Volatiles Fingerprinting of Aromatic Rice Cultivars for Varietal Discrimination Using Gas Chromatography–Flame Ionization Detector

Abstract

Aromatic rice has become an important commodity in global trade and commands a market price much higher than that of ordinary rice; thus, evaluation and monitoring of its authenticity have become a major concern among consumers and traders. Mass spectrometry, olfactometry, and flame photometry have been incorporated with gas chromatography to differentiate rice varieties. However, these systems are complex, expensive, and time-consuming. This study investigated the combination of headspace gas chromatography–flame ionization detector (HS-GC/FID) with multivariate data analysis for the chemometric differentiation of aromatic rice. The seven cultivars Basmati, Dinorado, Jasmine, Milagrosa, NSIC Rc148, Rc342, and Rc344 were characterized by different volatile patterns. Differences in the concentrations of volatiles were found to be useful in differentiating the varieties based on patterns and clusters generated through principal components analysis (PCA) and agglomerative hierarchical clustering (AHC), respectively. Visual patterns from the PCA prove that the technique was able to accurately classify (non-error rate ≈ 95%) the samples into different varieties. Correspondingly, AHC generated three clusters: [Group I, imported] Basmati, Jasmine, and NSIC Rc342 (in-bred rice with Jasmine parental line); [Group II, in-bred] NSIC Rc148 and Rc344; and [Group III, traditional Philippine rice] Dinorado and Milagrosa. Results demonstrated that chemometric analysis of HS-GC/FID chromatograms can be a reliable technique of high potential to discriminate aromatic rice samples based on their volatile fingerprints. The study provided a possible inexpensive and non-destructive alternative that has not been explored before to assess the authenticity of rice varieties using an existing analytical platform.