In this study, near-infrared (NIR) spectroscopy, in combination with chemometrics, was used as a rapid tool for determining if exposure to contamination from mine tailings influences the matrices of the specimens, compared to those from natural populations. Principal component analysis (PCA) plots were made from the chemometric models obtained to establish if season of harvest, geographical origin, and level of soil contamination play a determining role in the chemical profiles of the individual specimens harvested from mine sites or natural populations. The random distribution on PCA score plots corroborated the intraspecies variation of Lippia scaberrima previously observed by gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) essential oil profiles. Clustering according to the season and origin of the individual plants confirmed that the geographic location and the season of harvest influence the chemical profiles of L. scaberrima. The NIR data could not be correlated with the level of soil contamination to which the specimens were exposed. The PCA scores and loadings plots obtained from NIR data of Searsia pendulina suggest that the species is tolerant to pollution from mine tailings. Although separation was obtained in a three-component PCA model between specimens sampled during different seasons, some clustering was observed by specimens from the same geographical origin.
Conference
, 2012-07-08 - 2012-07-11, Analytical Chemistry Conference African Network of Analytical Chemists (SEANAC), 4th, Maputo, Mozambique
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