Comparative investigation of two different self-organizing map-based wavelength selection approaches for analysis of binary mixtures with strongly overlapping spectral lines

  • Lawan Sratthaphut
  • Sathit Niratisai
  • Onoomar Toyama
Keywords: Co-trimoxazole, Self-organizing map, Wavelength selection, Pharmaceutical analysis, Overlapping spectral lines

Abstract

Purpose: To demonstrate the ability and investigate the performance of two different wavelength selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) regression for analysis of pharmaceutical binary mixtures with strongly overlapping spectra.

Methods: Two different variable selection methods were compared, namely, SOM1-PLS and SOM2- PLS. The main difference between these methods involved the structure of neurons in input layer and the algorithm for variable selection. Adjustable parameters for each technique were optimized for better comparison. The performance of these methods was statistically verified for predictive ability using both synthetic mixtures and a real combination product of sulfamethoxazole (SMX) and trimethoprim (TMP), which exhibited strongly overlapping of spectral lines.

Results: The results obtained indicate that SOM2-PLS was more efficient than SOM1-PLS technique with 30 and 6 % improvement in predictive ability for SMX and TMP, respectively. Furthermore, the mean difference between the results obtained from SOM2-PLS method and those from the official method was not statistically significant as p-value was more than 0.01.

Conclusion: Although, SOM2-PLS method is more efficient than SOM1-PLS method for the analysis of pharmaceutical binary mixtures with severely overlapping spectra, some problems associated with SOM2-PLS technique include difficult computations of some parameters.

Keywords: Co-trimoxazole, Self-organizing map, Wavelength selection, Pharmaceutical analysis, Overlapping spectral lines

Published
2017-08-03
Section
Articles

Journal Identifiers


eISSN: 1596-9827
print ISSN: 1596-5996