- Email: [email protected]
Database of physical properties of agro-food materials
Journal of Food Engineering 61 (2004) 497–503 www.elsevier.com/locate/jfoodeng
Database of physical properties of agro-food materials P. Nesvadba a,*, M. Houska b,1, W. Wolf c,2, V. Gekas D. Jarvis e,4, P.A. Sadd f,5, A.I. Johns g,6
d,3
,
a The Robert Gordon University, St. Andrew Street, Aberdeen, AB25 1HG, UK The Institute of Food Research Prague, Radiov a 7, 102 31 Prague 10, Czech Republic c Federal Research Centre for Nutrition, Haid-und-Neu-Str. 9, DE-76131 Karlsruhe, Germany d Department of Environmental Engineering, Technical University of Crete, Politechneioupolis, GR-73100 Chania, Crete, Greece e Unilever Research Laboratory, Colworth House, Bedford, MK44 1LQ, UK f The Lord Rank Research Centre, RHM Technology Ltd., Lincoln Road, High Wycombe, Buckinghamshire, HP12 3QR, UK g National Engineering Laboratory, Reynolds Avenue, Scottish Enterprise Technology Park, East Kilbride, G75 0QU, UK b
Received 1 August 2002
Abstract The paper describes the EU database of physical properties of agro-food materials available on-line at http://www.nelfood.com. This database contains (at the time of writing) over 11,000 bibliographic records. About 16% of these records have numerical tables and equations attached to them. The novel and unique feature of the database is that it specifies both the measurement methods and the descriptions of foods. Two four-point scales indicate the quality of this specification (1–4 for method, A–D for food definition). There are five main categories of data on physical properties of agro-food materials: thermal, mechanical (rheological and textural), electrical, diffusional and optical (spectral and colour). The data are available in the form of tables and equations, given as functions of independent variables such as temperature, pressure, composition, etc. The database is a valuable resource for food engineers and scientists. The paper discusses the changing needs of the users of the database and the strategy for meeting these needs, including the following elements: (i) developing models/software for predicting the physical properties of foods from their chemical composition and structure (emulating the success of software such as COSTHERM for the thermal properties); (ii) methods of Artificial Intelligence, such as Case Based Reasoning and (iii) providing interfaces between the physics/engineering database and models dealing more directly with the quality and safety of foods. Last but not least, it is important to nurture the network of experts who provide knowledge in support of the database. This is possible within the Food Properties Awareness Club run under the auspices of the National Engineering Laboratory, UK. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Database; Physical properties; Foods
1. Introduction
*
Corresponding author. Tel.: +44-1224-262-839; fax +44-1224-262828. E-mail addresses: [email protected] (P. Nesvadba), [email protected] (M. Houska), [email protected] (W. Wolf), [email protected] (V. Gekas), [email protected] (D. Jarvis), [email protected] (P.A. Sadd), [email protected] (A.I. Johns). 1 Tel.: +420-2-7270-5893; fax: +4200-2-7270-1983. 2 Tel.: +49-721-6625-0; fax: +49-721-6625-111. 3 Tel.: +30-821-37486; fax: +30-821-37474. 4 Tel.: +44-1234-222-756; fax: +44-1234-222-259. 5 Tel.: +44-1494-428134; fax: +44-1494-428050. 6 Tel.: +44-1355-272-152 (direct line), +44-1355-220-222 (switchboard); fax: +44-1355-272-265. 0260-8774/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0260-8774(03)00213-9
Data on physical properties of agro-food materials are valuable because: (i) they are needed as input to models predicting the quality and behaviour of produce in pre-harvest, harvest and post-harvest situations; (ii) they aid the understanding of food processing; (iii) they are difficult to acquire. New specialised product specific and rapid measurement techniques often need to be developed. Replacing measurement by prediction is also difficult for most materials that are inhomogeneous and have a complex structure.
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The last three decades have seen much effort and progress in developing the measurement techniques for and accumulating data on physical properties of agrofood materials (Adam, 1969; Mayer, 2003). The European effort included the EU concerted action projects COST90 and COST90bis (Jowitt, Esher, Hallstr€ om, Meffert, & Vos, 1983; Jowitt, Escher, Kent, McKenna, & Roques, 1987). This work has consolidated the methods of measurement and predictive equations relating the physical properties of agro-food materials to the composition and structure of the materials and their processing conditions (Houska, 1994, 1997). The recommendation of the COST90 project in 1983 was to create a computerised database of the available data on physical properties of agro-food materials. Professor R.P. Singh assembled the first such database in the USA (Singh, 1995). This was followed in Europe by Unilever, leading to a project funded by the European Commission ‘‘Construction of a Database of Physical Properties of Foods’’, FAIR CT96-1063. The present paper describes the EU database of physical properties of agro-food materials, available online at www.nelfood.com. At the time of writing, the database contains over 11,000 of bibliographic records. About one in five of these records has numerical tables and equations attached to them. The novel and unique feature of the database is that it specifies both the measurement methods (in terms of their principle, accuracy and precision) and the foods (in terms of their composition and structure). Two four-point scales indicate the quality of this specification (1–4 for method, A–D for food definition). There are five main categories of data on physical properties of agro-food materials: (a) Thermal properties, including thermal conductivity, specific heat capacity, freezing point and other thermophysical quantities given as functions of temperature and proximate composition. (b) Mechanical properties (rheology and texture), including viscosity, elasticity modulus, puncture force and several tens of further properties of liquid and solid foods (Houska, Nesvadba, & Mayer, 2001). (c) Electrical properties, including the conductivity for direct and 50 Hz currents and the complex dielectric permittivity at 915 and 2450 MHz. (d) Diffusion coefficients and sorption isotherms (mainly of water), accompanied by a Knowledge Base explaining the use of these properties in the food industry and linking them to microbiological safety. (e) Optical properties (spectral and colour). The data are in the form of tables and equations, given as functions of independent variables such as temperature, pressure, etc. The paper describes the database and shows that it is a valuable resource for
food engineers and scientists. The paper also discusses the changing needs of the users of the database and the strategy for meeting these needs, in particular by developing of the database within the Food Properties Awareness Club run under the auspices of the National Engineering Laboratory, UK.
