Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo

K M Sharif, M. M. Rahman, J. Azmir, A. Khatib, E. Sabina, S. H. Shamsudin, I. S.M. Zaidul

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1 Citation (Scopus)

Abstract

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1+1+0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed.

Original languageEnglish
Pages (from-to)1826-1833
Number of pages8
JournalBiomedical Chromatography
Volume29
Issue number12
DOIs
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Keywords

  • Multivariate image analysis
  • Orthogonal partial least square model
  • Thin-layer chromatography

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