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New Dissimilarity Measures for Ultraviolet Spectra Identification

  • Andrés Eduardo Gutiérrez-Rodríguez
  • Miguel Angel Medina-Pérez
  • José Fco. Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
  • Milton García-Borroto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Ultraviolet Spectra (UVS) analysis is a frequent tool in tasks like diseases diagnosis, drugs detection and hyperspectral remote sensing. A key point in these applications is the UVS comparison function. Although there are several UVS comparisons functions, creating good dissimilarity functions is still a challenge because there are different substances with very similar spectra and the same substance may produce different spectra. In this paper, we introduce a new spectral dissimilarity measure for substances identification, based on the way experts visually match the spectra shapes. We also combine the new measure with the Spectral Correlation Measure. A set of experiments conducted with a database of real substances reveals superior results of the combined dissimilarity, with respect to state-of-the-art measures. We use Receiver Operating Characteristic curve analysis to show that our proposal get the best tradeoff between false positive rates and true positive rates.

Keywords

Ultraviolet Spectra Ultraviolet Spectra Comparisons Functions Substance Identification Dissimilarity Measures 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrés Eduardo Gutiérrez-Rodríguez
    • 1
  • Miguel Angel Medina-Pérez
    • 1
  • José Fco. Martínez-Trinidad
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 2
  • Milton García-Borroto
    • 1
    • 2
  1. 1.Centro de BioplantasCiego de ÁvilaCuba
  2. 2.Instituto Nacional de AstrofísicaÓptica y ElectrónicaSta. María TonanzintlaMéxico

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