2. Need for data in the food industry Industrial users often find that the published physical property data are not always suitable for industrial use for a number of reasons, such as, (1) Model systems tend to omit many of the minor ingredients in commercial formulations, which are often highly functional, e.g., minor ingredients can have large effects on the viscosity of sauces. (2) The quality of the data in terms of information about the material (e.g., compositional data) and the accuracy of the measurement technique are often not given. (3) Moisture and air content ranges tend to cover only a relatively narrow band, unrepresentative of real systems and data at both elevated temperatures and low temperatures are sparse. (4) In some areas the data are very limited, e.g., dielectric data. Nevertheless, such data as are available is often invaluable for preliminary design, and industrial users make use of the data in a variety of areas. The following sections describe the various scenarios. 2.1. Process design Thermal properties such as specific heat and thermal conductivity are routinely used in sizing thermal processing equipment (freezers, coolers, ovens, etc.). This is an area where the database is extremely useful as there is a lot of specific heat information available in the open literature and the thermal properties data are less sensitive to changes in physical microstructure than mechanical or diffusional properties. Mixer power requirements can be determined from mechanical property data such as viscosities. Viscosity data is also important in extrusion and enrobing operations, e.g., chocolate enrobing of ice cream, batter coating of fish, etc. Other thermodynamic properties such as the heat of hydration of powdered ingredients (starches, flours, additives, etc.) are also important because they influence the temperature of e.g., batters once the powders have been mixed with water. Hence they are used when considering temperature control.
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2.2. Modelling and optimisation Modelling of food processes invariably needs large amounts of physical property data. Density and viscosity data are used in computational fluid mechanics, specific heat and thermal conductivity for heat transfer calculations, and, if moisture movement is important (as in many baked goods) then moisture diffusivity and sorption isotherms are also required. Unfortunately these properties are often functions of the temperature and compositional properties such as moisture or air content. Also if the food undergoes a phase change during processing (ice formation, fat crystallisation, etc.) data on the associated enthalpy change will be required. 2.3. New product development Sorption isotherm data are regularly used in new product development because of the strong link between water activity and shelf life. The use of diffusivity of minor ingredients such as preservatives in multi-component foods such as pies to predict shelf life is also relevant here. Data on thermal properties are required in the development of ready meals and microwave-able products, and to ensure that the correct thermal processing is applied so that products are microbiologically safe.
3. Thermal properties These are the most ubiquitous properties, involved in almost every food processing operation. In water-containing foods heat transfer is often accompanied by a significant water transfer. Thus the quality and safety of foods depend critically on (a) the whole temperature history and (b) the state and distribution of water in the food. Furthermore (a) and (b) are highly dependent on all other physical properties. Other variables such as pressure, flow, electric fields and water activity also critically influence processing of foods. Thus full understanding and prediction of the thermal properties of foods and their dependence on composition, structure and interaction with other variables influencing the quality and safety of foods is a formidable challenge. As in any discipline, progress is made by gradual accumulation and pooling of data, forming models, testing the models against new data and making better models. A single organisation cannot master all these areas. A collaborative network is a successful way of doing this, as has been shown by projects such as COST90 and COST90bis (Jowitt et al., 1983, 1987).
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The thermal properties are relatively easy to model because, for example, the specific heat capacity is an additive property, so that the specific heat of mixtures can be determined using the knowledge of the properties of the components comprising the mixture. Modelling of the thermal conductivity is already much more difficult because it involves the structure of the food, for example, the porosity (the simplest description). Useful models for thermal conductivity can be constructed using models from the dielectric theory (the Maxwell– Eucken model), in which two component mixtures are modelled in terms of a major (continuous phase) and a minor (dispersed phase). Such models are the basis of the predictive computer program COSTHERM.
4. Mechanical properties Viscosity of foods and their mechanical properties are essential for modelling the mechanical behaviour of foods during deformation and flow. The models of flow are used for engineering calculations e.g., during design of food processing machines, pumping systems, packaging machines, etc. For this purposes the data should be derived from experiments using so called viscometric flows. These flows provide the rheological or mechanical properties independent of size of sample and size of instrument (so called material objective data). On the other hand there are plenty of methods for measurement of mechanical properties that were developed only to mimic the food quality assessment by human sensing (e.g., the food texture in mouth or by hand). In these cases the measured data are frequently incomparable between laboratories due to the lack of standardisation. There are some exceptions, e.g., the method of evaluation of flour quality where the producer of instrument Brabender amylograph has defined the standard. A similar situation is developing in the case of mechanical texture parameters. The company Stable Microsystems, which produces instruments for measuring the mechanical properties of foods, has defined a spectrum of descriptive properties that have became the world standard. The mechanical properties measured are frequently used only for comparison of samples in a given laboratory, and only the relative differences between samples and links to sensory data are interesting. The situation concerning standards of measurements determine to a large extent the selection of essential mechanical and rheological properties for the Database. The list of selected mechanical and rheological properties was completed at the start of the project. The list included the density, bulk density, porosity, shear viscosity, apparent shear viscosity, kinematic viscosity, apparent yield stress of fluid foods, shear stress versus time at constant shear rate, real and imaginary parts of
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the complex elasticity modulus, real and imaginary parts of complex viscosity, tan d, Young modulus of elasticity, maximum stress, surface tension, velocity of sound and Poisson ratio. This initial list has grown during literature searches and preparation of forms for data collection to over 120 different properties and parameters (including textural properties). Table 1 shows the long list. This large number of properties is due to the complexity of the mechanical properties of food (non-linear, viscoelastic, plastic, or brittle) and the need to connect the instrumental measurements with human sensory tests. One problem encountered during data collection is that authors of papers do not systematically adhere to the official rheological nomenclature (Dealy, 1996). The database requires the names of the properties to be more strictly defined to enable searches and retrieval by names of properties. This also avoids duplication of names. Therefore some adjustments had to be made during entry to the database. This posed the ‘‘ethical’’ question whether the contributor of data into the database has the right to change the name of property during extraction of data from original paper into the database. The NELFOOD database comprises a number of tables of values of the different mechanical and rheological properties, and also equations predicting the properties from composition (e.g., rheological properties of fruit pulps as a function of solid composition). Table
1 shows that the most frequent entries are those containing the rheological and mechanical properties which are needed for engineering calculations. There are also many tables in the database with numerical values of parameters of rheological models for characterisation of shear flow curves of different fluid and semi-solid foods (see apparent shear viscosity term). These flow curves are also characterised by individual equations, given by the text in a remark field of data tables as functions of shear rate, temperature and other parameters. The state-of-the-art in predicting the mechanical and rheological properties is not developed to the same degree as it is in the case of thermal properties due to the much greater influence of the structure of the food on the mechanical properties. There is a continuing research activity in trying to connect the structure of the foods with the observed mechanical behaviour. This is a complex task. However, some success has been achieved in predicting the rheological properties of fluids and the behaviour of dairy products. Fischer, Marti and Windhab (2000) reviewed the effort in this area.
