the instability of a feature selection method itself. The attempt to model interactions of heterogeneous complex chemical entities certainly is not an easy task.

Nevertheless, the random forest models improved significantly on the results published earlier with linear models. This results show utility of the machine learning methods for modelling the thin layer chromatography in particular and chemometry in general.

In many cases the relations between descriptive and response variables are complex and non-linear, thus the utility of linear models, even those including numerous variables, is limited. In comparison the random forest regression works very well, giving results that are significantly better and also more stable than those obtained with linear models. The question, whether application of other machine learning methods could improve on the current results will be explored in further studies, nevertheless, given that random forest is generally rather robust method no dramatic improvements are expected.

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