Analysis of amoxicillin in human urine by photo-activated generation of fluorescence excitation–emission matrices and artificial neural networks combined with residual bilinearization

Abstract

Fluorescence excitation–emission data recorded for amoxicillin after photo-activated reaction with periodate have been processed by a novel second-order multivariate method based on the combination of artificial neural networks and residual bilinearization (ANN/RBL), since the signals bear a strong non-linear relation with the analyte concentration. The selected chemometric methodology is employed for the first time to evaluate experimental non-linear second-order spectral information. Due to severe overlapping between the emission profiles for the analyte reaction product and for the urine background, calibration was done using different spiked urine samples. This allowed for the determination of amoxicillin in test spiked urines, other than those employed for calibration. When new urine samples containing a fluorescent anti-inflammatory were analyzed, accurate prediction in the presence of unexpected components required the achievement of the second-order advantage, which is provided by the post-training RBL procedure. Amoxicillin was also determined by ANN/RBL in a series of real urine samples, which allowed one to perform a comparison study with the reference high-performance liquid chromatographic technique.