5. Sorption properties Sorption properties define the water activity of the food at any given ambient humidity and temperature.
Table 1 Mechanical and rheological properties included in the list of properties in the database (numbers refer to the number of datasets input on 15th May 2002) Adhesiveness, 0 Apparent modulus, 0 Attractive force, 0 Bingham viscosity, B. yield stress, 0, 0 Bioyield Hencky strain and stress, 14, 0 Brittleness, 0 Bruise volume, 0 Bulk modulus, 0 Casson viscosity, 0 Casson yield stress, 0 Chewiness, 0 Cohesiveness, 0 Complex elasticity modulus, 6 Compression modulus, 0 Cutting resistance, 0 Deformation at peak force, 0 Density, bulk d., true d., specific gravity, 241 Displacement at max. force, 0 Elasticity modulus, 0 Energy absorbed, 0
Friction coefficient, 0 Gel strength, 0 Gumminess, 0 Hardness, relative h., 0, 0 Interfacial surface tension, 5 Kinematic viscosity, 19 Kramer max. shear force, 0 Loss tangent, 0 Loss modulus, 0 Max. bioyield stress, 0 Max. compression force, 0 Max. cutting force, 0 Max. Hencky strain, stress, 0, 30 Max. rupture force, 0 Max. strain, stress, 0, 2 Max. viscosity, 0 Modulus of elasticity, 1 Packability, 0 Penetration depth, p. force, 12, 0 Penetrometer reading, 0
Expansion ratio, 0 Expansion volume, 0 Failure deformation, f. force, f. strain, 0, 0, 0
Poisson ratio, 0 Porosity, 4 Puncture force, peak p. f., 0, 0
Firmness, 1 Flow index, consistency index, 1, 1
Refraction index, 1 Relaxation moduli, r. stress, r. time, 0, 0, 0
Fracture strain, f. stress, 0, 0
Residual force, 0
Rupture strain, 0 Sectility force, 2 Shear/compressive force, 0, 0 Shear modulus, s. strength, 0, 0 Shear stress, shear stress vs. time, 12, 1 Skin texture, 0 Sound velocity, 32 Springiness, 0 Stiffness, 0 Storage & loss modulus, 0, 0 Strain at failure, 0 Strain at max. viscosity, 0 Surface tension, 5 Tension force, 0 Texture, textural quality, flesh t., 3, 0, 0 Thermal expansivity, 1 Toughness, 0 Ultrasound velocity, 9 Viscosity, 247 Absolute v., relative v., of relax. of dashpots, 7, 0, 0 Apparent shear v., 74 Apparent shear viscosity Casson mode l, 1 Apparent shear viscosity power-law mode l, 1 Work done, 0 Yield stress, consistency coefficient, flow index, 14, 1, 1 Young modulus, 0
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Water activity, aw , is a physical property that has direct impact on microbiological safety of food. Water activity also influences the storage stability of foods because the deterioration of foods is often mediated by water. Storage life of dry foods such as biscuits is generally longer than that of moist foods such as meat at the same temperature. In this connection freezing of foods is equivalent to drying––the water is removed from the other components of the food although it is still in the food as ice. The NELFOOD Database includes a several tens of sorption isotherms for most representative foods. Also, because of the strong association between aw and the chemical and microbiological properties of foods, it was appropriate to include these aspects in the form of a Knowledge Base on the website (Gibbs & Gekas, 1999).
6. Diffusion properties FickÕs second law describes mass transfer in foods. This leads to the diffusion equation, involving the diffusion coefficient. When the coefficient is known then diffusion of a given component through food can be predicted. Conversely, doing experiments with injecting a known concentration of a component in a defined sample geometry enables the diffusion coefficient to be estimated by sampling the concentration at different points in the sample. There are two fundamental approaches to modelling the diffusion coefficient. One is based on a phenomenological description of the diffusion of water on a macroscopic level, the other uses the concept of chemical potential as the driving force on ‘‘cell level’’ that means the meso- or microscopic levels. The second, modern approach is expected to gain importance in the future because it better connects the diffusion phenomena with the structure and composition of the foods (for example, disruption of biological tissues by cooking, freezing or high pressure). The NELFOOD database incorporates a Knowledge Base concerning all aspects of the apparent diffusion coefficient. This warns the food engineer and scientist of the pitfalls in using the data on diffusion properties. It also gives a useful summary of the methods used for measurement of diffusion coefficients and their use in modelling of mass transfer (Doulia, Tzia & Gekas, 1999).
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conductivity and the components of the complex permittivity at microwave frequencies (915 and 2450 MHz).
8. Optical properties Optical properties (absorption or reflection spectra) over the infrared, visible and ultra violet ranges are very important for measuring the quality of foods (composition, state of freshness). The colour of foods is one of the essential properties for the consumer. Therefore measurements of colour are very important for the food manufacturer in order to achieve uniformity and consistency of colour, for example, of tomato ketchup. The NELFOOD database contains many bibliographic references dealing with these aspects.
9. The quality of data The aim of the Database is to provide meaningful data that were carefully selected, evaluated and entered in numerical tables. The present form of the NELFOOD database enables the existing data to be read and new data to be entered on-line. The data entry consists of filling in a standard pro-forma that details the bibliographic source of the data, the description of the food and of the measuring method. The Pro-forma can have tables attached for data and equations. The quality of the data depends on two factors: (1) how well the food is characterised in terms of composition, structure and experimental conditions such as temperature and pressure and (2) on the quality of the method of measurement (accuracy and precision). For this reason the quality of the data is validated by an alphanumeric score on scales A–D and 1–4. Thus for example, the code A1 means that the description of the food and the specification of experimental condition is excellent (score A) and the method of measurement is very accurate and precise (score 1). The indication of quality of the data is very valuable to the user, because it influences the degree of confidence with which the user can apply the data for a particular application. The accuracy of data that is required for most engineering calculations is 10% or better, corresponding to scores 2 or 1.
