Classification of Black Mahlab seeds (Monechma ciliatum) using GC-MS and FT-NIR and simultaneous prediction of their major volatile compounds using chemometrics.

The identification of geographical origin is an important factor in assessing the quality of aromatic and medicinal seeds such as Black Mahlab (Monechma ciliatum). However, at present, there are no studies concerning Black Mahlab Seeds (BMSs). To identify the geographical origin of BMSs, we have used gas chromatography-mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FT-NIR) combined with chemometrics. Chemometrics analysis showed that FT-NIR and GC-MS can be used to discriminate the geographical origin of BMSs. FT-NIR coupled with the partial least squares regression (PLSR) was applied to develop the calibration models. The calibration models had a coefficient of determination (Rc2) of 0.82 for coumarin and 0.81 for methyl salicylate. The prediction model (Rp2) values ranged from 0.83 for coumarin to 0.77 for methyl salicylate. Overall, the chemometrics presented correct classification, and PLSR accurately predicted the volatiles, with an RMSEP range of 0.9 to 0.16 for the two volatiles targeted.