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Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Combining prior knowledge with input selection algorithms for quantitative analysis using neural networks in laser

Danny Luarte1, Ashwin Kumar Myakalwar2, Marizú Velásquez2

  • 1Department of Electrical Engineering, Universidad de Concepcion, Concepcion, Chile. dsbarbar@udec.cl.

Analytical Methods : Advancing Methods and Applications
|February 18, 2021
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Summary

This study introduces a method using Akaike information criterion (AIC) to select optimal wavelengths for Laser-Induced Breakdown Spectroscopy (LIBS) and Artificial Neural Networks (ANNs) for mineral analysis. The approach effectively reduces data complexity for accurate concentration estimation.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful technique for elemental analysis of solid samples.
  • High-dimensional LIBS spectra require feature selection for efficient analysis with Artificial Neural Networks (ANNs).
  • Optimizing ANN model complexity alongside spectral data is crucial for accurate quantitative analysis.

Purpose of the Study:

  • To develop a systematic methodology for selecting informative wavelengths from LIBS spectra.
  • To determine optimal Artificial Neural Network (ANN) model complexity for quantitative analysis.
  • To compare the effectiveness of various variable selection algorithms within the proposed framework.

Main Methods:

  • A methodology combining prior knowledge with variable selection algorithms (KBest, LASSO, PCA, CARS) guided by Akaike Information Criterion (AIC).
  • Application to quantitative analysis of copper, iron, and arsenic concentrations in mineral samples using LIBS data.
  • Regression analysis and ANN training on a reduced set of selected wavelengths.

Main Results:

  • The proposed methodology effectively selects informative wavelengths and optimizes ANN model complexity.
  • LASSO and CARS algorithms, combined with prior knowledge, demonstrated high efficacy.
  • Accurate estimation of copper, iron, and arsenic concentrations was achieved using the selected spectral features.

Conclusions:

  • The systematic methodology is highly effective for wavelength selection and model complexity optimization in LIBS-ANN quantitative analysis.
  • Variable selection algorithms like LASSO and CARS are valuable tools for LIBS spectral data reduction.
  • This approach enhances the accuracy and efficiency of mineral concentration analysis using LIBS and ANNs.