10. Future developments 10.1. Predictive equations
7. Electrical properties Electrical properties are essentially the dielectric permittivity and loss of material. These properties are important in Ohmic and microwave heating of foods. The NELFOOD database comprises tables of the d.c.
Modern consumers in the developed countries require a large range of food products and the manufacturers are constantly introducing new food processes and products, leading to an almost infinite range of recipes and compositions. This makes it difficult to capture all
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the corresponding data on physical properties in the look-up type database. The future direction should therefore be towards a predictive database, attempting to predict the physical properties form composition and structure of the foods as is being done in the COSTHERM program for the thermal properties. Modelling of all the other properties is much more difficult, but could be attempted over narrow ranges of composition and structure. In time the gradual accumulation of the data may enable generalisation over wider ranges. This approach could be empirical, however, it is always better to have an understanding of the underlying mechanisms. Therefore the creation of the predictive models should be based on research of the influence of composition and structure of foods on their physical properties. An Expression of Interest (Nesvadba, 2002) by a consortium for food engineers and scientists under the Sixth EU Framework Programme proposed the construction of a predictive database (defined as PREDAT in Fig. 1). The aim is to construct generic models capable of encompassing this large diversity among food materials. The principal research activity will be to understand the connection between composition and structure and the physical properties and to create predictive models (links 1, 2 and 3 in Fig. 1). 10.2. Interface with models for food quality and safety To assist food scientists and technologists in predicting the Safety and Quality of foods (for example, to predict the shelf life of foods), it is necessary to construct a linking knowledge base (FOODSAQ in Fig. 1). The knowledge base FOODSAQ would act as an interface between PREDAT and the models for ‘‘real-life’’ industrial applications. The examples are predicting temperatures and water activity in food for quantifying
microbial growth and inactivation for use in risk analysis. A collection of on-line computer programs for modelling the various unit operations in the industry has been proposed (Nesvadba, 2002) to enable ‘‘what-if’’ and sensitivity analyses of the influence of physical properties of foods on the safety and quality of foods. 10.3. Artificial intelligence Even when a large number of predictive equations are available, it is unlikely that they will be able to cover all eventualities. However, the gradual accumulation of data and understanding could be exploited by modern methods of Artificial Intelligence such as Case Based or Analogical Reasoning. For example, when a skilled ‘‘intelligent user’’ does not find data or equations closely fitting a particular scenario (for example, the thermal conductivity of frozen fish), he may still obtain a satisfactory answer by using data for a similar product (for example, the thermal conductivity of frozen meat with a similar composition). The methods of Artificial Intelligence could assist in bibliographic searches, assessment of similarity between cases and provide a support for decision making. This concept could be applied to finding the data on physical properties of foods per se or, on a more systemic level, to reuse of project management experience (for example experience with freezing of meat would benefit a user needing to find solutions for the freezing of fish). One example of case based reasoning from the culinary domain is a computer program CHEF, which creates new recipes from old ones, by adapting them to requests for new ingredients or tastes (Kolodner, 1993). The Case Based Reasoning method stores previous experience (solved problems or cases) in a database. To solve a new problem the user retrieves from the database
Fig. 1. A system view of the network showing the various components. The links (numbered arrows) 1, 2 and 3 assist understanding of the dependence of the physical properties on the composition and structure of foods.
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a similar experience for a similar situation. This is then reused in the context of the new situation. The reuse may be complete or partial, or adapted according to differences between the cases. To facilitate learning, the user stores the solution for the new experience in the database. 11. Conclusions The NELFOOD database provides a systematised source of data and equations on the main physical properties of foods. The data are mainly presented as tables of numerical values. The quality of existing data has been assessed and is indicated in the database. The future work will improve the predictive features of the database. Acknowledgements The European Commission funded this work under the contract FAIR CT96-1063. Thanks are also due to the Chairmen of the physical property subgroups C.A. Miles/M.J. Morley (thermal), M. Merabet (electrical) and B.M. McKenna (optical) for their contribution to this work. References Adam, M. (Ed.). (1969). Bibliography of physical properties of foods. Czech Academy of Agriculture.
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Dealy, J. M. (1996). Official nomenclature for material functions describing the response of a viscoelastic fluid to various shearing and extensional deformations. Journal of Rheology, 39(1), 253–265. Doulia, D., Tzia, K., & Gekas V. (1999). A knowledge base for the apparent mass diffusion coefficient (Deff ) of Foods. Available: www.nelfood.com. Fischer, P., Marti, I., & Windhab, E. J. (Eds.). (2000). Proceedings of the 2nd international symposium on food rheology and structure, ETH Zurich, 12–16 March 2000, Switzerland. Gibbs, P., & Gekas, V. (1999). Water activity and microbiological aspects of foods––a knowledge base. Available: http://www.nelfood.com. Houska, M. (Ed.). (1994). Thermophysical and rheological properties of food––milk, milk products and semi-products. Food Research Institute Prague. Houska, M. (Ed.). (1997). Thermophysical and rheological properties of food––meat, meat products and semi-products. Food Research Institute Prague. Houska, M., Nesvadba, P., & Mayer, Z. (2001). Database of physical properties of foods: subgroup of mechanical and rheological properties. Journal of Texture Studies, 32, 155–160. Jowitt, R., Esher, F., Hallstr€ om, B., Meffert, H. F. Th. Spiess, W. E. L, & Vos, G. (Eds.). (1983). Physical properties of foods. Barking: Applied Science Publishers. Jowitt, R., Escher, F., Kent, M., McKenna, B. M., & Roques, M. (Eds.). (1987). Physical properties of foods––2. Amsterdam: Elsevier Applied Science. Kolodner, J. L. (1993). Case-based reasoning. San Mateo CA: Morgan Kaufmann Publishers (ISBN 1-55860-237-2). Mayer, Z. (2003). Data-bank of information about physical properties of foods at the Institute of Food Research Prague. Available: http://www.vupp.cz/envupp/research.htm. Nesvadba, P. (2002). Physical and engineering properties for the quality and safety of foods. An expression of interest for the FP6, acronym PENPROF, submitted to the European commission on 7 June 2002. Available: http://eoi.cordis.lu/docs/int_38471.doc. Singh, R. P. (1995). Food properties database. Version 2.0 for Windows. Boca Raton, FL: CRC Press.
