Abstract

A new multivariate calibration technique called net analyte preprocessing is introduced for the study of mixtures by near infrared spectroscopy. It involves the construction of a space spanned by all sources of variability except one component (or reference property). Both the data matrix and the unknown sample spectrum are preprocessed through a projection orthogonal to the latter space, yielding a set of net analyte signals appropriate for quantifying a particular mixture component. The construction of the net analyte space is carried out using a certain number of spectral factors, which can be estimated by cross-validation. Advantages of the presently discussed method include the estimation of figures of merit (sensitivity, selectivity and limit of determination) and the presentation of data in a useful pseudounivariate mode. An example involving the successful analysis of mixtures of the two rubber antioxidants tris-(nonylphenyl)-phosphite and 4,6-bis-(octylthiomethyl)-o-cresol is described, and compared with classical techniques such as principal component regression and partial least-squares.

© 2001 NIR Publications

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