Geographical classification of Spanish bottled mineral waters by means of iterative models based on linear discriminant analysis and artificial neural networks

  • Francisco Gutiérrez-Reguera
  • J. Marcos Jurado
  • Rocío Montoya-Mayor
  • Miguel Ternero-Rodríguez
Original Article

Abstract

The composition of Spanish natural mineral waters has been determined by means of inductively coupled plasma-mass spectrometry, inductively coupled plasma-atomic emission spectrometry, ionic chromatography and other routine techniques. Methods were applied to samples of bottled water from springs situated in five different mountain systems such as Cordillera Costero-Catalana, Macizo Galaico, Sistemas Béticos, Sistema Central and Sistema Ibérico. Pattern recognition techniques have been applied to differentiate the origin of samples. Data were initially studied by using nonparametric multiple comparison techniques and principal component analysis to highlight data trends. Classification models based on linear discriminant analysis and multilayer perceptron artificial neural networks have been built and validated by means of a stratified jackknifing methodology. An iterative approach has been used to build an artificial neural network model based on the variables selected by linear discriminant analysis. The prediction ability of the constructed model was 94 %.

Keywords

Pattern recognition Multivariate analysis Multielemental analysis Geographical characterization Natural mineral water 

Supplementary material

521_2016_2459_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (DOCX 48 kb)

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Francisco Gutiérrez-Reguera
    • 1
  • J. Marcos Jurado
    • 1
  • Rocío Montoya-Mayor
    • 1
  • Miguel Ternero-Rodríguez
    • 1
  1. 1.Department of Analytical Chemistry, Faculty of ChemistryUniversity of SevillaSevilleSpain

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