Siouffi A M, Phan-Tan-Luu R
Faculté des Sciences de St. Jérôme, Université Aix-Marseille III, France.
J Chromatogr A. 2000 Sep 15;892(1-2):75-106. doi: 10.1016/s0021-9673(00)00247-8.
Many methods have been developed in order to optimize the parameters of interest in either chromatography or capillary electrophoresis. In chemometric approaches experimental measurements are performed in such a way that all factors vary together. An objective function is utilized in which the analyst introduces the desired criteria (selectivity, resolution, time of analysis). Simplex methods and overlapping resolution maps are declining. Factorial designs and central composite designs are more and more popular in electrodriven capillary separations since the number of parameters to master is much larger than in either GC or LC. The use of artificial neural networks is increasing. The advantage of chemometrics tools is that no explicit models are required, conversely the number of experiments to perform may be high and boundaries of the domain are not straightforward to draw and the approach does more than is required. When models are available optimization is easier to perform by regression methods. Computer assisted methods in RPLC are readily available and work well but are still in infancy in CE. Linear solvation energy relationships seem a very valuable tool but estimates of coefficients still require many experiments.
为了优化色谱法或毛细管电泳中感兴趣的参数，已经开发了许多方法。在化学计量学方法中，实验测量以所有因素共同变化的方式进行。使用一个目标函数，分析人员在其中引入所需的标准（选择性、分辨率、分析时间）。单纯形法和重叠分辨率图正在衰落。因子设计和中心复合设计在电动毛细管分离中越来越受欢迎，因为需要掌握的参数数量比气相色谱或液相色谱中的要多得多。人工神经网络的应用正在增加。化学计量学工具的优点是不需要明确的模型，相反，需要进行的实验数量可能很多，并且领域的边界不容易绘制，而且该方法做的比要求的更多。当有模型可用时，通过回归方法进行优化更容易。反相液相色谱中的计算机辅助方法很容易获得且效果良好，但在毛细管电泳中仍处于起步阶段。线性溶剂化能关系似乎是一个非常有价值的工具，但系数的估计仍然需要许多实验。
