A new procedure for calibrating multiple instruments is presented in which spectra from each are used simultaneously during the construction of multivariate calibration models. The application of partial least-squares (PLS) and genetic regression (GR) to the problem of generating these hybrid calibrations is presented. Spectra of ternary mixtures of methylene chloride, ethyl acetate, and methanol were collected on a dispersive and a Fourier transform spectrometer. Calibration models were generated by using differing numbers of spectra from each instrument simultaneously in the calibration and prediction sets, and then validated by using a set of spectra from each instrument separately. Calibration models were found that perform well on both instruments, even when only a single spectrum from the second instrument was used during the calibration process. As a benchmark, comparison with PLS showed that GR is more effective than PLS in building these hybrid calibration models.
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