19 May 2016 Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study
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Abstract
Interference and in particular spectral interference is a well documented problem in optical emission spectrometry. For example, it is commonly encountered even when commercially-available spectrometers with medium to high resolution are used (e.g., those with focal lengths of 0.75 m to 1 m). Such interference must be corrected. Although portable spectrometers are better suited for "taking part of the lab to the sample" types of applications, the effects of interference become more pronounced due to the short focal length of such spectrometers (e.g., 10 cm to 15 cm). We describe use of Artificial Neural Networks (ANNs) and of Partial Least Squares (PLS) methods for spectral interference correction.
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Z. Li, Z. Li, Vassili Karanassios, Vassili Karanassios, "Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710M (19 May 2016); doi: 10.1117/12.2224402; https://doi.org/10.1117/12.2224402
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