Caroline West | Authors

Decision trees offer great visuals to observe complex data sets and to classify data according to simple decision rules.

The sum of ranking differences (SRD) is a useful statistical tool for comparing methods, models, columns, or samples. It is also simple and straightforward.

Mixed-mode high performance liquid chromatography (MM-HPLC) involves the combined use of two (or more) retention mechanisms in a single chromatographic system. Many original stationary phases have been proposed in recent years with promising possibilities, while applications have only started to appear in the literature. In this review, the authors discuss mixed-mode chromatography stationary phases. An overview of applications using mixed-mode chromatography is described, as well as the increased interest in mixed-mode systems for two-dimensional chromatography.

Derringer desirability functions are a great favorite of mine because they are very simple and flexible. They may be applied to a variety of problems: whenever you need to select the “best” (sample, method, operating conditions etc.) from a set. It is also a convenient way to compare apples and oranges, whenever totally unrelated features must be ranked. They were first described by Derringer and Markham1 to select polymeric materials based on varied properties.

Part V of this series takes a closer look at discriminant analysis (DA). Discriminant analysis is a supervised method, meaning that it involves some previous knowledge of your samples.

Part IV of this series takes a closer look at clustering. Clustering can be very useful at observing your data when the sample dimensionality is large. This is a barbarian term meaning that diversity among your samples may be wide. In that case, the space reduction provided by principal component analysis (PCA) is not always convincing, because the simplification provided by a single two-dimensional plot erases too much information. Clustering allows you to preserve more information.