Database of physical properties of agro-food materials P. Nesvadba a,*, M. Houska b,1, W. Wolf c,2, V. Gekas D. Jarvis e,4, P.A. Sadd f,5, A.I. Johns g,6
d,3
,
a The Robert Gordon University, St. Andrew Street, Aberdeen, AB25 1HG, UK The Institute of Food Research Prague, Radiov a 7, 102 31 Prague 10, Czech Republic c Federal Research Centre for Nutrition, Haid-und-Neu-Str. 9, DE-76131 Karlsruhe, Germany d Department of Environmental Engineering, Technical University of Crete, Politechneioupolis, GR-73100 Chania, Crete, Greece e Unilever Research Laboratory, Colworth House, Bedford, MK44 1LQ, UK f The Lord Rank Research Centre, RHM Technology Ltd., Lincoln Road, High Wycombe, Buckinghamshire, HP12 3QR, UK g National Engineering Laboratory, Reynolds Avenue, Scottish Enterprise Technology Park, East Kilbride, G75 0QU, UK b
Received 1 August 2002
Abstract The paper describes the EU database of physical properties of agro-food materials available on-line at http://www.nelfood.com. This database contains (at the time of writing) over 11,000 bibliographic records. About 16% of these records have numerical tables and equations attached to them. The novel and unique feature of the database is that it specifies both the measurement methods and the descriptions of foods. Two four-point scales indicate the quality of this specification (1–4 for method, A–D for food definition). There are five main categories of data on physical properties of agro-food materials: thermal, mechanical (rheological and textural), electrical, diffusional and optical (spectral and colour). The data are available in the form of tables and equations, given as functions of independent variables such as temperature, pressure, composition, etc. The database is a valuable resource for food engineers and scientists. The paper discusses the changing needs of the users of the database and the strategy for meeting these needs, including the following elements: (i) developing models/software for predicting the physical properties of foods from their chemical composition and structure (emulating the success of software such as COSTHERM for the thermal properties); (ii) methods of Artificial Intelligence, such as Case Based Reasoning and (iii) providing interfaces between the physics/engineering database and models dealing more directly with the quality and safety of foods. Last but not least, it is important to nurture the network of experts who provide knowledge in support of the database. This is possible within the Food Properties Awareness Club run under the auspices of the National Engineering Laboratory, UK. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Database; Physical properties; Foods
1. Introduction
*
Corresponding author. Tel.: +44-1224-262-839; fax +44-1224-262828. E-mail addresses: [email protected] (P. Nesvadba), [email protected] (M. Houska), [email protected] (W. Wolf), [email protected] (V. Gekas), [email protected] (D. Jarvis), [email protected] (P.A. Sadd), [email protected] (A.I. Johns). 1 Tel.: +420-2-7270-5893; fax: +4200-2-7270-1983. 2 Tel.: +49-721-6625-0; fax: +49-721-6625-111. 3 Tel.: +30-821-37486; fax: +30-821-37474. 4 Tel.: +44-1234-222-756; fax: +44-1234-222-259. 5 Tel.: +44-1494-428134; fax: +44-1494-428050. 6 Tel.: +44-1355-272-152 (direct line), +44-1355-220-222 (switchboard); fax: +44-1355-272-265. 0260-8774/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0260-8774(03)00213-9
Data on physical properties of agro-food materials are valuable because: (i) they are needed as input to models predicting the quality and behaviour of produce in pre-harvest, harvest and post-harvest situations; (ii) they aid the understanding of food processing; (iii) they are difficult to acquire. New specialised product specific and rapid measurement techniques often need to be developed. Replacing measurement by prediction is also difficult for most materials that are inhomogeneous and have a complex structure.
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The last three decades have seen much effort and progress in developing the measurement techniques for and accumulating data on physical properties of agrofood materials (Adam, 1969; Mayer, 2003). The European effort included the EU concerted action projects COST90 and COST90bis (Jowitt, Esher, Hallstr€ om, Meffert, & Vos, 1983; Jowitt, Escher, Kent, McKenna, & Roques, 1987). This work has consolidated the methods of measurement and predictive equations relating the physical properties of agro-food materials to the composition and structure of the materials and their processing conditions (Houska, 1994, 1997). The recommendation of the COST90 project in 1983 was to create a computerised database of the available data on physical properties of agro-food materials. Professor R.P. Singh assembled the first such database in the USA (Singh, 1995). This was followed in Europe by Unilever, leading to a project funded by the European Commission ‘‘Construction of a Database of Physical Properties of Foods’’, FAIR CT96-1063. The present paper describes the EU database of physical properties of agro-food materials, available online at www.nelfood.com. At the time of writing, the database contains over 11,000 of bibliographic records. About one in five of these records has numerical tables and equations attached to them. The novel and unique feature of the database is that it specifies both the measurement methods (in terms of their principle, accuracy and precision) and the foods (in terms of their composition and structure). Two four-point scales indicate the quality of this specification (1–4 for method, A–D for food definition). There are five main categories of data on physical properties of agro-food materials: (a) Thermal properties, including thermal conductivity, specific heat capacity, freezing point and other thermophysical quantities given as functions of temperature and proximate composition. (b) Mechanical properties (rheology and texture), including viscosity, elasticity modulus, puncture force and several tens of further properties of liquid and solid foods (Houska, Nesvadba, & Mayer, 2001). (c) Electrical properties, including the conductivity for direct and 50 Hz currents and the complex dielectric permittivity at 915 and 2450 MHz. (d) Diffusion coefficients and sorption isotherms (mainly of water), accompanied by a Knowledge Base explaining the use of these properties in the food industry and linking them to microbiological safety. (e) Optical properties (spectral and colour). The data are in the form of tables and equations, given as functions of independent variables such as temperature, pressure, etc. The paper describes the database and shows that it is a valuable resource for
food engineers and scientists. The paper also discusses the changing needs of the users of the database and the strategy for meeting these needs, in particular by developing of the database within the Food Properties Awareness Club run under the auspices of the National Engineering Laboratory, UK.
