Principal component analysis-ranking as a variable selection method for the simultaneous spectrophotometric determination of phenol, resorcinol and catechol in real samples

Nahid Ghasemi, Mohammad Goodarzi, Morteza Khosravi

Research output: Contribution to journalArticle

Abstract

Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000.The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.

Original languageEnglish (US)
Pages (from-to)895-900
Number of pages6
JournalWorld Academy of Science, Engineering and Technology
Volume32
StatePublished - Dec 1 2009

Keywords

  • Catechol
  • Chemometrics
  • Phenol
  • Principal componentranking Artificial Neural Network
  • Resorcinol

ASJC Scopus subject areas

  • Engineering(all)

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