Wheat grain cooking process as investigated by modulated temperature differential scanning calorimetry

The thermal properties of soft and hard wheat grains, cooked in a steam pressure cooker, as a function of cooking temperature and time were investigated by modulated temperature differential scanning calorimetry (MTDSC). Four cooking temperatures (110, 120, 130 and 140 degrees C) and six cooking times (20, 40, 60, 80, 100 and 120 min) for each temperature were studied. It was found that typical non-reversible heat flow thermograms of cooked and uncooked wheat grains consisted of two endothermic baseline shifts localised around 40-50 degrees C and then 60-70 degrees C. The second peaks of non-reversible heat flow thermograms (60-70 degrees C) were associated with starch gelatinisation. The degree of gelatinisation was quantified based on these peaks. In this study, starch was completely gelatinised within 60-80 min for cooking temperatures at 110-120 degrees C and within 20 min for cooking temperatures at 130-140 degrees C. MTDSC detected reversible endothermic baseline shifts in most samples, localised broadly around 48-67 degrees C with changes in heat capacity ranging from 0.02 to 0.06 J/g per degrees C. These reversible endothermic baseline shifts are related to the glass transition, which occurs during starch gelatinisation. Data on the specific heat capacity of the cooked wheat samples are provided. (C) 2005 Elsevier Ltd. All rights reserved.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.