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

Z. Li; Vassili Karanassios

doi:10.1117/12.2224402

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

Z. Li, Vassili Karanassios

Author Affiliations +

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 98710M (2016) https://doi.org/10.1117/12.2224402
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Z. Li and 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); https://doi.org/10.1117/12.2224402

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