Simultaneous determination of composition of human urinary calculi by use of artificial neural networks

A new chemometric method, which uses artificial neural networks (ANN), is p resented for determination of the composition of urinary calculi. The selec ted constituents were whewellite, weddellite, and uric acid from which appr oximately 40 % of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obt ained by partial least squares (PLS) regression or by the principal compone nt regression (PCR), because neural networks have better prediction capacit y. The generalization abilities of the optimized neural networks were check ed using the standard addition method on carefully selected real natural sa mples.