2. Need for data in the food industry Industrial users often find that the published physical property data are not always suitable for industrial use for a number of reasons, such as, (1) Model systems tend to omit many of the minor ingredients in commercial formulations, which are often highly functional, e.g., minor ingredients can have large effects on the viscosity of sauces. (2) The quality of the data in terms of information about the material (e.g., compositional data) and the accuracy of the measurement technique are often not given. (3) Moisture and air content ranges tend to cover only a relatively narrow band, unrepresentative of real systems and data at both elevated temperatures and low temperatures are sparse. (4) In some areas the data are very limited, e.g., dielectric data. Nevertheless, such data as are available is often invaluable for preliminary design, and industrial users make use of the data in a variety of areas. The following sections describe the various scenarios. 2.1. Process design Thermal properties such as specific heat and thermal conductivity are routinely used in sizing thermal processing equipment (freezers, coolers, ovens, etc.). This is an area where the database is extremely useful as there is a lot of specific heat information available in the open literature and the thermal properties data are less sensitive to changes in physical microstructure than mechanical or diffusional properties. Mixer power requirements can be determined from mechanical property data such as viscosities. Viscosity data is also important in extrusion and enrobing operations, e.g., chocolate enrobing of ice cream, batter coating of fish, etc. Other thermodynamic properties such as the heat of hydration of powdered ingredients (starches, flours, additives, etc.) are also important because they influence the temperature of e.g., batters once the powders have been mixed with water. Hence they are used when considering temperature control.
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2.2. Modelling and optimisation Modelling of food processes invariably needs large amounts of physical property data. Density and viscosity data are used in computational fluid mechanics, specific heat and thermal conductivity for heat transfer calculations, and, if moisture movement is important (as in many baked goods) then moisture diffusivity and sorption isotherms are also required. Unfortunately these properties are often functions of the temperature and compositional properties such as moisture or air content. Also if the food undergoes a phase change during processing (ice formation, fat crystallisation, etc.) data on the associated enthalpy change will be required. 2.3. New product development Sorption isotherm data are regularly used in new product development because of the strong link between water activity and shelf life. The use of diffusivity of minor ingredients such as preservatives in multi-component foods such as pies to predict shelf life is also relevant here. Data on thermal properties are required in the development of ready meals and microwave-able products, and to ensure that the correct thermal processing is applied so that products are microbiologically safe.
3. Thermal properties These are the most ubiquitous properties, involved in almost every food processing operation. In water-containing foods heat transfer is often accompanied by a significant water transfer. Thus the quality and safety of foods depend critically on (a) the whole temperature history and (b) the state and distribution of water in the food. Furthermore (a) and (b) are highly dependent on all other physical properties. Other variables such as pressure, flow, electric fields and water activity also critically influence processing of foods. Thus full understanding and prediction of the thermal properties of foods and their dependence on composition, structure and interaction with other variables influencing the quality and safety of foods is a formidable challenge. As in any discipline, progress is made by gradual accumulation and pooling of data, forming models, testing the models against new data and making better models. A single organisation cannot master all these areas. A collaborative network is a successful way of doing this, as has been shown by projects such as COST90 and COST90bis (Jowitt et al., 1983, 1987).
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The thermal properties are relatively easy to model because, for example, the specific heat capacity is an additive property, so that the specific heat of mixtures can be determined using the knowledge of the properties of the components comprising the mixture. Modelling of the thermal conductivity is already much more difficult because it involves the structure of the food, for example, the porosity (the simplest description). Useful models for thermal conductivity can be constructed using models from the dielectric theory (the Maxwell– Eucken model), in which two component mixtures are modelled in terms of a major (continuous phase) and a minor (dispersed phase). Such models are the basis of the predictive computer program COSTHERM.
4. Mechanical properties Viscosity of foods and their mechanical properties are essential for modelling the mechanical behaviour of foods during deformation and flow. The models of flow are used for engineering calculations e.g., during design of food processing machines, pumping systems, packaging machines, etc. For this purposes the data should be derived from experiments using so called viscometric flows. These flows provide the rheological or mechanical properties independent of size of sample and size of instrument (so called material objective data). On the other hand there are plenty of methods for measurement of mechanical properties that were developed only to mimic the food quality assessment by human sensing (e.g., the food texture in mouth or by hand). In these cases the measured data are frequently incomparable between laboratories due to the lack of standardisation. There are some exceptions, e.g., the method of evaluation of flour quality where the producer of instrument Brabender amylograph has defined the standard. A similar situation is developing in the case of mechanical texture parameters. The company Stable Microsystems, which produces instruments for measuring the mechanical properties of foods, has defined a spectrum of descriptive properties that have became the world standard. The mechanical properties measured are frequently used only for comparison of samples in a given laboratory, and only the relative differences between samples and links to sensory data are interesting. The situation concerning standards of measurements determine to a large extent the selection of essential mechanical and rheological properties for the Database. The list of selected mechanical and rheological properties was completed at the start of the project. The list included the density, bulk density, porosity, shear viscosity, apparent shear viscosity, kinematic viscosity, apparent yield stress of fluid foods, shear stress versus time at constant shear rate, real and imaginary parts of
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the complex elasticity modulus, real and imaginary parts of complex viscosity, tan d, Young modulus of elasticity, maximum stress, surface tension, velocity of sound and Poisson ratio. This initial list has grown during literature searches and preparation of forms for data collection to over 120 different properties and parameters (including textural properties). Table 1 shows the long list. This large number of properties is due to the complexity of the mechanical properties of food (non-linear, viscoelastic, plastic, or brittle) and the need to connect the instrumental measurements with human sensory tests. One problem encountered during data collection is that authors of papers do not systematically adhere to the official rheological nomenclature (Dealy, 1996). The database requires the names of the properties to be more strictly defined to enable searches and retrieval by names of properties. This also avoids duplication of names. Therefore some adjustments had to be made during entry to the database. This posed the ‘‘ethical’’ question whether the contributor of data into the database has the right to change the name of property during extraction of data from original paper into the database. The NELFOOD database comprises a number of tables of values of the different mechanical and rheological properties, and also equations predicting the properties from composition (e.g., rheological properties of fruit pulps as a function of solid composition). Table
1 shows that the most frequent entries are those containing the rheological and mechanical properties which are needed for engineering calculations. There are also many tables in the database with numerical values of parameters of rheological models for characterisation of shear flow curves of different fluid and semi-solid foods (see apparent shear viscosity term). These flow curves are also characterised by individual equations, given by the text in a remark field of data tables as functions of shear rate, temperature and other parameters. The state-of-the-art in predicting the mechanical and rheological properties is not developed to the same degree as it is in the case of thermal properties due to the much greater influence of the structure of the food on the mechanical properties. There is a continuing research activity in trying to connect the structure of the foods with the observed mechanical behaviour. This is a complex task. However, some success has been achieved in predicting the rheological properties of fluids and the behaviour of dairy products. Fischer, Marti and Windhab (2000) reviewed the effort in this area.
