Exploratory data analysis analytical chemistry

Chemometrics tools can be used for a wide variety of tasks, including experimental design, exploratory data analysis, and the development of predictive models. In the context of analytical chemistry, however, chemometrics has been shown to be most effective for two general functions ... 

A trend to more complex problems and the availability of automated instruments are novel aspects of modern scientific research. When complex problems are investigated it is usually necessary to characterize an object (e.g. a sample, a reaction, a fact) not only by one parameter (measurement, feature) but by several parameters. The aim of the investigation is often to obtain a better insight into the treated problem, rather in a qualitative than in a quantitative manner. In chemistry such demands for an exploratory data analysis frequently arise in connection with analytical work on complex samples, e.g. environmental samples and also in the field of structure-property-relationships. With modern, sometimes called intelligent, instruments a great amount of data can easily be obtained from samples. The bottle-neck in this work is the data interpretation. 

After applying the appropriate pre-processing, different chemometric techniques can be applied according to the aim of the study. Pattern recognition is one of the chemometric methods most used in analytical chemistry and this is true for separations data. Pattern recognition can be generally divided into two classes exploratory data analysis and unsupervised and supervised pattern recognition (Otto, 2007 Brereton, 2007). 

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