Infrared analysis of urinary calculi by a single reflection accessory and a neural network interpretation algorithm.

BACKGROUND Preparation of KBr tablets, used for Fourier transform infrared (FT-IR) analysis of urinary calculus composition, is time-consuming and often hampered by pellet breakage. We developed a new FT-IR method for urinary calculus analysis. This method makes use of a Golden Gate Single Reflection Diamond Attenuated Total Reflection sample holder, a computer library, and an artificial neural network (ANN) for spectral interpretation. METHODS The library was prepared from 25 pure components and 236 binary and ternary mixtures of the 8 most commonly occurring components. The ANN was trained and validated with 248 similar mixtures and tested with 92 patient samples, respectively. RESULTS The optimum ANN model yielded root mean square errors of 1.5% and 2.3% for the training and validation sets, respectively. Fourteen simple expert rules were added to correct systematic network inaccuracies. Results of 92 consecutive patient samples were compared with those of a FT-IR method with KBr tablets, based on an initial computerized library search followed by visual inspection. The bias was significantly different from zero for brushite (-0.8%) and the concomitantly occurring whewellite (-2.8%) and weddellite (3.8%), but not for ammonium hydrogen urate (-0.1%), carbonate apatite (0.5%), cystine (0.0%), struvite (0.4%), and uric acid (-0.1%). The 95% level of agreement of all results was 9%. CONCLUSIONS The new Golden Gate method is superior because of its smaller sample size, user-friendliness, robustness, and speed. Expert knowledge for spectral interpretation is minimized by the combination of a library search and ANN prediction, but visual inspection remains necessary.