Measurement of glass-rubber transition temperature of skim milk powder by static mechanical test

Stickiness behavior of skim milk powder was investigated based on the mechanical property of the material during the glass-rubber transition. A thermally controlled device was developed for the static mechanical test. This device was attached to a texture analyzer, and skim milk powder, which was used as a model sample, was tested for its glass-rubber transition temperature (Tg-r) using static compression technique (creep test). Changes in compression probe distance as a function of temperature were recorded. Tg-r was determined, in the region where changes in the probe distance were observed, by using linear regression technique. The effect of sample quantity, compression force, and heating rate on the determination of Tg-r was investigated. All these parameters significantly influenced the Tg-r determination (p < 0.05). The Tg-r of skim milk powder measured by this novel technique was found closely correlated to its glass transition temperature (T-g) measured by DSC.

Simple texture-based liberation modelling of ores

Mineralogical analysis is often used to assess the liberation properties of particles. A direct method of estimating liberation is to actually break particles and then directly obtain liberation information from applying mineralogical analysis to each size-class of the product. Another technique is to artificially apply random breakage to the feed particle sections to estimate the resultant distribution of product particle sections. This technique provides a useful alternative estimation method. Because this technique is applied to particle sections, the actual liberation properties for particles can only be estimated by applying stereological correction. A recent stereological technique has been developed that allows the discrepancy between the linear intercept composition distribution and the particle section composition distribution to be used as guide for estimating the particle composition distribution. The paper will show results validating this new technique using numerical simulation. (C) 2004 Elsevier Ltd. All rights reserved.

Comparison of texture of yogurt made from conventionally treated milk and UHT milk fortified with low-heat skim milk powder

The textures of yogurt made from ultra-high temperature (UHT) treated and conventionally treated milks at high total solids were investigated. The yogurt premixes, fortified with low-heat skim milk powder to 16%, 18%, and 20% total solids, were UHT processed at 143 degreesC for 6 s and heated at 85 degreesC for 30 min using the conventional method. The onset of gelation was delayed in the UHT-processed milk compared with conventionally heated milk. During fermentation, the viscosity of yogurt made, from UHT-treated milk at 20% total solids was close to that of yogurt made from conventionally treated milk with 16% total solids. However, after storage for greater than or equal to1 d, the yogurt made from UHT-treated milk had lower viscosity and gel strength than the yogurt made from conventionally treated milk. The solids level had no influence on yogurt culture growth.

Application of edge-to-edge matching model to understand the in-plane texture of TiSi2 (C49) thin films on (001)(Si) surface

The edge-to-edge matching model, which was originally developed for predicting crystallographic features in diffusional phase transformations in solids, has been used to understand the formation of in-plane textures in TiSi2 (C49) thin films on Si single crystal (001)si surface. The model predicts all the four previously reported orientation relationships between C49 and Si substrate based on the actual atom matching across the interface and the basic crystallographic data only. The model has strong potential to be used to develop new thin film materials. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.