Breaking with trends in pre-processing?

• Pre-processing can make or break data analysis.
• Selection of appropriate pre-processing strategies is crucial.
• Three types of selection approaches seem commonly used.
• All approaches have serious drawbacks and can provide misleading results.
• Objective approaches to quality parameters are required for future research.

Data pre-processing is an essential part of chemometric data analysis, which aims to remove unwanted variation (such as instrumental artifacts) and thereby focusing on the variation of interest. The choice of an optimal pre-processing method or combination of methods may strongly influence the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis. This first critical review is devoted to the selection procedure for appropriate pre-processing strategies. We show that breaking with current trends in pre-processing is essential, as all selection approaches have serious drawbacks and cannot be properly used.