Differentiating Pakistani long-grain rice grown inside and outside the accepted Basmati Himalayan geographical region using a 'one-class' multi-element chemometric model. (May 2021) - Arif, M.; Chilvers, G.; Day, S.; Naveed, S.A.; Woolfe, M.; Rodionova, O.Ye.; Pomerantsev, A.L.; Kracht, O.; Brodie, C.; Mihailova, A.; Abrahim, A.; Cannavan, A.; et al.
Abstract: "Basmati" is the name used for a class of rice comprising a few defined varieties grown in several defined regions of the Indo-Gangetic Plains in India and Pakistan. Due to its highly valued qualities Basmati rice is sold at a premium price, which makes it susceptible to fraud due to economically motivated adulteration. Unscrupulous producers or distributors can increase profits by mislabeling rice grown outside the recognized Basmati region and passing it off as Basmati rice. Therefore, the authentication of the geographical origin of Basmati rice is of high importance for the entire Basmati rice supply chain and is becoming increasingly so due to pending applications for Geographical Indication (GI) status with the European Commission. Although elemental analysis has been applied previously for the authentication of rice, only a few studies have assessed the application of elemental analysis for the authentication of the geographical origin of Basmati rice. This study demonstrates the high potential of the combination of elemental analysis and data-driven soft independent modelling by class analogy (DD-SIMCA) for the differentiation of Pakistani rice grown inside and outside the recognized Basmati growing region, thus permitting the 'geographical component' of Basmati rice authenticity to be verified. The achieved sensitivity and specificity of the one class DD-SIMCA model is 100% and 98%, respectively. Highlights: The authentication of the geographical origin of Basmati rice is of high importance. Multi-element analysis of Pakistani long grain rice was performed. Multi-element chemometric modelling differentiated Basmati and non-Basmati regions. Sensitivity and specificity of the one-class model is 100% and 98%, respectively.