Abstract

A spectrum simulation method is described for use in the development and transfer of multivariate calibration models from near-infrared spectra. By use of previously measured molar absorptivities and solvent displacement factors, synthetic calibration spectra are computed using only background spectra collected with the spectrometer for which a calibration model is desired. The resulting synthetic calibration set is used with partial least squares regression to form the calibration model. This methodology is demonstrated for use in the analysis of physiological levels of glucose (1–30 mM) in an aqueous matrix containing variable levels of alanine, ascorbate, lactate, urea, and triacetin. Experimentally measured data from two different Fourier transform spectrometers with different noise levels and stabilities are used to evaluate the simulation method. With the more stable instrument (A), well-performing calibration models are obtained, producing a standard error of prediction (SEP) of 0.70 mM. With the less stable instrument (B), the calibration based solely on synthetic spectra is less successful, producing an SEP value of 1.58 mM. For cases in which the synthetic spectra do not describe enough spectral variance, an augmentation protocol is evaluated in which the synthetic calibration spectra are augmented with the spectra of a small number of experimentally measured calibration samples. For instruments A and B, respectively, augmentation with measured spectra of nine samples lowers the SEP values to 0.64 and 0.85 mM.

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