Effects of the duration of post-silking drought on the starch physicochemical properties of waxy maize.

BACKGROUND: Drought is a major abiotic stress that affects the physicochemical properties of cereal starch. However, quantitative information on the effects of drought duration on the starch quality of waxy maize, a special maize-type starch composed of nearly pure amylopectin, has been lacking. The effects of post-silking drought duration 1-10 (DS10), 1-20 (DS20), and 1-30 (DS30) days after pollination on the physicochemical properties of starch were assessed from 2019 to 2020 using two waxy maize hybrids as materials. RESULTS: With extending drought duration, the starch granule size and average amylopectin chain length of Jingkenuo2000 (JKN2000) gradually increased, with those of Suyunuo5 (SYN5) being the highest for DS20, followed by DS30. All drought durations decreased the degree of branching of both hybrids, with the lowest value obtained for DS30 and DS20 in JKN2000 and SYN5, respectively. Relative crystallinity increased for DS30 in both hybrids but its responses for DS10 and DS20 differed. Pasting viscosities and gelatinization enthalpy were decreased and retrogradation percentage was increased by drought stress. The lowest pasting viscosities were observed for DS30, and the highest retrogradation percentage was found for DS10 in general. CONCLUSION: Post-silking drought led to the pasting and retrogradation properties deteriorating, with decreased pasting viscosities and increased retrogradation percentage. The decrease in viscosity was caused by enlarged granules. Meanwhile, the increased proportion of amylopectin chains with a degree of polymerization of 25-36 resulted in lower viscosity and higher retrogradation. © 2022 Society of Chemical Industry.

Research on river water quality evaluation based on the GA-PP and improved fuzzy model.

To ensure the water quality of rivers, it is crucial to scientifically evaluate their water quality status. This study takes a river in Jiangsu, China, as an example to establish six targeted main indicators for river water quality evaluation and uses a projection pursuit model optimized by the genetic algorithm to determine weights. Applying the improved fuzzy evaluation model to the final evaluation of water quality, the results indicate that this article adopts a weight calculation model that reduces dimensionality without losing data features, and the comprehensive evaluation model is also more complete, resulting in more accurate evaluation results. According to model analysis, the summer water quality is good and peaks from June to July. This article proposes corresponding measures and suggestions in response to the reasons behind this seasonal change. The evaluation model used in this article is superior to other models in terms of accuracy and portability, making it an excellent choice for river water quality evaluation. It can provide valuable technical guidance for similar river water quality evaluations.