How do artificial neural networks (ANNs) compare to partial least squares (PLS) for spectral interference correction in optical emission spectrometry?

Z. Li; X. Zhang; V. Karanassios

doi:10.1117/12.2177516

20 May 2015 How do artificial neural networks (ANNs) compare to partial least squares (PLS) for spectral interference correction in optical emission spectrometry?

Z. Li, X. Zhang, V. Karanassios

Author Affiliations +

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 94960M (2015) https://doi.org/10.1117/12.2177516
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Spectral interference from overlaps of spectral lines is a well-documented problem optical emission spectrometry. Spectral interference is encountered even when spectrometers with medium to high resolution are used (e.g., with a focal length of 0.75 m 1 m). The adverse effects of spectral interference are more pronounced when portable spectrometers with low resolution are used (e.g., with focal lengths of about 12.5 cm). Portable spectrometers are suited for “taking part of the lab to the sample” types of applications. We used Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) to address spectral interference correction. And our efforts using these methods are described.

Citation Download Citation

Z. Li, X. Zhang, and V. Karanassios "How do artificial neural networks (ANNs) compare to partial least squares (PLS) for spectral interference correction in optical emission spectrometry?", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960M (20 May 2015); https://doi.org/10.1117/12.2177516

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