Self-modeling curve resolution techniques applied to comparative analysis of volatile components of Iranian saffron from different regions Original Research Article

Volatile components of saffron from different regions of Iran were extracted by ultrasonic-assisted solvent extraction (USE) and were analyzed by gas chromatography–mass spectrometry (GC–MS). Self-modeling curve resolution (SMCR) was proposed for resolving the co-eluted GC–MS peak clusters into pure chromatograms and mass spectra. Multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least square (MCR-ALS) were successfully used for this purpose. The accuracy of the qualitative and quantitative results was improved considerably using SMCR techniques. Comparison of the results of saffron from different regions of Iran showed that their volatile components are different from chemical components and relative percentages points of view. Safranal is the main component of all samples. In addition, 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde (HTCC), 2(5H)-furanone, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one and 2(3H)-furanone, dihydro-4-hydroxy were common in all samples with high percentages. The results proved that combining of SMCR techniques with USE-GC–MS produces a powerful tool for the analysis of the complex samples.