Aspects of Multivariate Calibration Applied to Near-Infrared Spectroscopy

Further Analysis of Corn Flour Spectra Optical Regression 10.6.1 Theory. 

Chemometrics is often defined as the application of statistics and mathematics to the analysis of chemical data. Without arguing the sufficiency of this definition, it is safe to say that the application of multivariate statistical and mathematical spectral analysis methods to near-infrared (NIR) data provides an intriguing set of advantages absent in univariate analysis of NIR data. Foremost of these advantages are the abilities to preprocess NIR spectra for removal of complex background signals, perform multianalyte calibration and calibration in the presence of multiple changing chemical 

This chapter consists of two distinct parts. In the first part, a cursory overview of chemometric methods, as applicable to analysis of and quantitation with NIR data is presented. In Section 10.2 common methods for preprocessing NIR spectra are described. Section 10.3 discusses multivariate methods for developing predictive calibration models with NIR spectra. In Section 10.4, strategies for sample and model validation are presented that exploit the multivariate nature of NIR spectra. The performance of the multivariate methods discussed in Section 10.2 through Section 10.4 are applied to a set of 80 NIR reflectance data of corn flour for determination of four physical properties moisture, oil, protein, and starch. 

The second part of this chapter presents a novel method for improving the precision of multivariate calibration with NIR spectra from scanning and filter-wheel spectrometers. Optical regression (OR) employs the regression model to determine the optimal operational parameters for the scanning monochrometer or filter-wheel that maximizes the analytically useful signal-to-noise ratio. The theory of OR is presented is Section 10.6 and the method is applied to mixtures of dense nonaqueous phase liquids. 

Two of the most commonly employed methods of preprocessing multivariate data are mean centering and variance scaling of the spectra. Taken together, the application of mean centering and variance scaling is autoscaling. 

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Boysworth MK, Booksh KS. Aspects of multivariate calibration applied to near-infrared spectroscopy. In Bums DA, Ciurczak EW, editors. Handbook of Near-Infrared Analysis. 2nd ed. New York Marcel Dekker 2001. 

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