Particle formation during soy protein precipitation

The current success of soy foods is driving soy ingredient manufacturers to develop innovative products for food manufacturers. One such innovation is separating the soy proteins glycinin and beta-conglycinin to take advantage of their individual functional and nutritional properties. Precipitation by acidification is a low-cost method for separating these two proteins. Separation is achieved by preferentially precipitating glycinin at pH ~ 6 while beta-conglycinin remains in solution. Understanding the particle formation during protein precipitation is important as it can influence the efficiency of the precipitation process as well as subsequent downstream processes such as the particle-liquid separation step, usually achieved by centrifugation. Most of the previous soy protein precipitation studies are limited to precipitation at pH 4 as this is the pH range most commonly used in the commercial manufacturing of soy protein isolates. To date, there have been no studies on the particle formation during precipitation at pH > 5.Precipitation of soy protein is generally thought to occur by the rapid formation of primary particles in the size range of 0.1 - 0.3 microns followed by aggregation of these particles via collision to aggregates of size about 1 - 50 microns. The formation of the primary particles occurs on a time scale much shorter than that of the overall precipitation process (Nelson and Glatz, 1985). This study shows that precipitation of soy protein is indeed rapid. At high pH levels, binary liquid-liquid separation occurs forming a protein-rich heavy phase. The protein-rich phase appears as droplets which can be coalesced to form a uniform bulk layer under centrifugation forces. Upon lowering the pH level by the addition of acid, further protein is precipitated as amorphous material which binds the droplets together to form aggregates of amorphous precipitates. Liquid-liquid separation has been observed in many protein solutions but this phenomenon has only scarcely been reported in the literature for soy proteins. It presents an exciting opportunity for an innovative product. Features of the liquid-phase protein such as protein yield and purity will be characterized.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Efficient spatial clustering algorithm using binary tree

In this paper we present an efficient k-Means clustering algorithm for two dimensional data. The proposed algorithm re-organizes dataset into a form of nested binary tree*. Data items are compared at each node with only two nearest means with respect to each dimension and assigned to the one that has the closer mean. The main intuition of our research is as follows: We build the nested binary tree. Then we scan the data in raster order by in-order traversal of the tree. Lastly we compare data item at each node to the only two nearest means to assign the value to the intendant cluster. In this way we are able to save the computational cost significantly by reducing the number of comparisons with means and also by the least use to Euclidian distance formula. Our results showed that our method can perform clustering operation much faster than the classical ones. © Springer-Verlag Berlin Heidelberg 2005