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Summary. The paper compares linear, quadratic, and cubical regression together with several weighted and robust approaches in the context of lipophilicity determination. The comparison is done on 35 model compounds on data from different modifiers used on RP18, CN, and silica plates. It can be concluded that the use of weighted and moderately robust regression technique increases correlation between extrapolated retention and real lipophilicity, whereas polynomial and very robust techniques give visibly worse results due to their excessive flexibility and higher extrapolation uncertainty. Additionally, we have compared averaging retention from different modifiers by R F , k, and R M values. The results are similar; however, surprisingly, R F averaging performs slightly better to the other approaches.

This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.