QUANTIFICATION OF BIOMEDICAL NMR DATA USING ARTIFICIAL NEURAL-NETWORKANALYSIS - LIPOPROTEIN LIPID PROFILES FROM H-1-NMR DATA OF HUMAN PLASMA

Artificial neural network (ANN) analysis is a new technique in NMR spe ctroscopy, It is very often considered only as an efficient 'black-box ' tool for data classification, but we emphasize here that ANN analysi s Is also powerful for data quantification, The possibility of finding out the biochemical rationale controlling the ANN outputs is presente d and discussed, Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to th ose of sophisticated lineshave fitting (LF) analysis, The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks, The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap, The ANN analysis en ables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a H-1 NMR spectrum of a plasma sample in <1 h, The ANN e xtension presented is believed to increase the value of the H-1 NMR ba sed lipoprotein quantification to the point that it could be the metho d of choice in some advanced research settings. Furthermore, the excel lent quantification performance of the ANN analysis, demonstrated in t his study, serves as an indication of the broad potential of neural ne tworks in biomedical NMR.