Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundb...Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundbreaking study,we have established QuEChERS-HPLC‒MS/MS method,for the simultaneous detection of multiple plant hormones in cotton leaves,allowing the analysis and quantification of five key plant hormones.Results Sample extraction and purification employed 0.1%acetic acid in methanol and C18 for optimal recovery of plant hormones.The method applied to cotton demonstrated excellent linearity across a concentration range of 0.05–1 mg・L−1,with linear regression coefficients exceeding 0.99.The limits of quantification(LOQs)were 20μg・kg−1 for GA3 and 5μg・kg−1 for the other four plant hormones.Recovery rates for the five plant hormones matrix spiked at levels of 5,10,100,and 1000μg・kg−1 were in the range of 79.07%to 98.97%,with intraday relative standard deviations(RSDs)ranging from 2.11%to 8.47%.The method was successfully employed to analyze and quantify the five analytes in cotton leaves treated with plant growth regulators.Conclusion The study demonstrates that the method is well-suited for the determination of five plant hormones in cotton.It exhibits excellent selectivity and sensitivity in detecting field samples,thus serving as a robust tool for indepth research into cotton physiology.展开更多
粉尘浓度预测可为粉尘防治提供依据。为探明国内外露天矿粉尘浓度预测研究进展,针对中国知网和Web of Science收录的粉尘浓度预测相关文献,采用CiteSpace、VOSviewer可视化图谱分析软件,从文献计量学角度对关键词进行处理,提取高频关键...粉尘浓度预测可为粉尘防治提供依据。为探明国内外露天矿粉尘浓度预测研究进展,针对中国知网和Web of Science收录的粉尘浓度预测相关文献,采用CiteSpace、VOSviewer可视化图谱分析软件,从文献计量学角度对关键词进行处理,提取高频关键词、研究热点,生成聚类图谱和热点时间轴。根据可视化分析结果,从露天矿粉尘浓度影响因素、指标体系、预测方法3个方面进行分析。结果表明:露天矿粉尘浓度的影响因素研究主要体现在生产强度和气象因素方面,选取的粉尘浓度预测指标通常有剥离量、采煤量、温度、湿度、风速、风向等;粉尘浓度预测方法主要采用神经网络、随机森林、线性回归等以及结合粒子群、遗传算法优化的组合算法预测模型。未来露天矿粉尘浓度预测研究应深入挖掘影响粉尘浓度的因素和科学建立预测指标体系,加强机器学习和智能优化算法的组合应用。展开更多
基金National Key R&D Program of China(2022YFD1400300)Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural SciencesChina Agriculture Research System.
文摘Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundbreaking study,we have established QuEChERS-HPLC‒MS/MS method,for the simultaneous detection of multiple plant hormones in cotton leaves,allowing the analysis and quantification of five key plant hormones.Results Sample extraction and purification employed 0.1%acetic acid in methanol and C18 for optimal recovery of plant hormones.The method applied to cotton demonstrated excellent linearity across a concentration range of 0.05–1 mg・L−1,with linear regression coefficients exceeding 0.99.The limits of quantification(LOQs)were 20μg・kg−1 for GA3 and 5μg・kg−1 for the other four plant hormones.Recovery rates for the five plant hormones matrix spiked at levels of 5,10,100,and 1000μg・kg−1 were in the range of 79.07%to 98.97%,with intraday relative standard deviations(RSDs)ranging from 2.11%to 8.47%.The method was successfully employed to analyze and quantify the five analytes in cotton leaves treated with plant growth regulators.Conclusion The study demonstrates that the method is well-suited for the determination of five plant hormones in cotton.It exhibits excellent selectivity and sensitivity in detecting field samples,thus serving as a robust tool for indepth research into cotton physiology.
文摘粉尘浓度预测可为粉尘防治提供依据。为探明国内外露天矿粉尘浓度预测研究进展,针对中国知网和Web of Science收录的粉尘浓度预测相关文献,采用CiteSpace、VOSviewer可视化图谱分析软件,从文献计量学角度对关键词进行处理,提取高频关键词、研究热点,生成聚类图谱和热点时间轴。根据可视化分析结果,从露天矿粉尘浓度影响因素、指标体系、预测方法3个方面进行分析。结果表明:露天矿粉尘浓度的影响因素研究主要体现在生产强度和气象因素方面,选取的粉尘浓度预测指标通常有剥离量、采煤量、温度、湿度、风速、风向等;粉尘浓度预测方法主要采用神经网络、随机森林、线性回归等以及结合粒子群、遗传算法优化的组合算法预测模型。未来露天矿粉尘浓度预测研究应深入挖掘影响粉尘浓度的因素和科学建立预测指标体系,加强机器学习和智能优化算法的组合应用。
