Neural Computing and Applications

, Volume 29, Issue 2, pp 459–468 | Cite as

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


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 %.


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)


  1. 1.
    European Union (2009) Directive 2009/54/EC of the European Parliament and of the Council of 18 June 2009 on the exploitation and marketing of natural mineral waters. Official Journal of the European Union, L 164/45, Brussels. Accessed 9 July 2016
  2. 2.
    Sipos L, Kovács Z, Sági-Kiss V, Csiki T, Kókai Z, Fekete A, Éberger K (2012) Discrimination of mineral waters by electronic tongue, sensory evaluation and chemical analysis. Food Chem 135:2947–2953. doi: 10.1016/j.foodchem.2012.06.021 CrossRefGoogle Scholar
  3. 3.
    Oyebog SA, Ako AA, Nkeng GE, Suh EC (2012) Hydrogeochemical characteristics of some Cameroon bottled waters, investigated by multivariate statistical analyses. J Geochem Explor 112:118–130. doi: 10.1016/j.gexplo.2011.08.003 CrossRefGoogle Scholar
  4. 4.
    Peh Z, Ŝorŝa A, Halamić J (2010) Composition and variation of major and trace elements in Croatian bottled waters. J Geochem Explor 107:227–237. doi: 10.1016/j.gexplo.2010.02.002 CrossRefGoogle Scholar
  5. 5.
    Fugedi U, Kuti L, Jordan G, Kerek B (2010) Investigation on the hydrogeochemistry of some bottled mineral waters in Hungary. J Geochem Explor 107:305–316. doi: 10.1016/j.gexplo.2010.10.011 CrossRefGoogle Scholar
  6. 6.
    Naddeo V, Zarra T, Belgiorno V (2008) A comparative approach to the variation of natural elements in Italian bottled waters according to the national and international standard limits. J Food Comp Anal 21:505–514. doi: 10.1016/j.jfca.2008.02.010 CrossRefGoogle Scholar
  7. 7.
    Birke M, Rauch U, Harazim B, Lorenz H, Glatte W (2010) Major and trace elements in German bottled water, their regional distribution and accordance with national and international standards. J Geochem Explor 107:245–271. doi: 10.1016/j.gexplo.2010.06.002 CrossRefGoogle Scholar
  8. 8.
    Güler C (2007) Characterization of Turkish bottled waters using pattern recognition methods. Chemom Intell Lab Syst 86:86–94. doi: 10.1016/j.chemolab.2006.08.009 CrossRefGoogle Scholar
  9. 9.
    Gutiérrez-Reguera F, Seijo-Delgado I, Montoya-Mayor R, Ternero-Rodríguez M (2012) Caracterización fisicoquímica (parámetros generales y componentes mayoritarios) de las aguas minerales naturales envasadas de España. Afinidad 519:165–174Google Scholar
  10. 10.
    Smedley PL (2010) A survey of the inorganic chemistry of bottled mineral waters from the British Isles. Appl Geochem 25:1872–1888. doi: 10.1016/j.apgeochem.2010.10.003 CrossRefGoogle Scholar
  11. 11.
    Souza AL, Lemos SG, Naozuka J, Miranda-Correia PR, Oliveira PV (2011) Exploring the emission intensities of ICPOES aided by chemometrics in the geographical discrimination of mineral waters. J Anal At Spectrom 26:852–860. doi: 10.1039/C0JA00071J CrossRefGoogle Scholar
  12. 12.
    Groŝelj N, van der Veer G, Tuŝar M, Vračko M, Novič M (2010) Verification of the geological origin of bottled mineral waters using artificial neural networks. Food Chem 118:941–947. doi: 10.1016/j.foodchem.2008.11.085 CrossRefGoogle Scholar
  13. 13.
    Thermo Electron Corporation (2004) X series ICP-MS getting started guide. Ref. no. S419MA. Thermo Electron Corporation, WinsfordGoogle Scholar
  14. 14.
    AOAC (2012) Appendix F: guidelines for standard method performance requirements. In: Official methods of analysis of AOAC international, 19th edn. AOAC International, GaithersburgGoogle Scholar
  15. 15.
    Cuadros L, García AM, Bosque JM (1996) Statistical estimation of linear calibration range. Anal Lett 29:1231–1239. doi: 10.1080/00032719608001471 CrossRefGoogle Scholar
  16. 16.
    ISO (1994) ISO 9963-1:1994 Water quality. Determination of alkalinity. Part 1: determination of total and composite alkalinity. International Organization for Standardization, GenevaGoogle Scholar
  17. 17.
    ISO (1985) ISO 7888:1985 Water quality. Determination of electrical conductivity. International Organization for Standardization, GenevaGoogle Scholar
  18. 18.
    Muth JE (1999) Basic statistic and pharmaceutical statistical applications, 1st edn. Chapman and Hall/CRC, New YorkGoogle Scholar
  19. 19.
    Jolliffe IT (2002) Principal components analysis, 2nd edn. Springer, New YorkMATHGoogle Scholar
  20. 20.
    Palacios-Morillo A, Alcázar A, Pablos F, Jurado JM (2013) Differentiation of tea varieties using UV–Vis spectra and pattern recognition techniques. Spectrochim Acta A 103:79–83. doi: 10.1016/j.saa.2012.10.052 CrossRefGoogle Scholar
  21. 21.
    Tsakovski S, Simeonov V (2009) Chemometrics as a tool for treatment processing of multiparametric analytical data sets. In: Namiesnik J, Szefer P (eds) Analytical measurements in aquatic environments. CRC Press, Boca Raton, pp 369–388CrossRefGoogle Scholar
  22. 22.
    Valle S, Li W, Qin SJ (1999) Selection of the number of principal components: the variance of reconstruction error criterion with comparison to other methods. Ind Eng Chem Res 38:4389–4401. doi: 10.1021/ie990110i CrossRefGoogle Scholar
  23. 23.
    Massart DL (1998) Handbook of chemometrics and qualimetrics, part B. Elsevier, AmsterdamGoogle Scholar
  24. 24.
    Forina M, Armanino C, Leardi R, Drava G (1991) A class modelling technique based on potential functions. J Chemom 5:435–453. doi: 10.1002/cem.1180050504 CrossRefGoogle Scholar
  25. 25.
    Kott PS (2001) The delete-a-group jackknife. J Off Stat 17:521–526Google Scholar
  26. 26.
    Tetko IV, Livingstone DJ, Luik AI (1995) Neural network studies. 1. Comparison of overfitting and overtraining. J Chem Inform Comput Sci 35:826–833. doi: 10.1021/ci00027a006 CrossRefGoogle Scholar
  27. 27.
    Martin AE, Watling RJ, Lee GS (2012) The multi-element determination and regional discrimination of Australian wines. Food Chem 133:1081–1089. doi: 10.1016/j.foodchem.2012.02.013 CrossRefGoogle Scholar

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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