• Issam Barra
  • Mourad Kharbach
  • Mohamed Bousrabat
  • Yahia Cherrah
  • Mohamed Hanafi
  • El Mostafa Qannari
  • Abdelaziz Bouklouze

The purpose of this study was to perform a discrimination and classification of diesel samples from the four major suppliers of petroleum products in Morocco using Fourier Transform Infrared Spectroscopy (FTIR), Gas Chromatography coupled with Mass Spectrometry (GC-MS) and chemometrics tools. Eighty diesel samples were collected from different gas stations owned by the four biggest brands in the Moroccan market. Principal Component Analysis (PCA) was performed to depict the similarities between the samples and check the presence of outliers. Partial Least Squares Discriminant Analysis (PLS-DA) models were set up for the discrimination and the classification of the four groups of samples (i.e., diesel suppliers). The models proposed in this study, were characterized by good prediction abilities, especially the FTIR-PLSDA model that was characterized by 100% of accurate discrimination of the four groups. The approach of analysis showed that the FTIR spectra can provide a cheap and rapid means for the determination of the diesel origin and to ensure the traceability of diesel products marketed in Morocco with respect for the rules of the green chemistry.

Original languageEnglish
Article number120543
Publication statusPublished - 1 Mar 2020

    Research areas

  • Chemometrics, Diesel, FTIR, GC-MS, PLS discriminant analysis

ID: 48821280