School of Chemistry, University of Manchester, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK.
Anal Bioanal Chem. 2010 Jul;397(5):1893-901. doi: 10.1007/s00216-010-3739-z. Epub 2010 May 4.
In most optimisation experiments, a single parameter is first optimised before a second and then third one are subsequently modified to give the best result. By contrast, we believe that simultaneous multiobjective optimisation is more powerful; therefore, an optimisation of the experimental conditions for the colloidal SERS detection of L-cysteine was carried out. Six aggregating agents and three different colloids (citrate, borohydride and hydroxylamine reduced silver) were tested over a wide range of concentrations for the enhancement and the reproducibility of the spectra produced. The optimisation was carried out using two methods, a full factorial design (FF, a standard method from the experimental design literature) and, for the first time, a multiobjective evolutionary algorithm (MOEA), a method more usually applied to optimisation problems in computer science. Simulation results suggest that the evolutionary approach significantly out-performs random sampling. Real experiments applying the evolutionary method to the SERS optimisation problem led to a 32% improvement in enhancement and reproducibility compared with the FF method, using far fewer evaluations.
在大多数优化实验中，首先优化一个参数，然后再修改第二个和第三个参数，以获得最佳结果。相比之下，我们认为同时进行多目标优化更加强大；因此，我们对胶体 SERS 检测 L-半胱氨酸的实验条件进行了优化。我们测试了六种聚集剂和三种不同的胶体（柠檬酸盐、硼氢化钠和羟胺还原银），以考察它们在产生的光谱的增强和重现性方面的广泛浓度范围内的效果。优化使用了两种方法，全因子设计（FF，实验设计文献中的标准方法）和多目标进化算法（MOEA），这是一种更常用于计算机科学中的优化问题的方法。模拟结果表明，进化方法的性能明显优于随机抽样。将进化方法应用于 SERS 优化问题的实际实验与 FF 方法相比，使用的评估次数更少，但增强和重现性提高了 32%。
