Physicochemical properties and leaching behavior of eight U.S. long-grain rice cultivars as related to rice texture

There are many long-grain rice cultivars produced commercially in the U.S.; however, little work has been done on correlating the structure and physicochemical properties of starch with their texture. The physicochemical properties, leaching behavior, and texture attributes of eight longgrain rice cultivars were studied. Differences were observed in the approximate composition of kernels, including crude protein (6.6-9.3%), crude lipid (0.18-0.51%), and apparent amylose content (25.5-30.9%). These cultivars also differed slightly in thermal properties, such as onset temperature (73.7° to 77.4°C) and peak temperature (78.8° to 81.9°C). Although they showed a similar pasting temperature, their peak viscosities ranged from 680 to 982 Brabender units. The amount and the molecular size distribution of the leached starch molecules varied greatly among the samples. The leached amylose, instead of the apparent amylose, was suggested to play an important role in cooked rice texture

Stochastic Trust Region Methods with Trust Region Radius Depending on Probabilistic Models

We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic models. Especially, we propose a specific algorithm, termed STRME, in which the trust-region radius depends linearly on the latest model gradient. The complexity of STRME method in non-convex, convex and strongly convex settings has all been analyzed, which matches the existing algorithms based on probabilistic properties. In addition, several numerical experiments are carried out to reveal the benefits of the proposed methods compared to the existing stochastic trust-region methods and other relevant stochastic gradient methods