Improving complex near-IR calibrations using a new wavelength selection algorithm
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abstract
Near-infrared spectroscopy is being considered as a tool for the noninvasive determination of important cell culture media constituents, which would allow frequent, harmless sampling and computer interfacing for closed-loop control. Partial least-squares calibration models for glucose and lactate are constructed for cell culture media and aqueous media comprised of several absorbing species. Wavelength selection, having failed in previous attempts with these data, is shown to reduce the error prediction and number of required wavelengths when performed with the use of a newly developed peak-hopping algorithm. The selection method reduces prediction errors in every case considered here and is extendable to combined calibration models that are built for use with a particular type of sample with the aid of high-quality spectra from simpler mixtures. The new selection algorithm leads to calibrations producing accurate predictions with fewer wavelengths, in support of previous results obtained when applied to single-component Raman spectroscopy data. The findings continue to suggest that the algorithm can be used as a simple alternative to the difficult-to-configure genetic algorithm.
