Selection of operational parameters for the production of instant soy protein isolate by pulsed fluid bed agglomeration

The objective of this study was to select the optimal operational conditions for the production of instant soy protein isolate (SPI) by pulsed fluid bed agglomeration. The spray-dried SPI was characterized as being a cohesive powder, presenting cracks and channeling formation during its fluidization (Geldart type A). The process was carried out in a pulsed fluid bed, and aqueous maltodextrin solution was used as liquid binder. Air pulsation, at a frequency of 600 rpm, was used to fluidize the cohesive SPI particles and to allow agglomeration to occur. Seventeen tests were performed according to a central composite design. Independent variables were (i) feed flow rate (0.5-3.5 g/min), (ii) atomizing air pressure (0.5-1.5 bar) and (iii) binder concentration (10-50%). Mean particle diameter, process yield and product moisture were analyzed as responses. Surface response analysis led to the selection of optimal operational parameters, following which larger granules with low moisture content and high process yield were produced. Product transformations were also evaluated by the analysis of size distribution, flowability, cohesiveness and wettability. When compared to raw material, agglomerated particles were more porous and had a more irregular shape, presenting a wetting time decrease, free-flow improvement and cohesiveness reduction. (C) 2010 Elsevier B.V. All rights reserved.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Cutpoint selection for discretizing a continuous covariate for generalized estimating equations

We consider consider the problem of dichotomizing a continuous covariate when performing a regression analysis based on a generalized estimation approach. The problem involves estimation of the cutpoint for the covariate and testing the hypothesis that the binary covariate constructed from the continuous covariate has a significant impact on the outcome. Due to the multiple testing used to find the optimal cutpoint, we need to make an adjustment to the usual significance test to preserve the type-I error rates. We illustrate the techniques on one data set of patients given unrelated hematopoietic stem cell transplantation. Here the question is whether the CD34 cell dose given to patient affects the outcome of the transplant and what is the smallest cell dose which is needed for good outcomes. (C) 2010 Elsevier BM. All rights reserved.