Dual-domain regression analysis for spectral calibration models

Autor: Huwei Tan, Steven D. Brown
Rok vydání: 2003
Předmět:
Zdroj: Journal of Chemometrics. 17:111-122
ISSN: 1099-128X
0886-9383
DOI: 10.1002/cem.768
Popis: Taking advantage of the local nature of spectral data in both the time and frequency domains, two novel chemometric algorithms using the wavelet transform, namely dual-domain partial least squares (DDPLS) and dual-domain principal component regression (DDPCR), are reported here. The proposed algorithms establish parallel, regular models to describe spectral variation in the time (wavelength) domain. They incorporate these parallel models as a way of emphasizing local features in the frequency domain. Compared with regular PLS or PCR regression models applied to a single domain, these algorithms generate more parsimonious regression models that are also more robust against unexpected variations in the prediction set. Simulation data have been used in this paper to demonstrate this improvement. The new methods have also been successfully applied to NIR spectral data sets to predict moisture, oil, protein and starch content in Cargill corn samples, as well as a set of properties in a series of Amoco hydrocarbon samples. Through their special emphasis on the local nature of spectral signals in the frequency domain, spectral variance can be separately explained over the frequency and time domains with fewer latent variables and with better predictive performance. Copyright © 2003 John Wiley & Sons, Ltd.
Databáze: OpenAIRE
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