Artificial neural networks (ANNs) compared to partial least squares (PLS) for spectral interference correction in optical emission spectrometry

Z. Li; X. Zhang; Vassili Karanassios

doi:10.1117/12.2016253

29 May 2013 Artificial neural networks (ANNs) compared to partial least squares (PLS) for spectral interference correction in optical emission spectrometry

Z. Li, X. Zhang, Vassili Karanassios

Author Affiliations +

Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 87500C (2013) https://doi.org/10.1117/12.2016253
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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).

Citation Download Citation

Z. Li, X. Zhang, and 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); https://doi.org/10.1117/12.2016253

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