5. Sorption properties Sorption properties define the water activity of the food at any given ambient humidity and temperature.
Table 1 Mechanical and rheological properties included in the list of properties in the database (numbers refer to the number of datasets input on 15th May 2002) Adhesiveness, 0 Apparent modulus, 0 Attractive force, 0 Bingham viscosity, B. yield stress, 0, 0 Bioyield Hencky strain and stress, 14, 0 Brittleness, 0 Bruise volume, 0 Bulk modulus, 0 Casson viscosity, 0 Casson yield stress, 0 Chewiness, 0 Cohesiveness, 0 Complex elasticity modulus, 6 Compression modulus, 0 Cutting resistance, 0 Deformation at peak force, 0 Density, bulk d., true d., specific gravity, 241 Displacement at max. force, 0 Elasticity modulus, 0 Energy absorbed, 0
Friction coefficient, 0 Gel strength, 0 Gumminess, 0 Hardness, relative h., 0, 0 Interfacial surface tension, 5 Kinematic viscosity, 19 Kramer max. shear force, 0 Loss tangent, 0 Loss modulus, 0 Max. bioyield stress, 0 Max. compression force, 0 Max. cutting force, 0 Max. Hencky strain, stress, 0, 30 Max. rupture force, 0 Max. strain, stress, 0, 2 Max. viscosity, 0 Modulus of elasticity, 1 Packability, 0 Penetration depth, p. force, 12, 0 Penetrometer reading, 0
Expansion ratio, 0 Expansion volume, 0 Failure deformation, f. force, f. strain, 0, 0, 0
Poisson ratio, 0 Porosity, 4 Puncture force, peak p. f., 0, 0
Firmness, 1 Flow index, consistency index, 1, 1
Refraction index, 1 Relaxation moduli, r. stress, r. time, 0, 0, 0
Fracture strain, f. stress, 0, 0
Residual force, 0
Rupture strain, 0 Sectility force, 2 Shear/compressive force, 0, 0 Shear modulus, s. strength, 0, 0 Shear stress, shear stress vs. time, 12, 1 Skin texture, 0 Sound velocity, 32 Springiness, 0 Stiffness, 0 Storage & loss modulus, 0, 0 Strain at failure, 0 Strain at max. viscosity, 0 Surface tension, 5 Tension force, 0 Texture, textural quality, flesh t., 3, 0, 0 Thermal expansivity, 1 Toughness, 0 Ultrasound velocity, 9 Viscosity, 247 Absolute v., relative v., of relax. of dashpots, 7, 0, 0 Apparent shear v., 74 Apparent shear viscosity Casson mode l, 1 Apparent shear viscosity power-law mode l, 1 Work done, 0 Yield stress, consistency coefficient, flow index, 14, 1, 1 Young modulus, 0
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Water activity, aw , is a physical property that has direct impact on microbiological safety of food. Water activity also influences the storage stability of foods because the deterioration of foods is often mediated by water. Storage life of dry foods such as biscuits is generally longer than that of moist foods such as meat at the same temperature. In this connection freezing of foods is equivalent to drying––the water is removed from the other components of the food although it is still in the food as ice. The NELFOOD Database includes a several tens of sorption isotherms for most representative foods. Also, because of the strong association between aw and the chemical and microbiological properties of foods, it was appropriate to include these aspects in the form of a Knowledge Base on the website (Gibbs & Gekas, 1999).
6. Diffusion properties FickÕs second law describes mass transfer in foods. This leads to the diffusion equation, involving the diffusion coefficient. When the coefficient is known then diffusion of a given component through food can be predicted. Conversely, doing experiments with injecting a known concentration of a component in a defined sample geometry enables the diffusion coefficient to be estimated by sampling the concentration at different points in the sample. There are two fundamental approaches to modelling the diffusion coefficient. One is based on a phenomenological description of the diffusion of water on a macroscopic level, the other uses the concept of chemical potential as the driving force on ‘‘cell level’’ that means the meso- or microscopic levels. The second, modern approach is expected to gain importance in the future because it better connects the diffusion phenomena with the structure and composition of the foods (for example, disruption of biological tissues by cooking, freezing or high pressure). The NELFOOD database incorporates a Knowledge Base concerning all aspects of the apparent diffusion coefficient. This warns the food engineer and scientist of the pitfalls in using the data on diffusion properties. It also gives a useful summary of the methods used for measurement of diffusion coefficients and their use in modelling of mass transfer (Doulia, Tzia & Gekas, 1999).
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conductivity and the components of the complex permittivity at microwave frequencies (915 and 2450 MHz).
8. Optical properties Optical properties (absorption or reflection spectra) over the infrared, visible and ultra violet ranges are very important for measuring the quality of foods (composition, state of freshness). The colour of foods is one of the essential properties for the consumer. Therefore measurements of colour are very important for the food manufacturer in order to achieve uniformity and consistency of colour, for example, of tomato ketchup. The NELFOOD database contains many bibliographic references dealing with these aspects.
9. The quality of data The aim of the Database is to provide meaningful data that were carefully selected, evaluated and entered in numerical tables. The present form of the NELFOOD database enables the existing data to be read and new data to be entered on-line. The data entry consists of filling in a standard pro-forma that details the bibliographic source of the data, the description of the food and of the measuring method. The Pro-forma can have tables attached for data and equations. The quality of the data depends on two factors: (1) how well the food is characterised in terms of composition, structure and experimental conditions such as temperature and pressure and (2) on the quality of the method of measurement (accuracy and precision). For this reason the quality of the data is validated by an alphanumeric score on scales A–D and 1–4. Thus for example, the code A1 means that the description of the food and the specification of experimental condition is excellent (score A) and the method of measurement is very accurate and precise (score 1). The indication of quality of the data is very valuable to the user, because it influences the degree of confidence with which the user can apply the data for a particular application. The accuracy of data that is required for most engineering calculations is 10% or better, corresponding to scores 2 or 1.
