Abstract: Method and apparatus is described for identifying a subset of components of a system, the subset being capable of predicting a feature of a test sample. The method comprises generating a linear combination of components and component weights in which values for each component are determined from data generated from a plurality of training samples, each training sample having a known feature. A model is defined for the probability distribution of a feature wherein the model is conditional on the linear combination and wherein the model is not a combination of a binomial distribution for a two class response with a probit function linking the linear combination and the expectation of the response. A prior distribution is constructed for the component weights of the linear combination comprising a hyperprior having a high probability density close to zero, and the prior distribution and the model are combined to generate a posterior distribution.

Abstract: A method for identifying components of a system from data generated from the system, which exhibit a response pattern associated with a test condition applied to the system, comprising the steps of specifying design factors to specify a response pattern for the test condition and identifying a linear combination of components from the input data which correlate with the response pattern.

Abstract: Debranched amylopectin and debranched low-DE maltodextrin have been found to cause protein to aggregate in solution. Such aggregation, which can lead to precipitation or flocculation of the protein, is useful for instance in aerated aqueous fat emulsions, e.g. ice cream and whipped cream, where the stability of the product can be altered as desired.