Tag: chemometrics

Chromatographic signals comprise three components, (i) low-frequency baseline drift, (ii) high-frequency noise and, for chromatography (iii) the relative mid-frequency peaks. The first two contributions together are the “background” of the signal. Often, there is more background than chromatographic information, as each data point contains a background contribution. In such a case, or if the background is of a frequency very similar to that of the relevant signals, problems may occur with the interpretation of the data. For example, peak detection may be hindered, and errors in classification, discrimination, and, especially, quantification, may occur [1].

We earlier reported how the past decade has seen the development of a plethora of different background correction algorithms [1]. While this is technically a welcome trend, it is currently unclear which tool is useful in what case. There thus is a need for a tool to objectively compare these algorithms. This will allow other users to select the appropriate algorithm for their case.

Often scientists use simulated data for such a comparison, yet such data is often controversial as it is regarded to poorly represent a realistic case. Experimental data solves this issue, but is difficult to generate for each type of chromatographic application. Moreover, it is impossible to determine the statistically true value of, for example, the area of any given peak in the chromatogram.

In this light, scientist Leon Niezen developed a data simulation tool to generate realistic data for use in algorithm comparison studies. The tool was designed to combine experimental baseline drift and noise signals with carefully modeled chromatographic peaks. For the latter, Niezen modelled experimental chromatographic peaks with distribution functions (Figure 1).