Texture analysis software

A list of traditional pole figure software vi. whole profile software analysis is listed in Table 17.18. 

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LaboTex The Texture Analysis Software, K. Pawlik and P. Ozga, Gottinger Arbeiten zur Geologic und Palaontologie, SB4, 1999 MAUD (Material Analysis Using Diffraction) a user friendly (Java) program for Rietveld) Texture Analysis and more,... 

Table 17.18 Available texture and pole figure analysis software.

Texture profile analysis (TPA) was determined using the TA.TX2i texturometer (Stable Micro Systems, Surrey, UK), operated with the Texture Expert software, using a pretest velocity of 2 mm/ s, test velocity of 1 mm/ s and posttest velocity of 2 mm/ s, with a distance of 5 mm and time of 5 s, the test body being acrylic (P25/L) with a diameter of 25 mm. The product was placed in an aluminium capsule with a diameter of 50 mm and height of 25 mm for standardization. A graph of force x time was constructed, each characteristic peak corresponding to one of the TP A characteristics. 

There are today standard computer controlled x ray diffraction equipments for pole figure measurements as well as software packages for ODF calculation so that only a general knowledge about texture analysis is usually required of researchers who intend to study the anisotropic behavior of materials. Moreover, many papers have demonstrated how physical or mechanical properties can be calculated in textured materials. This is especially true for cubic metals and alloys and, to a lesser extent, for hexagonal alloys. There remain, therefore, many studies to be undertaken, particularly for non-metallic polycrystalline materials. 

The data from sensory evaluation and texture profile analysis of the jellies made with amidated pectin and sunflower pectin were subjected to Principal component analysis (PC) using the statistical software based on Jacobi method (Univac, 1973). The results of PC analysis are shown in figure 7. The plane of two principal components (F1,F2) explain 89,75 % of the variance contained in the original data. The attributes related with textural evaluation are highly correlated with the first principal component (Had.=0.95, Spr.=0.97, Che.=0.98, Gum.=0.95, Coe=0.98, HS=0.82 and SP=-0.93). As it could be expected, spreadability increases along the negative side of the axis unlike other textural parameters. 

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