Microcapsules of a Casein Hydrolysate: Production, Characterization, and Application in Protein Bars

The aim of this work was to encapsulate a casein hydrolysate by spray drying using maltodextrins (DE 10 and 20) as wall materials and to evaluate the efficiency of the microencapsulation in attenuating the bitter taste of the hydrolysate using protein bars as the model system. Microcapsules were evaluated for morphology (SEM), particle size, hygroscopicity, solubility, thermal behavior (DSC), and bitter taste with a trained sensory panel by a paired comparison test (nonencapsulated samples vs. encapsulated samples). Bars were prepared with the addition of 3% casein hydrolysate at free or both encapsulated forms, and were then evaluated for their moisture, water activity (a(w)) and for their bitter taste by a ranking test. Microcapsules were of the matrix type, having continuous surfaces with no apparent porosity for both coatings. Both encapsulated casein hydrolysates had similar hygroscopicity, and lower values than free encapsulated hydrolysates. The degree of hydrolysis of the maltodextrin influenced only the particle size and T(g). The sensory panel considered the protein bars produced with both encapsulated materials less bitter (p < 0.05) than those produced with the free casein hydrolysates. Microencapsulation by spray drying with maltodextrin DE 10 and 20 was successful to attenuate the bitter taste and the hygroscopicity of casein hydrolysates.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

Xyloglucan nano-aggregates: Physico-chemical characterisation in buffer solution and potential application as a carrier for camptothecin, an anti-cancer drug

In this work, native xyloglucan was extracted from Tamarindus indica seeds (XGT), and its properties in phosphate buffer solution (PBS) were evaluated in comparison with a commercial tamarind kernel powder (TKP). The physico-chemical characteristics of the polysaccharides such as molar mass, critical concentration and intrinsic viscosity were determined. Furthermore, using spectroscopic and microscopy techniques, it was observed that the XGs tested can be considered macromolecules able to aggregate as nano-entities of 60-140 nm. The XGT tended to an ordered and compact spherical conformation determined by the Huggins constant, circular dichroism, atomic force microscopy and transmission electron microscopy. After the determination of the properties in PBS the XGs, at concentrations of 25% above their critical aggregation concentration, were used to encapsulate camptothecin, an anti-cancer drug. The XGT sample showed an encapsulation efficiency of 42% and first-order drug delivery kinetics. These results demonstrated the importance of knowledge of the physico-chemical properties of polysaccharides, for example, to better conduct their biotechnological applications as drug carriers. (C) 2010 Elsevier Ltd. All rights reserved.