Skip to main content
Log in

Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks

  • Original Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detection. Determination of both analytes was performed on the basis of differences between the acid–base and spectral features of each drug species. Non-linearities were detected by using both qualitative and quantitative tools. As a consequence of the non-linearity, a recently reported algorithm, artificial neural networks followed by residual bilinearization (ANN/RBL), was shown to furnish more satisfactory results. Recoveries of 99.7% (LOR) and 95.6% (PES) were obtained when analyzing a validation set containing unexpected components (the usual excipients found in pharmaceutical preparations). The average value obtained by implementation of the method on four replicates was compared with that obtained by a reference method based on HPLC (difference not significant; p > 0.05).

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

U-PLS/RBL:

Unfolded partial-least-squares/residual bilinearization

ANN/RBL:

Artificial neural networks followed by residual bilinearization

HPLC:

High-performance liquid chromatography

PARAFAC:

Parallel-factor analysis

GRAM:

Generalized rank-annihilation method

MCR–ALS:

Multivariate curve resolution–alternating least squares

FIA:

Flow injection analysis

DAD:

Diode-array detector

SVD:

Singular-value decomposition

References

  1. Brunton L (2005) In: Lazo J, Parker K (eds) Goodman and Gilman’s The pharmacological basis of therapeutics. McGraw–Hill Professional, New York

    Google Scholar 

  2. Mabrouk MM, El-Fatatry HM, Hammad S, Aziz A, Wahbi AAM (2003) J Pharm Biomed Anal 33:597–604

    Article  CAS  Google Scholar 

  3. Onur F, Yucesoy C, Dermis S, Kartal M, Kokdil G (2000) Talanta 51:269–279

    Article  CAS  Google Scholar 

  4. Sane RT, Francis M, Khedkar S, Pawar S, Moghe A (2001) Indian Drugs 38:436–438

    CAS  Google Scholar 

  5. Vasudevan M, Ravisankar S, Sathiyanarayanan A, Chandan RS (2001) Indian Drugs 38:276–278

    CAS  Google Scholar 

  6. Culzoni MJ, Damiani PC, García-Reiriz A, Goicoechea HC, Olivieri AC (2007) Analyst 132:654–663

    Article  CAS  Google Scholar 

  7. Naidong W (2003) J Chromatogr B 796:209–224

    Article  CAS  Google Scholar 

  8. Gergov M, Ojanpera I, Vuori E (2003) J Chromatogr B 795:41–53

    Article  CAS  Google Scholar 

  9. Escandar GM, Faber NM, Goicoechea HC, Muñoz de la Peña A, Olivieri AC, Poppi RJ (2007) Trends Anal Chem 26:752–764

    Article  Google Scholar 

  10. Booksh KS, Kowalski BR (1994) Anal Chem 66:782A

    Article  CAS  Google Scholar 

  11. Bro R (1997) Chemom Intell Lab Syst 38:149–171

    Article  CAS  Google Scholar 

  12. Sanchez E, Kowalski BR (1986) Anal Chem 58:496–499

    Article  CAS  Google Scholar 

  13. De Juan A, Casassas E, Tauler R (2002) In: Myers (ed) Encyclopedia of analytical chemistry, vol 11. Wiley, Chichester

    Google Scholar 

  14. Olivieri AC (2005) J Chemom 19:253–265

    Article  CAS  Google Scholar 

  15. Olivieri AC (2006) J Chemom 20: 1–10

    Article  Google Scholar 

  16. Despagne F, Massart DL (1998) Analyst 123:157R–178R

    Article  CAS  Google Scholar 

  17. Zupan J, Gasteiger J (1999) Neural networks in chemistry and design. Wiley, New York

    Google Scholar 

  18. Tauler R (1995) Chemom Intell Lab Syst 30:133–146

    Article  CAS  Google Scholar 

  19. Windig W, Guilment J (1991) Anal Chem 63:1425–1432

    Article  CAS  Google Scholar 

  20. Matlab 7.1 (2005) The MathWorks Inc., Natick, Massachusetts, USA

  21. Olivieri AC, Goicoechea HC, Iñon FA (2004) Chemom Intell Lab Syst 73:189–197

    Article  CAS  Google Scholar 

  22. Statgraphics Plus (1994–2000) Statistical Graphics Corp., Herndon, Virginia, USA

  23. Center V, de Noord OE, Massart DL (1998) Anal Chim Acta 376:153–168

    Article  Google Scholar 

  24. Drapper NR, Smith H (1981) Applied regression analysis, 2nd edn. Wiley, New York

    Google Scholar 

  25. Montgomery DC (1991) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  26. Haaland DM, Thomas EV (1988) Anal Chem 60:1193–1202

    Article  CAS  Google Scholar 

  27. AOAC (1993) Peer-verified method program, manual on policies and procedures. AOAC, Arlington, VA, USA

    Google Scholar 

Download references

Acknowledgment

Financial support from Universidad Nacional del Litoral and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) is gratefully acknowledged. M.J.C. thanks CONICET for a fellowship.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Héctor C. Goicoechea.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Culzoni, M.J., Goicoechea, H.C. Determination of loratadine and pseudoephedrine sulfate in pharmaceuticals based on non-linear second-order spectrophotometric data generated by a pH-gradient flow injection technique and artificial neural networks. Anal Bioanal Chem 389, 2217–2225 (2007). https://doi.org/10.1007/s00216-007-1656-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-007-1656-6

Keywords

Navigation