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

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.

• Tipo de recurso: Book
• Fecha de publicación: 2017
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16802
• Acceso en línea: https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/artificial-neural-networks-anns-for-spectral-interference-correction-using-a-large-size-spectrometer; http://hdl.handle.net/20.500.12010/16802
• Palabra clave: Biología; Redes neuronales artificiales; Inteligencia artificial -- Robótica; Espectrometría de emisión óptica portátil
• License: Abierto (Texto Completo)