Stable isotopic fingerprinting of authentic basmati rice from Pakistan

Abstract

Basmati rice is a popular, high-value aromatic rice which is an easy target for fraudulent activities such as mislabeling and substitution with non-basmati cultivars. Bulk isotope ratio mass spectrometry (IRMS) combined with multivariate analysis was employed to develop a model to identify the geographical origin of rice and authenticate different rice cultivars from Pakistan. ANOVA showed significant statistical differences for δ13C, δ15N, δ2H and δ18O isotopes amongst different basmati and non-basmati rice cultivars. δ2H and δ18O values showed a larger variation between basmati and non-basmati rice cultivars. Multivariate ANOVA showed a significant influence on rice from different regions, cultivars and their stable isotopic values. Regional effects contributed the highest variation for both δ13C and δ15N isotopes (56.6 % and 42.7 %), whereas cultivar type largely contributed to differences in δ2H and δ18O values. Finally, supervised classification models (LDA and PLS-DA) were constructed to assess origin classification ability. The PLS-DA model achieved a higher origin classification accuracy in both training and validation sets (76.7 % and 70.0 %, respectively). In conclusion, isotopic fingerprints along with multivariate statistical analysis showed good potential to characterize rice according to cultivar type and production region. This study provides valuable insights to authenticate Pakistan basmati rice from other non-basmati cultivars, and will be instrumental in developing new tools for industry and regulatory agencies to control fraudulent labeling of basmati rice.