10. Future developments 10.1. Predictive equations
7. Electrical properties Electrical properties are essentially the dielectric permittivity and loss of material. These properties are important in Ohmic and microwave heating of foods. The NELFOOD database comprises tables of the d.c.
Modern consumers in the developed countries require a large range of food products and the manufacturers are constantly introducing new food processes and products, leading to an almost infinite range of recipes and compositions. This makes it difficult to capture all
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the corresponding data on physical properties in the look-up type database. The future direction should therefore be towards a predictive database, attempting to predict the physical properties form composition and structure of the foods as is being done in the COSTHERM program for the thermal properties. Modelling of all the other properties is much more difficult, but could be attempted over narrow ranges of composition and structure. In time the gradual accumulation of the data may enable generalisation over wider ranges. This approach could be empirical, however, it is always better to have an understanding of the underlying mechanisms. Therefore the creation of the predictive models should be based on research of the influence of composition and structure of foods on their physical properties. An Expression of Interest (Nesvadba, 2002) by a consortium for food engineers and scientists under the Sixth EU Framework Programme proposed the construction of a predictive database (defined as PREDAT in Fig. 1). The aim is to construct generic models capable of encompassing this large diversity among food materials. The principal research activity will be to understand the connection between composition and structure and the physical properties and to create predictive models (links 1, 2 and 3 in Fig. 1). 10.2. Interface with models for food quality and safety To assist food scientists and technologists in predicting the Safety and Quality of foods (for example, to predict the shelf life of foods), it is necessary to construct a linking knowledge base (FOODSAQ in Fig. 1). The knowledge base FOODSAQ would act as an interface between PREDAT and the models for ‘‘real-life’’ industrial applications. The examples are predicting temperatures and water activity in food for quantifying
microbial growth and inactivation for use in risk analysis. A collection of on-line computer programs for modelling the various unit operations in the industry has been proposed (Nesvadba, 2002) to enable ‘‘what-if’’ and sensitivity analyses of the influence of physical properties of foods on the safety and quality of foods. 10.3. Artificial intelligence Even when a large number of predictive equations are available, it is unlikely that they will be able to cover all eventualities. However, the gradual accumulation of data and understanding could be exploited by modern methods of Artificial Intelligence such as Case Based or Analogical Reasoning. For example, when a skilled ‘‘intelligent user’’ does not find data or equations closely fitting a particular scenario (for example, the thermal conductivity of frozen fish), he may still obtain a satisfactory answer by using data for a similar product (for example, the thermal conductivity of frozen meat with a similar composition). The methods of Artificial Intelligence could assist in bibliographic searches, assessment of similarity between cases and provide a support for decision making. This concept could be applied to finding the data on physical properties of foods per se or, on a more systemic level, to reuse of project management experience (for example experience with freezing of meat would benefit a user needing to find solutions for the freezing of fish). One example of case based reasoning from the culinary domain is a computer program CHEF, which creates new recipes from old ones, by adapting them to requests for new ingredients or tastes (Kolodner, 1993). The Case Based Reasoning method stores previous experience (solved problems or cases) in a database. To solve a new problem the user retrieves from the database
Fig. 1. A system view of the network showing the various components. The links (numbered arrows) 1, 2 and 3 assist understanding of the dependence of the physical properties on the composition and structure of foods.
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a similar experience for a similar situation. This is then reused in the context of the new situation. The reuse may be complete or partial, or adapted according to differences between the cases. To facilitate learning, the user stores the solution for the new experience in the database. 11. Conclusions The NELFOOD database provides a systematised source of data and equations on the main physical properties of foods. The data are mainly presented as tables of numerical values. The quality of existing data has been assessed and is indicated in the database. The future work will improve the predictive features of the database. Acknowledgements The European Commission funded this work under the contract FAIR CT96-1063. Thanks are also due to the Chairmen of the physical property subgroups C.A. Miles/M.J. Morley (thermal), M. Merabet (electrical) and B.M. McKenna (optical) for their contribution to this work. References Adam, M. (Ed.). (1969). Bibliography of physical properties of foods. Czech Academy of Agriculture.
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Dealy, J. M. (1996). Official nomenclature for material functions describing the response of a viscoelastic fluid to various shearing and extensional deformations. Journal of Rheology, 39(1), 253–265. Doulia, D., Tzia, K., & Gekas V. (1999). A knowledge base for the apparent mass diffusion coefficient (Deff ) of Foods. Available: www.nelfood.com. Fischer, P., Marti, I., & Windhab, E. J. (Eds.). (2000). Proceedings of the 2nd international symposium on food rheology and structure, ETH Zurich, 12–16 March 2000, Switzerland. Gibbs, P., & Gekas, V. (1999). Water activity and microbiological aspects of foods––a knowledge base. Available: http://www.nelfood.com. Houska, M. (Ed.). (1994). Thermophysical and rheological properties of food––milk, milk products and semi-products. Food Research Institute Prague. Houska, M. (Ed.). (1997). Thermophysical and rheological properties of food––meat, meat products and semi-products. Food Research Institute Prague. Houska, M., Nesvadba, P., & Mayer, Z. (2001). Database of physical properties of foods: subgroup of mechanical and rheological properties. Journal of Texture Studies, 32, 155–160. Jowitt, R., Esher, F., Hallstr€ om, B., Meffert, H. F. Th. Spiess, W. E. L, & Vos, G. (Eds.). (1983). Physical properties of foods. Barking: Applied Science Publishers. Jowitt, R., Escher, F., Kent, M., McKenna, B. M., & Roques, M. (Eds.). (1987). Physical properties of foods––2. Amsterdam: Elsevier Applied Science. Kolodner, J. L. (1993). Case-based reasoning. San Mateo CA: Morgan Kaufmann Publishers (ISBN 1-55860-237-2). Mayer, Z. (2003). Data-bank of information about physical properties of foods at the Institute of Food Research Prague. Available: http://www.vupp.cz/envupp/research.htm. Nesvadba, P. (2002). Physical and engineering properties for the quality and safety of foods. An expression of interest for the FP6, acronym PENPROF, submitted to the European commission on 7 June 2002. Available: http://eoi.cordis.lu/docs/int_38471.doc. Singh, R. P. (1995). Food properties database. Version 2.0 for Windows. Boca Raton, FL: CRC Press.