ARSE: Adaptive Regression via Subspace Elimination, a novel algorithm for eliminating the contribution of uncalibrated interferents
Date
2017
Authors
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Journal ISSN
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Publisher
University of Delaware
Abstract
Chemometrics represents an extremely effective data analysis tool. Through its
judicious application, information that would otherwise be obscured within a data set
can be discovered. This dissertation investigates the development of novel
chemometric algorithms for accommodating the presence of uncalibrated spectral
components. It will also discuss building models for both the classification of edible
oils as well as predicting the peroxide value of those edible oils. ☐ Adaptive Regression via Subspace Elimination, ARSE, is demonstrated to be
able to effectively handle the presences of uncalibrated chemical components within
the prediction set. This is demonstrated first with a model system consisting of
Gaussian “model spectra”. It is then expand to two different artificial data sets based
upon actual pure component spectra. Across all the data sets a maximum of 4.2x
improvement in prediction compared to just PLS is observed. ☐ Also shown within this dissertation is the ability to build models to accurately
predict the type of edible oil as well as the peroxide value. The classification models
demonstrate an overall 92.75% accuracy. The error for predicting peroxide value
depends on both the type of spectroscopy used as well as the composition of the data
set. The prediction error varies from 3.60 to 8.72 on the same data set for Near IR and
Raman spectroscopy, respectively.