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Testing experimental designs in liquid chromatography (I): Development and validation of a method for the comprehensive inspection of experimental designs

机译:试验液相色谱(I)的实验设计:开发和验证实验设计综合检查的方法

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The basis of interpretive optimisation in liquid chromatography is the prediction of resolution, from appropriate solute retention models. The reliability of the process depends critically on the quality of the experimental design. This work develops, validates and applies a general methodology aimed to evaluate the quality of any training experimental design, which will be applied in Part II to design optimisation. The methodology is based on the systematic evaluation of the uncertainties associated to the prediction of retention times in comprehensive scans of both isocratic and gradient experimental conditions. It is able to evaluate comprehensively experimental designs of arbitrary complexity. Five common training experimental designs were used to model the retention, according to the Linear Solvent Strength (LSS) and the Neue-Kuss (NK) equations, using a set of 14 sulphonamides of different polarity. The results are presented in terms of relative uncertainties in predictions, which provide significant and interpretable results. The magnitude of such uncertainties, together with the systematic, coherent and logical changes observed at increasing solute hydrophobicity, give support to the results. The NK model gave smaller errors and unbiased predictions, whereas the LSS model gave rise to lack of fit. Isocratic training designs, which are widely accepted as the most informative, are confirmed as the best. As a general conclusion, gradients are predicted with intrinsically smaller uncertainties, independently of the training experimental design. In addition, gradients are more insensitive than isocratic predictions with regard to the type of training design used. Isocratic predictions deteriorate quickly with mobile phase composition. This explains the better performance of gradient predictions, even with biased models. (C) 2020 Elsevier B.V. All rights reserved.
机译:液相色谱中解释优化的基础是从适当的溶质保留模型中预测分辨率。该过程的可靠性主要取决于实验设计的质量。这项工作开发,验证和应用旨在评估任何培训实验设计的质量的一般方法,该设计将应用于第二部分以设计优化。该方法基于对综合扫描在等梯度实验条件的综合扫描中的保留时间相关的不确定性的系统评价。它能够评估全面的任意复杂性的实验设计。根据线性溶剂强度(LSS)和Neue-Kuss(NK)方程,使用五种常见的训练实验设计来模拟保留,使用一组不同极性的14个氧化物。结果是在预测中的相对不确定性方面提出,这提供了重大和可解释的结果。这种不确定因素的大小与在增加溶质疏水性时观察到的系统,相干和逻辑变化,给予结果。 NK模型给出了较小的错误和无偏见的预测,而LSS模型会产生缺乏契合。广泛被认为是最具信息丰富的等家庭训练设计得到确认为最好的。作为一般的结论,预测梯度以本质上较小的不确定性,独立于训练实验设计。此外,梯度比关于所用训练设计的类型更不敏感。使用流动相组合物迅速恶化等家庭预测。这解释了渐变预测的更好性能,即使有偏置模型。 (c)2020 Elsevier B.v.保留所有权利。

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