Development of artificial neural networks for spectral interference correction in optical emission spectrometry

Li, Z.; Huang, Shimeng; Karanassios, Vassili

doi:10.1117/12.884341

Cited by 2 publications

(6 citation statements)

References 14 publications

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“…To avoid bias and instrumental errors and to eliminate the adverse effects 1/f noise, both PLS and ANNs that we developed were initially tested primarily using simulated spectral scans [21][22][23]. Some laboratory prepared mixtures were used along with laboratory prepared "unknown" mixtures (to examine the predictive ability of these methods) in a practical setting [24].…”

Section: Methodsmentioning

confidence: 99%

“…Chemometrics can be broadly defined as "the application of mathematical or statistical methods for the treatment of chemical data" [21][22][23][24]. Partial Least Squares (PLS) is one of the chemometric method.…”

Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

“…Their computing nodes (called neurons) are interconnected, they are weighted and they are typically formed in multiple layers. There are two steps in using ANNs: a training step (or learning step) is first performed [21][22][23][24]. For our application, standard solutions of known concentrations or with known A:I ratios were used, thus the A:I ratio was varied.…”

Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

“…[1][2][3][4]. In this continuation of previous work [21][22][23][24] (in some of the early ones simulated spectral scans were used), Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) will be further explored as methods of spectral interference correction.…”

Section: Introductionmentioning

confidence: 99%

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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

Karanassios

2016

SPIE Proceedings

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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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Section: Methodsmentioning

confidence: 99%

Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Karanassios

2016

SPIE Proceedings

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“…Since then, ANNs have been applied to many chemistry-related areas, limited examples include of IR-and UV-spectra, classification, calibration, nuclear magnetic resonance (NMR), and ion mobility spectrometry (IMS). In my lab, we have been applying ANNs for spectral interference correction in analytical atomic spectrometry [22][23][24][25][26][27].…”

Section: Brief Background On Annsmentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Li¹,

Zhang²,

Mohua³

et al. 2018

Advanced Applications for Artificial Neural Networks

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Artificial neural networks (ANNs) are evaluated for spectral interference correction using simulated and experimentally obtained spectral scans. Using the same data set (where possible), the predictive ability of shallow depth ANNs was validated against partial least squares (PLS, a traditional chemometrics method). Spectral interference (in the form of overlaps between spectral lines) is a key problem in large-size, long focal length inductively coupled plasma-optical emission spectrometry (ICP-OES). Unless corrected, spectral interference can be sufficiently severe to the point of preventing precise and accurate analytical determinations. In miniaturized, microplasma-based optical emission spectrometry with a portable, short focal length spectrometer (having poorer resolution than its large-size counterpart), spectral interference becomes even more severe. To correct it, we are evaluating use of deep learning ANNs. Details are provided in this chapter.

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Development of artificial neural networks for spectral interference correction in optical emission spectrometry

Cited by 2 publications

References 14 publications

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

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

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

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