Laser desorption/ionization mass spectrometry fingerprinting of complex hydrocarbon mixtures: Application to crude oils using data mining techniques

Hien P. Nguyen, Israel P. Ortiz, Chivalai Temiyasathit, Seoung Bum Kim, Kevin A. Schug

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Crude oil fingerprints were obtained from four crude oils by laser desorption/ionization mass spectrometry (LDI-MS) using a silver nitrate cationization reagent. Replicate analyses produced spectral data with a large number of features for each sample (>11 000 m/z values) which were statistically analyzed to extract useful information for their differentiation. Individual characteristic features from the data set were identified by a false discovery rate based feature selection procedure based on the analysis of variance models. The selected features were, in turn, evaluated using classification models. A substantially reduced set of 23 features was obtained through this procedure. One oil sample containing a high ratio of saturated/aromatic hydrocarbon content was easily distinguished from the others using this reduced set. The other three samples were more difficult to distinguish by LDI-MS using a silver cationization reagent; however, a minimal number of significant features were still identified for this purpose. Focus is placed on presenting this multivariate statistical method as a rapid and simple analytical procedure for classifying and distinguishing complex mixtures.

Original languageEnglish
Pages (from-to)2220-2226
Number of pages7
JournalRapid Communications in Mass Spectrometry
Volume22
Issue number14
DOIs
Publication statusPublished - 2008 Jul
Externally publishedYes

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy
  • Organic Chemistry

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