The Use of HRMS and Statistical Analysis in the Investigation of Basmati Rice Authenticity and Potential Food Fraud

Significance of the Topic

The authenticity of basmati rice directly affects consumer trust, market value, and regulatory compliance. Widespread adulteration with inferior long‐grain varieties undermines quality, risks economic loss, and poses challenges for food safety and traceability. Advanced analytical strategies are therefore essential to reliably distinguish genuine basmati from fraudulent substitutes.

Objectives and Study Overview

This proof‐of‐principle study aimed to develop a non‐targeted workflow combining high‐resolution mass spectrometry and multivariate statistics to investigate the authenticity of basmati rice. Off‐the‐shelf supermarket samples of basmati, jasmine, and long‐grain rice were analyzed to identify chemical fingerprints and potential markers of food fraud.

Methodology

Sample preparation involved weighing 10 g of dried rice into headspace vials, followed by heated solid‐phase microextraction (SPME) at 120 °C. Chromatographic separation was achieved on a DB‐5MS GC column under a temperature gradient. Soft ionization via atmospheric pressure gas chromatography (APGC) minimized in‐source fragmentation. Detection employed a SYNAPT G2‐Si mass spectrometer operated in HDMSE mode, capturing accurate‐mass precursor and fragment data with an additional ion mobility dimension.

Four‐dimensional data were processed using Progenesis QI software for retention time and drift alignment, peak deconvolution, and compound quantification. Statistical analyses included principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (OPLS‐DA), and correlation analysis to isolate significant markers.

Instrumentation Used

• CTC PAL autosampler with DVB/CAR/PDMS SPME fiber assembly
• Agilent 7890A gas chromatograph with DB‐5MS column
• Waters SYNAPT G2‐Si high‐definition mass spectrometer in HDMSE mode
• MassLynx software for instrument control and structural elucidation
• Progenesis QI software for multi‐dimensional data processing and statistical analysis

Main Results and Discussion

A total of 3,885 compound ions were detected across all injections. PCA revealed distinct clustering of basmati, jasmine, and long‐grain samples, with two basmati samples grouping anomalously with non‐basmati counterparts from the same producers, suggesting potential mislabeling or packaging effects. OPLS‐DA highlighted key discriminatory ions, and standardized abundance profiles identified six basmati‐related markers. Correlation analysis further extracted 57 markers correlating with basmati profiles and 26 markers associated with long‐grain rice. These findings demonstrate the method’s ability to isolate characteristic chemical signatures but also underscore the need for larger, well‐characterized sample sets to validate marker specificity.

Benefits and Practical Applications

This workflow delivers an information‐rich, non‐targeted approach to food authenticity testing by leveraging four‐dimensional MS data for enhanced specificity. Automated alignment and deconvolution streamline data handling, while multivariate models facilitate rapid marker discovery. The method can support quality control laboratories in detecting adulteration, safeguarding supply chains, and ensuring regulatory compliance.

Future Trends and Applications

Future work involves expanding the dataset to include authenticated and adulterated reference samples for method validation and development of targeted pass/fail assays on more routine MS platforms such as single or tandem quadrupoles. Integration of embedded elemental composition algorithms and comprehensive databases will enhance identification confidence. Wider adoption of HRMS fingerprinting is anticipated in regulatory frameworks for food fraud prevention.

Conclusion

This study illustrates a robust proof‐of‐principle workflow combining APGC‐HDMSE and advanced informatics to discriminate basmati rice authenticity. While promising discriminatory markers were identified, further validation with extensive sample cohorts is required to translate this approach into routine quality control assays.

References

1. Jagannathan P. Basmati export adulteration leaves bad taste in mouth. The Economic Times. 2007 Jul 6.
2. Fletcher A. Contamination concerns force new Basmati regulations. FoodNavigator. 2005 Aug.
3. Waters Corporation. SYNAPT G2‐Si Product Brochure. Part No. 720004681EN.
4. Waters Corporation. APGC White Paper. Part No. 720004771EN.