of the value of the web. This relation is plotted in Figure I-1. The implications of all these developments for the food industry, where information on the dynamics of consumer behavior, marketing, and advertising patterns, and control of sourcing chains are essential ingredients for a successful business, are staggering. These later Information Technology developments will have a tremendous influence on market research, consumer science, and extension of information about nutrition and health to individual consumers. Highly interactive communication patterns between our business and its end consumers may emerge with which our current situation does not compare at all. For instance, personalized diets can be constructed and monitored so that weekly or monthly the adherence to an individual optimal diet can be controlled via suggested menus from a palmtop computer used during shopping or tele-shopping. They will also have a tremendous impact on our manufacturing systems and on the logistics of supply of our goods to retailers or industrial customers or individual consumers. Biosciences

In the context of our presentation, we use the word biosciences to indicate the rapidly developing area of the impact of rDNA technology, genomics, proteomics, and metabolomics on enzymology, microorganisms (pathology, fermentation technology), plant breeding, and biomedical sciences. The impact of these developments on the food industry will be great. On the one hand, raw materials (vegetables, wheat, corn, oil seeds) can be developed that are much more tailor-made to specific product uses through advanced plant breeding techniques, and thus only require minimum processing. On the other hand, biochemical conversion processes in our manufacturing operations can become much more selective, mild, or otherwise more effective than current operations; for example, by using vastly improved enzymes or microorganisms as biocatalysts. Suffice to say that we believe the biosciences revolution is only just beginning. The extremely complex and exquisitely subtle regulation mechanisms of living cells, be it a microorganism, a cell in a plant tissue, or the human body, will be unraveled in the coming decade. This knowledge can then be used to design foods with greater nutritional functionality (‘functional foods’), improved quality, flavor, and keepability, and less waste and lower energy consumption during processing. A major related issue is how the social, moral, and ethical issues perceived by the consumer toward this technology can suitably be answered by scientists and regulatory bodies. However, there is little doubt that advances in bioscience will enable the food industry to raise the quality and convenience of food products in a nature-friendly and safe manner.

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Appendix J: Trends in the Food Industry The world-processed food trade is valued at about 1500 b$ and expected to grow proportionally to the world populationgrowth of more than 1% per year. Important trends in the food industry are the following: (1) Today the consumer is much more quality conscious, has access to much more information than 20 years ago. One of the fastest growing activities in the food industry is to build up knowledge on consumer behavior: why do consumers buy certain products? (2) Higher quality standards lead to higher functional requirements on products. While it was accepted in the past that higher convenience was linked to compromises on quality, today easy to prepare foods must be available all year round, must have excellent taste, and must be healthy. (3) Modern nutrition science is successful in relating nutritional habits to general health. In addition, consumption of certain foods can lead to prevention of diseases or alleviating their effects: functional foods. These trends will bring the food industry and the pharmaceutical industry closer together. (4) ‘Naturalness’ increasingly becomes an important emotional value of aspects related to health and care for the environment. Everywhere in the world, there is a clear tendency to use natural ingredients in foods. (5) Convenience is still an increasing requirement for food products. In North America and NW Europe, time spent to prepare food for a family has dropped from 2 to 3 h to 20 min, and this trend widens to S Europe. (6) Variety of choice of foods increases and life cycles of nontraditional foods can be as short as 9 mo (for example, in Japan). This means that the high bonus on being first with a product innovation of substance is getting increasingly difficult to achieve. Speeding up the product/process development cycle is, therefore, of paramount importance. (7) This last trend leads to new production methods that provide for, on one hand, a lower cost price and higher production efficiency and on the other increased production flexibility These 7 trends are, of course, all consumer-led and have a big impact on technology development in the food industry, including the chemical/process engineering aspects.

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sure equipment for food preservation. Food Technol 47(6):162-3. MS 20010681 Submitted 12/13/02, Revised 12/20/01, Accepted 1/3/02

Wim Castenmiller, Christophe Duchanoy, Fabien Jousse, Rob Groot, Dominic Tildesley, Michel Vander Stappen, and Remko Boom from Wageningen Univ. and Research, Cecilia Kahn-Abanza of Foodlink Forum, and Daryl Lund of the Univ. of Wisconsin.

The authors thank their Unilever colleagues Eckhard Floter for calculating the figures on the triglyceride equilibria, Allan H. Clark for many helpful suggestions and the figures on gelatin/amylopectin,

The authors are with Unilever Research, Vlaardingen, The Netherlands. Direct inquiries to S. Bruin (E-mail: [email protected]).
