Two-dimensional fingerprinting approach for comparison of complex substances analysed by HPLC-UV and fluorescence detection

Abstract

This work is concerned with the research and development of methodology for analysis of complex mixtures such as pharmaceutical or food samples, which contain many analytes. Variously treated samples (swill washed, fried and scorched) of the Rhizoma atractylodis macrocephalae (RAM) traditional Chinese medicine (TCM) as well as the common substitute, Rhizoma atractylodis (RA) TCM were chosen as examples for analysis. A combined data matrix of chromatographic 2-D HPLC-DAD-FLD (two-dimensional high performance liquid chromatography with diode array and fluorescence detectors) fingerprint profiles was constructed with the use of the HPLC-DAD and HPLC-FLD individual data matrices; the purpose was to collect maximum information and to interpret this complex data with the use of various chemometrics methods e.g. the rank-ordering multi-criteria decision making (MCDM) PROMETHEE and GAIA, K-nearest neighbours (KNN), partial least squares (PLS), back propagation-artificial neural networks (BP-ANN) methods. The chemometrics analysis demonstrated that the combined 2-D HPLC-DAD-FLD data matrix does indeed provide more information and facilitates better performing classification/prediction models for the analysis of such complex samples as the RAM and RA ones noted above. It is suggested that this fingerprint approach is suitable for analysis of other complex, multi-analyte substances.