In this study (open access), headspace solid-phase microextraction for sample extraction followed by untargeted gas chromatography coupled to high-resolution mass spectrometry (HS-SPME-GC-HRMS) for volatile compounds was used to build a classification model to discriminate saffron, safflower, calendula, capsicum and turmeric. (the latter four being potential adulterants of saffron). The model was based on reference analysis of 38 authentic saffron (Crocus sativus L.) samples from different origins (Iran, Spain, Greece and Italy) 6 samples of turmeric (Curcuma longa L), 9 of calendula (Calendula officinalis L), 6 of capsicum (Capsicum annum L) and 4 of safflower (Carthamus tinctorius L., n = 4). The instrument software was used to normalise the signals from the less concentrated or less responsive volatile compounds. Unsupervised PCA and supervised PLS-DA gave a chemometric model that could clearly differentiate pure samples of all 5 species.
The researchers then sought specific volatile markers for each species using the pattern search function of MetaboAnalyst software. This function uses a template matching method and the results are expressed as a ranked list of variables with the Spearman correlation coefficient and p-value. They short-listed any compound with a Spearman correlation coefficient ≥ 0.80. Tentative Identification of short-listed ‘markers’ was performed using mass spectra NIST 17 library. Only compounds with match factor ≥ 750 and relevant Kovats retention indexes (RI) relative to n-alkanes (C7–C30) were considered. The compliance of exact mass of detected ions (mass error < 5 ppm) and isotopic pattern were used to confirm the identification.
Once markers were identified they were used to build specific classification models to differentiate pure saffron from saffron adulterated with each specific species. Models were built using in-house prepared mixes at 20, 10, 4 and 2% adulteration in each case. The authors could successfully detect 2% adulteration with each of the 4 species modelled.
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