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Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis

Authors
Journal
Spectrochimica Acta Part B Atomic Spectroscopy
0584-8547
Publisher
Elsevier
Publication Date
Volume
58
Issue
12
Identifiers
DOI: 10.1016/j.sab.2003.07.003
Keywords
  • Iron
  • Copper
  • Zinc
  • Selenium
  • Cancer
  • Artificial Neural Networks (Anns)
  • Total Reflection X-Ray Fluorescence Spectrometry (Txrf)
Disciplines
  • Chemistry
  • Computer Science

Abstract

Abstract Iron, copper, zinc and selenium were determined directly in serum samples from healthy individuals ( n=33) and cancer patients ( n=27) by total reflection X-ray fluorescence spectrometry using the Compton peak as internal standard [L.M. Marcó P. et al., Spectrochim. Acta Part B 54 (1999) 1469–1480]. The standardized concentrations of these elements were used as input data for two-layer artificial neural networks trained with the generalized delta rule in order to classify such individuals according to their health status. Various artificial neural networks, comprising a linear function in the input layer, a hyperbolic tangent function in the hidden layer and a sigmoid function in the output layer, were evaluated for such a purpose. Of the networks studied, the (4:4:1) gave the highest estimation (98%) and prediction rates (94%). The latter demonstrates the potential of the total reflection X-ray fluorescence spectrometry/artificial neural network approach in clinical chemistry.

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