肟菌酯中间体杂质分离效果的对比数据集

1、数据采集：从4组实验（1组最优条件实验+3组对比实验）中，采集肟菌酯中间体检测的关键变量参数与对应分离效果数据。关键变量参数包括流动相pH调节剂类型、流动相乙腈与水体积比、检测波长；分离效果数据涵盖目标峰与相邻杂质峰分离状态、各峰拖尾现象、目标峰出峰时间及分析周期。仪器、色谱柱、柱温、流速、进样体积、样品制备方式等条件，除1组对比实验有额外步骤外，其余实验保持统一；
2、数据处理：1）按“单一变量”原则分组形成3组对照，分别针对流动相pH调节剂类型、流动相乙腈比例、流动相乙腈比例与检测波长，其余参数一致；2）从实验附图及文字描述中提取并记录每组对照实验的分离效果指标，结合附图时间轴记录分析周期；3）将“变量参数”与“分离效果指标”一一对应，形成结构化数据，明确不同变量调整对分离效果的具体影响；
3、数据应用：依据“变量-效果”关联数据，验证最优检测条件的唯一性与优越性，同时为同类酯类化合物检测方法开发提供参数优化参考，减少方法开发成本。

1. Data Collection: Key variable parameters and corresponding separation effect data for the detection of trifloxystrobin intermediates are collected from 4 sets of experiments (1 optimal condition experiment + 3 control experiments). The key variable parameters include the type of mobile phase pH regulator, the volume ratio of acetonitrile to water in the mobile phase, and the detection wavelength. The separation effect data covers the separation status between the target peak and adjacent impurity peaks, peak tailing phenomena, the retention time of the target peak, and the analysis cycle. Conditions including chromatographic instruments, chromatographic columns, column temperature, flow rate, injection volume, and sample preparation method remain consistent across all experiments, except for additional steps in 1 control experiment.
2. Data Processing: 1) Three control groups are established following the single-variable principle, targeting the type of mobile phase pH regulator, the proportion of acetonitrile in the mobile phase, and the combination of acetonitrile proportion in the mobile phase and detection wavelength respectively, with all other parameters kept consistent; 2) Separation effect indicators of each control group are extracted and recorded from the experimental figures and textual descriptions, and the analysis cycle is recorded combined with the time axis in the figures; 3) The "variable parameters" and "separation effect indicators" are matched one-to-one to form structured data, clarifying the specific impacts of different variable adjustments on the separation effect.
3. Data Application: Based on the "variable-effect" correlation data, the uniqueness and superiority of the optimal detection condition are verified. Additionally, it provides reference for parameter optimization during the development of detection methods for similar ester compounds, reducing the cost of method development.