Retention Prediction in Reversed-Phase Liquid Chromatography

反相液相色谱中的保留预测

基本信息

批准号：
60550537
负责人：
JINNO Kiyokatsu
金额：
$ 1.28万
依托单位：
Toyohashi University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1985
资助国家：
日本
起止时间：
1985 至 1986
项目状态：
已结题

项目摘要

The selection of satisfactory separation conditions is still a major problem in LC. At present, trial and error experiments are mostly performed in actual analysis to find the best optimal separation for the purpose. To improve this problem, the retention prediction is one of the best way. If retention of solutes can be predicted at appropriate experimental condition, optimization procedure can be more easily attained in a short time using modern computer technique.We have investigated the basic concept of retention prediction and optimization, and obtained the following successful results:1; retention prediction system for aromatic compounds, especially PAHs, has been constructed. Using this concept, a computer interactive identification system for PAHs has also been constructed. These concepts can be applied for SFC as well as LC. Total computer-assisted system has been assembled on a 16-bits microcomputer and the potential of this system was evaluated for the practical sample, where PAHs in diesel particulate matter were identified by coupling this system with microcomputer-assisted photodiode array UV multichannel detector.2; basic studies for retention prediction of PTH-amino acids has been performed and the systemization is in progress.3; basic studies for retention prediction of amino acids and peptides has been tried and the best way to establish the retention prediction system has been found.The results of this investigation can open the new dimension of LC and SFC optimization process and the separation system assisted by computer will be realized in the near future using the proposed concept.

选择满意的分离条件仍然是LC中的一个主要问题。目前，在实际分析中大多进行试错实验，以找到适合目的的最佳分离效果。为了改善这个问题，保留预测是最好的方法之一。如果可以在适当的实验条件下预测溶质的保留，则可以利用现代计算机技术在短时间内更容易地实现优化过程。我们研究了保留预测和优化的基本概念，并获得了以下成功结果：1；构建了芳香族化合物特别是PAHs的保留预测系统。利用这一概念，还构建了多环芳烃计算机交互式识别系统。这些概念可应用于 SFC 和 LC。整个计算机辅助系统已组装在 16 位微型计算机上，并针对实际样品评估了该系统的潜力，通过将该系统与微型计算机辅助光电二极管阵列紫外多通道检测器耦合来识别柴油颗粒物中的 PAH。2 ; PTH-氨基酸保留预测的基础研究已经完成，系统化正在进行中3；尝试了氨基酸和肽保留预测的基础研究，并找到了建立保留预测系统的最佳方法。这项研究的结果可以打开LC和SFC优化过程以及计算机辅助分离系统的新维度将在不久的将来利用所提出的概念来实现。