Optical regression: a method for improving quantitative precision of multivariate prediction with single channel spectrometers
'Optical regression' (OR) is presented as a method for improving the quantitative precision of scanning and filter wheel process analyzers. OR combines analog variable selection and optimization of signal to noise measurements under constrained total measurement time to maximize the precision of prediction in multivariate analysis. With optical regression, the regression vector is employed as a template to optimize the data collection lime at each wavelength of the unknown spectra. Implicitly, this performs the dot product of the spectrum and regression vector by electronically integrating the signal of the detector instead of performing the mathematical operations in the computer following digitization of the spectrum. The theory of optical regression is developed and the expected precision of optical regression is shown to be superior to the expected precision of digital regression. This conclusion is supported by Monte Carlo simulations with three types of random errors. Further support is supplied by quantitation of three fluorescent dyes with a fiber optic fluorescence spectrometer, (C) 1999 Elsevier Science B.V. All rights reserved.
variable selection data collection multivariate calibration variable weighting prediction errors