A Two-tiered system of analysis to tackle rice fraud: The Indian Basmati study

Maeve Shannon*, Terry McGrath, Christopher Elliott, Ratnasekhar Ch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
142 Downloads (Pure)


Demand for high quality Basmati rice has increased significantly in the last decade. This commodity is highly vulnerable to fraud, especially in the post COVID-19 era. A unique two-tiered analytical system comprised of rapid on-site screening of samples using handheld portable Near-infrared NIR and laboratory confirmatory technique using a Head space gas chromatography mass spectrometry (HS-GC-MS) strategy for untargeted analysis was developed. Chemometric models built using NIR data correctly predicted nearly 100% of Pusa 1121 and Taraori, two high value types of Basmati, from potential adulterants. Furthermore, rice VOC profile fingerprints showed very good classification (R2 >0.9, Q2 > 0.9, Accuracy > 0.99) for these high quality Basmati varieties from potential adulterant varieties with aldehydes identified as key VOC marker compounds. Using a two-tiered system of a rapid method for on-site screening of many samples alongside a laboratory-based confirmatory method can classify Basmati rice varieties, protecting the supply chain from fraud.
Original languageEnglish
Article numberTAL_122038
Early online date19 Dec 2020
Publication statusEarly online date - 19 Dec 2020


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