Skip to main content
SpringerLink
Log in

Application of Artificial Neural Network and Multiple Linear Regression Retention Models for Optimization of Separation in Ion Chromatography by Using Several Criteria Functions

  • Published:
Chromatographia Aims and scope Submit manuscript

Abstract

This work focuses on problems regarding empirical retention modelling and optimization of separation in ion chromatography. Influences of eluent flow rate and concentration of eluent competing ion (OH−) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) were investigated. Artificial neural networks and multiple linear regression retention models in combination with several criteria functions were used and compared in global optimization process. It can be seen that general recommendations for optimization of separation in ion chromatography is application of chromatography exponential function criterion in combination with artificial neural networks retention model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Schoenmakers PJ (1986) Optimisation of Chromatographic Selectivity, Elsevier, Amsterdam

  • Berridge JC (1988) Techniques for the Automated Optimisation of HPLC Separations, Wiley, New York

  • Snyder LR (1997) Practical HPLC Method Development, 2nd ed., Wiley, New York

  • Snyder LR, Dolan JW, Lommen DC (1989) J Chromatogr 485:65–89

    Google Scholar 

  • Heinisch S, Lesellier E, Podevin C, Rocca JL, Tchapla A (1997) Chromatographia 44:529–537

    Google Scholar 

  • Torres Lapasió JR, García Alvarez-Coque MC, Baeza Baeza JJ (1997) Anal Chim Acta 348:187–196

    Google Scholar 

  • Madden JE, Haddad PR (1998) J Chromatogr A 829:65–80

    Google Scholar 

  • Madden JE, Haddad PR (1999) J Chromatogr A 850:29–41

    Google Scholar 

  • Gjerde DT, Schmuckler G, Fritz JS (1980) J Chromatogr 187:35–45

    Article  CAS  Google Scholar 

  • Haddad PR, Cowie CE (1984) J Chromatogr 303:321–330

    Google Scholar 

  • Jenke DR, Pagenkopf GK (1984) Anal Chem 56:85–88

    Google Scholar 

  • Jenke DR, Pagenkopf GK (1984) Anal Chem 56:88–91

    Google Scholar 

  • Jenke DR (1984) Anal Chem 56:2674–2681

    Google Scholar 

  • Maruo M, Hirayama N, Kuwamoto T (1989) J Chromatogr 481:315–322

    Google Scholar 

  • Jenke DR (1994) Anal Chem 66:4466–4470

    Google Scholar 

  • Hajos P, Horvath O, Denke V (1995) Anal Chem 67:434–441

    Google Scholar 

  • Noviè Ma, Zupan J, Noviè Mi (2001) J Chromatogr A 922:1–11

    Google Scholar 

  • Sacchero G, Buzzoniti MC, Sarzanini C, Mentasti E, Metting HJ, Coenegracht P MJ (1998) J Chromatogr A 799:35–45

    Google Scholar 

  • Madden JE, Avdalovic N, Jackson PE, Haddad PR (1999) J Chromatogr A 837:65–74

    Google Scholar 

  • Havel J, Madden JE, Haddad PR (1999) Chromatographia 49:481–488

    Google Scholar 

  • Madden JE, Avdalovic N, Haddad PR, Havel J (2001) J Chromatogr A 910:173–179

    Google Scholar 

  • Srecnik G, Debeljak Z, Cerjan-Stefanović Š, Bolanèa T, Nović M, Lazarić K, Gumhalter – Luliæ Z (2002) Croat Chem Acta 75:713–725

    Google Scholar 

  • Sreènik G, Debeljak Z, Cerjan – Stefanoviæ Š, Noviè M, Bolanća T (2002) J Chromatogr 973:47–59

    Google Scholar 

  • Strasters JK, Breyer ED, Rodgers AH, Khaledi MG (1990) J Chromatogr 511:335–343

    Google Scholar 

  • Lema M, Otero J, Marcó J (1991) J Chromatogr 547:113–120

    Google Scholar 

  • Sekulic S, Haddad PR (1989) J Chromatogr 485:501–515

    Google Scholar 

  • Heinisch S, Rocca JL (1995) Chromatographia 41:544–552

    Google Scholar 

  • Carda-Broch S, Torres-Lapasió J R, García-Alvarez-Coque MC (1999) Anal Chim Acta 396:61–74

    Google Scholar 

  • López-Grío SJ, Vivó-Truyols G, Torres-Lapasió JR, García-Alvarez-Coque MC (2001) Anal Chim Acta 433:187–198

    Article  CAS  Google Scholar 

  • Li J (1997) Anal Chem 69:4452–4462

    Google Scholar 

  • Di Marco VB, Bombi GG (2001) J Chromatogr A 931:1–30

    Google Scholar 

  • Nikitas P, Pappa-Louisi A, Papageorgiou A (2001) J Chromatogr A 912:13–29

    Google Scholar 

  • Pápai Zs, Pap TL (2002) Analyst 127:494–498

    Google Scholar 

  • Li J (2002) J Chromatogr A 952:63–70

    Google Scholar 

  • Torres-Lapasió JR, Villanueva-Camañas RM, Sanchis-Mallols JM, Medina- Hernández MJ, García-Alvarez-Coque MC (1994) J Chromatogr A 677:239–253

    Google Scholar 

  • Torres-Lapasió JR, Baeza-Baeza JJ, García-Alvarez-Coque MC (1997) Anal Chem 69:3822–3831

    Google Scholar 

  • Caballero RD, García-Alvarez-Coque MC, Baeza-Baeza JJ (2002) J Chromatogr A 954:59–76

    Google Scholar 

Download references

Acknowledgments.

The authors would like to thank for the assistance and comments of Goran Srečnik (Pliva, Pharmaceutical Industry, Zagreb, Croatia) and Željko Debeljak (Clinical Hospital Osijek, Osjek, Croatia).

Author information

Authors and Affiliations

  1. Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićv trg 20, 10000, Zagreb, Croatia

    T. Bolanča & Š. Cerjan-Stefanović

  2. National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia

    M. Novič

Authors
  1. T. Bolanča
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Š. Cerjan-Stefanović
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Novič
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Bolanča.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bolanča, T., Cerjan-Stefanović, Š. & Novič, M. Application of Artificial Neural Network and Multiple Linear Regression Retention Models for Optimization of Separation in Ion Chromatography by Using Several Criteria Functions. Chroma 61, 181–187 (2005). https://doi.org/10.1365/s10337-004-0487-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1365/s10337-004-0487-2

Keywords

Search

Navigation