Philippine Journal of Science
152 (4): 1475-1482, August 2023
ISSN 0031 – 7683
Date Received: 09 Feb 2023

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

Michael E. Serafico1,2* and Fortunato III B. Sevilla1,3

1The Graduate School, University of Santo Tomas, España Boulevard,
Sampaloc, Manila 1008 Metro Manila, Philippines
2Food and Nutrition Research Institute, Department of Science and Technology,
Gen. Santos Ave., Bicutan, Taguig City 1630 Metro Manila, Philippines
3Research Center for the Natural and Applied Sciences, University of Santo Tomas,
España Boulevard, Sampaloc, Manila 1008 Metro Manila, Philippines

*Corresponding author: michaelserafico@gmail.com

[Download]
Serafico M, Sevilla F. 2023. Volatiles Fingerprinting of Aromatic Rice Cultivars for Varietal
Discrimination Using Gas Chromatography–Flame Ionization Detector. Philipp J Sci 152(4): 1475–1482.
https://doi.org/10.56899/152.04.16

 

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.