Texture models, property

In contrast to the mechanical and rheological properties of materials, which have defined physical meanings, no such definitions exist for the psychophysical assessment of equivalent textural properties of foods. To identify material properties, or combinations of these, which are able to model sensory assessments requires a mixture of theory and experimentation. Scientific studies of food texture began during the twentieth century by the analysis of the rheological properties of liquid or semi-solid foods. In particular Kokini14 combined theoretical and experimental approaches in order to identify appropriate rheological parameters from which to derive mathematical models for textural attributes of liquid and semi-solid foods, namely, thickness, smoothness and creaminess. 

D CAD/modeler Free-form surfaces, parametric geometry, trim curves, surface/material properties, assembly information Tessellation (static dynamic), data reduction (LOD), siuface properties — > textures, materials, reflection mapping... 

Figure 6.34 shows a comparison of calculated compressional velocities with experimental data from measurements on granite of different grain sizes (Lebedev et al., 1974a,b). Forward-calculated curves cover the experimental data and indicate also for this model that textural properties are connected with the aspect ratio as model input. 

The approaches considered allow modeling of the primary texture of PS and the processes, limited by individual PBUs that mainly correspond to level III and partially to level IV in the hierarchical system of models (see Section 9.6.3). PBUs are identical in regular PSs, and simulation of numerous processes may be reduced to analysis of a process in a single PBU/C or PBU/P. An accurate modeling of the processes in irregular PSs requires the studies of the properties of structure and properties of the ensembles (clusters) of particles and pores (level IV of the system of models) and the lattices of such clusters (levels V to VII of the system of models). Let us consider the composition of clusters on the basis of fractal [127], and the lattices on the basis of percolation [8] theories. 

The bicubic patches are characterized with different colors, intensities and line textures to show attributes such as hydrophobicity and steric properties. Only one attribute may be displayed at a time, with color and intensity representing the value of the attribute, and line texture representing karma s confidence level in the information. For example, when displaying hydrophobicity, red patches are hydrophobic space while blue patches are polar space. Patches drawn with solid lines represent areas which are well explored while patches with short dashes contain little information. Displaying information using multiple cues a ows the user to examine various aspects of the surface model without having to deal with large amounts of numerical data. 

Polyolefin foams are easier to model than polyurethane (PU) foams, since the polymer mechanical properties does not change with foam density. An increase in water content decreases the density of PU foams, but increases the hard block content of the PU, hence increasing its Young s modulus. However, the microstructure of semi-crystalline PE and PP in foams is not spherulitic, as in bulk mouldings. Rodriguez-Perez and co-workers (20) showed that the cell faces in PE foams contain oriented crystals. Consequently, their properties are anisotropic. Mechanical data for PE or PP injection mouldings should not be used for modelling foam properties. Ideally the mechanical properties of the PE/PP in the cell faces should be measured. However, as such data is not available, it is possible to use data for blown PE film, since this is also biaxially stretched, and the texture of the crystalline orientation is known to be similar to that in foam faces. 

Celis, R. Cornejo, J. Hermosin, M.C. (1998) Textural properties of synthetic clay-ferrihy-drite associations. Clay Miner. 33 395-407 Celis, R. Hermosin, M.C. Cox, L. Cornejo, J. (1999) Sorption of 2,4-Dichlorophenoxyacetic acid by model particles simulating naturally occurring soil colloids. Environ. Sci. Techn. 31 1200-1206... 

Because the flrmness of potatoes seems to decrease rapidly at temperatures >70°C, in most studies, its values are modeled using first-order kinetics (Table 9.7). Defining P as a texture property, the first-order kinetics fortexture degradation can be written as (Rao and Lund, 1986) ... 

An Instron Testing System (Model 1122), fitted with a 10 cm six-wire grid (Ottawa Texture measuring system, OTMS cell) was used to determine rheological properties. A loading rate of 50 mm/min and a chart speed of 500 mm/min resulted in a well defined force-deformation curve. Force at the bioyield point and the area under the curve were calculated. These values were then converted into maximum stress, work and specific work values ... 

Q The most detailed studies were reported by Hermansson and Akesson ( , 41) and Hermansson (42) in which the properties of a soy isolate, caseinate, WPC, and model test systems of additive and lean beef or pork were studied. Solubility, swelling, and viscosity (properties reviewed as related to water absorption) were correlated with moisture loss in the raw systems. In cooked systems, the best predictability of meat texture as affected by additive was a statistical model that included the functional properties of swelling and gel strength of protein additive dispersions. 

When we apply this model to clairvoyance, we begin with some physical object or event, which is the target about which we wish to acquire information. Any object has a certain set of physical properties that define it and constitute its uniqueness. The book on your lap has a set of properties such as a certain mass, the ability to reflect certain wavelengths of light (its color), a certain surface texture, and a specific collection of printed words within it that make it not only a book but one specific book. 

Thus, triazine movement and persistence are influenced by many factors, the interactions of which are not always easy to predict. Several models have been used as tools to estimate losses and to identify variables that will impact the rate and magnitude of loss. Considering the broad range in soil properties and climatic conditions used, some models performed well. However, modeling results and predictions are only estimates, and the fate and transport of triazines in the soil environment has been shown to be affected by many factors, including concentration, soil texture, variation in climate, and differences in tillage practices. 

Watkinson, P.J., Crawford, R.A., Dodds, C.C. 2002. Effect of moisture on instrumentally measured textured properties of model cheese. Aust. J. Dairy Technol. 57, 153 (1 page). 

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