Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics.

Thin layer chromatography in combination with image analysis and advanced chemometric methods were successfully used to classify the medicinal herbs according to their therapeutic effects and usage. The investigations were conducted using two types of plates (HPTLC Silica gel 60 and HPTLC Silica gel 60 F254) which were evaluated in UV light at 254 and 365 nm. The holistic evaluation of the numerical data corresponding different image processing channels (blue, grey, red, green) was performed by employing appropriate multivariate methods: hierarchical cluster analysis (HCA), principal component analysis (PCA), fuzzy principal component analysis (FPCA) and linear discriminant analysis (LDA) applied to the first relevant principal components. The results obtained by applying LDA method indicate a highly accurate separation of the medicinal herbs within the four groups, in good agreement with therapeutic effects and usage. According to this classification, the best image processing channels were identified for each of the investigated HPTLC plates: blue channel for HPTLC Silica gel 60 F254 (with 92.9% percent of discrimination in case of PCA and FPCA) and respectively red channel for HPTLC Silica gel 60 (with 93.9% percent of discrimination in case of FPCA). The 2D and 3D score scatterplots illustrate also the accurate and reliable discrimination between the four distinct groups.