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Proceedings Paper

A partial least squares model for non-volatile residue quantification using diffuse reflectance infrared reflectance spectroscopy
Author(s): Amylynn Chen; Robert M. Moision
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Paper Abstract

Traditionally, quantification of non-volatile residue (NVR) on surfaces relevant to space systems has been performed using solvent wipes for NVR removal followed by gravimetric analysis. In this approach the detectable levels of NVR are ultimately determined by the mass sensitivity of the analytical balance employed. Unfortunately, for routine samples, gravimetric measurement requires large sampling areas, on the order of a square foot, in order to clearly distinguish sample and background levels. Diffuse Reflectance Infrared Reflectance Spectroscopy (DRIFTS) is one possible alternative to gravimetric analysis for NVR measurement. DRIFTS is an analytical technique used for the identification and quantification of organic compounds that has two primary advantages relative to gravimetric based methods: increased sensitivity and the ability to identify classes of organic species present. However, the use of DRIFTS is not without drawbacks, most notably repeatability of sample preparation and the additive quantification uncertainty arising from overlapping infrared signatures. This can result in traditional calibration methods greatly overestimating the concentration of species in mixtures. In this work, a partial least squares (PLS) regression model is shown to be an effective method for removing the over prediction error of a three component mixture of common contaminant species.

Paper Details

Date Published: 27 September 2016
PDF: 12 pages
Proc. SPIE 9952, Systems Contamination: Prediction, Control, and Performance 2016, 99520A (27 September 2016); doi: 10.1117/12.2241112
Show Author Affiliations
Amylynn Chen, The Aerospace Corp. (United States)
Univ. of California, Los Angeles (United States)
Robert M. Moision, The Aerospace Corp. (United States)


Published in SPIE Proceedings Vol. 9952:
Systems Contamination: Prediction, Control, and Performance 2016
Joanne Egges; Carlos E. Soares; Eve M. Wooldridge, Editor(s)

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