Verification of the performance of Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction in optical emission spectrometry

Z. Li; X. Zhang; V. Karanassios

doi:10.1117/12.2050326

22 May 2014 Verification of the performance of Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction in optical emission spectrometry

Z. Li, X. Zhang, V. Karanassios

Author Affiliations +

Proceedings Volume 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII; 911812 (2014) https://doi.org/10.1117/12.2050326
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Interference from overlaps of spectral lines is a key concern in optical emission spectrometry even when a spectrometer with relatively high resolution and a long focal length (e.g., 1 m) is used. The problem becomes more complex when a portable spectrometer (e.g., with a focal length of 12.5 cm) with low resolution is used. Such a spectrometer is better 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 are described here in some detail.

Citation Download Citation

Z. Li, X. Zhang, and V. Karanassios "Verification of the performance of Artificial Neural Networks (ANNs) versus Partial Least Squares (PLS) for spectral interference correction in optical emission spectrometry", Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 911812 (22 May 2014); https://doi.org/10.1117/12.2050326

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