Document Type

Article

Subject Area(s)

Chemistry, Physics, Optics

Abstract

Multivariate optical computing (MOC) is a method of performing chemical analysis using a multilayer thin-film structure known as a multivariate optical element (MOE). Recently we have been advancing MOC for imaging problems by using an imaging MOE (IMOE) in a normal-incidence geometry and employing normalization by the 1-norm. There are several important differences between the previously described 45° and the normal-incidence imaging, one of which is the measurement precision due to photon counting. We compare this precision to 45° MOC. We also discuss how MOE models with similar values of standard errors of calibration and prediction and similar gain values may vary in precision because of the sign or offset of the regression vector encoded in the IMOE spectrum. Experimental verification of a key result is provided by near-infrared imaging of slides coated with a dye-doped polymer film.

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