[Curve fitting based on genetic algorithms for quantitative resolution in overlapped fluorescence spectra].

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Context:

• Quantitative analysis of fluorescent substances often faces challenges due to spectral interference.
• Endogenous fluorophores in biological samples like urine can complicate the detection of specific analytes.
• Accurate determination of drug concentrations is crucial for pharmacokinetic studies and therapeutic drug monitoring.

Purpose:

• To develop and validate a sensitive fitting fluorescence spectrometric method for determining gatifloxacin (GFLX) in human urine.
• To utilize an exponentially modified Gaussian (EMG) model-based genetic algorithm as a fitness function for spectral fitting.
• To address and overcome the interference from endogenous fluorophores in urine during GFLX quantification.

Summary:

• A novel method employs an exponentially modified Gaussian (EMG) model-based genetic algorithm for fitting fluorescence spectrograms, effectively resolving interference in multi-component quantitative analysis.
• The method was applied to determine gatifloxacin (GFLX) in human urine, successfully mitigating interference from endogenous fluorophores.
• Optimized conditions yielded a linear relationship (R=0.9994) for GFLX concentrations from 0.06-3.5 µg/mL, with a detection limit of 0.02 µg/mL and recovery of 99.2%-109.4%.

Impact:

• The proposed fitting fluorescence spectrometric method provides a rapid, simple, and highly sensitive approach for GFLX determination in urine without preseparation.
• The method demonstrates excellent linearity, selectivity, precision, and accuracy, making it suitable for routine assays.
• This technique is valuable for pharmacokinetic studies, enabling efficient drug monitoring in biological samples.