Robust absorbance computations in the analysis of glucose by near-infrared spectroscopy

Procedures for data acquisition and data processing are evaluated for the optimal computation of absorbance values based on Fourier transform near-infrared transmission spectra. Samples consisting of physiological levels (1–20 mM) of glucose in an aqueous matrix of variable levels of bovine serum albumin and triacetin are studied in the combination spectral region (5000–4000 cm⁻¹). The weak glucose signals in this region define a challenging analysis that is extremely sensitive to the effects of instrumental drift. The impact of different procedures for obtaining absorbance estimates is evaluated in the context of multivariate calibration models based on partial least-squares (PLS) regression. Replicate calibration and prediction data acquired over 6 months are used to study the robustness of PLS models with respect to time. The recommended protocol for the absorbance calculations is based on the collection of a large group of individual background spectra during the instrumental warm-up period. Seven procedures are tested for obtaining optimal backgrounds for use with either the calibration or prediction data sets. When the developed methodology is employed, standard errors of prediction are maintained in the range of 1.0 mM for spectra acquired up to 6 months after the collection of the calibration data. This level of performance compares favorably to daily internal cross-validation errors of 0.5–0.9 mM.