29 May 2013 Artificial neural networks (ANNs) compared to partial least squares (PLS) for spectral interference correction in optical emission spectrometry
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Abstract
Spectral interference arising from direct, wing or background-induced spectral overlaps are a key concern in optical emission spectrometry even if an optical spectrometer with a 1 m focal length is used (thus resulting in peaks with halfwidth of ~80 pm). The problem of spectral interferences becomes even more acute when a portable spectrometer with a relatively short focal length (e.g., 10-15 cm) is used. In our lab, we are addressing spectral interference correction methods using artificial neural networks (ANNs) and partial least squares (PLS). In this paper, the application of ANNS and of PLS for spectral interference correction is compared using spectral simulations (to avoid the effects of 1/f noise).
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Z. Li, Z. Li, X. Zhang, X. Zhang, Vassili Karanassios, Vassili Karanassios, "Artificial neural networks (ANNs) compared to partial least squares (PLS) for spectral interference correction in optical emission spectrometry", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500C (29 May 2013); doi: 10.1117/12.2016253; https://doi.org/10.1117/12.2016253
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