Multi-Instrument Calibration with Genetic Regression in UV-Visible Spectroscopy

Abstract

The applicability of genetic regression (GR) to multi-instrument calibration was demonstrated by using several UV-visible spectrophotometers. GR is a calibration technique that optimizes linear regression using a genetic algorithm (GA). Sample spectra of ternary and quaternary mixtures of the pharmaceuticals furaltadone (Fd), doxycycline (Dx), sulfadiazine (Sd), and trimethoprim (Tm) were collected on four different UV-visible spectrophotometers, including one single-beam diode array and three double-beam dispersive instruments. Hybrid calibration models (HCMs) were generated by combining the data collected on multiple instruments into one calibration model as if they had all been collected on a single instrument. For comparison, single-instrument calibration models were also generated for each instrument. Both HCMs and single-instrument models were tested by using a validation set measured on all four instruments. Results obtained from single-instrument models were comparable with a previous study in which partial least-squares (PLS) regression was used for multivariate calibration of these compounds. HCMs for double-instrument cases performed equally well as single-instrument models and slightly worse for the four-instruments models.