Authenticating Rice by Elemental Profiling Using ICP-MS and Statistical Modeling

Importance of the topic

Rice serves as a primary staple for nearly half of the world’s population and its market value often depends on geographic origin. Premium rice varieties are targets for food fraud through mislabeling or adulteration, leading to economic losses and potential regulatory penalties. Reliable authentication of rice origin is therefore critical for producers, retailers, regulators, and consumers to ensure product integrity and compliance.

Study objectives and overview

This study aimed to develop a robust approach for determining the geographical origin of rice samples by combining multi‐elemental profiling via inductively coupled plasma mass spectrometry (ICP-MS) with advanced statistical modeling. Ninety rice samples from five distinct Chinese provinces were analyzed to build classification models, which were then validated using 24 blind test samples.

Methodology

Sample collection included 90 authenticated rice lots (15 from each province, with two varieties from Liaoning). Each sample (0.5 g) underwent microwave-assisted acid digestion in PTFE vessels using concentrated HNO₃. Digests were diluted to 50 mL and spiked with a rhodium internal standard. Twenty-four elements were quantified by Agilent 7900 ICP-MS operating in helium collision mode to remove polyatomic interferences. Quality control employed NIST SRM 1568b rice flour, yielding recoveries of 80–120% for most elements. Detection limits and background equivalent concentrations were established for each analyte.

Used Instrumentation

• Agilent 7900 ICP-MS with ORS4 collision/reaction cell (helium mode)
• Agilent SPS 4 autosampler
• Glass concentric nebulizer and quartz double-pass spray chamber
• Anton Paar microwave digestion system
• Agilent MassHunter and Mass Profiler Professional software

Main results and discussion

PCA of the 24‐element data captured 65% of variance across the first three components, enabling clear separation of most regional groups. Elements driving discrimination included B, Na, Zn, Cd, Al, Fe, Mn, Sr, Cu, Se, and Rb. Four classification algorithms (PLS-DA, SVM, LDA, SIMCA) were trained on 66 samples and tested on 24 unknowns. PLS-DA, SVM, and LDA achieved 100% correct assignments with high confidence scores. SIMCA misclassified one sample consistent with its overlapping PCA profile.

Benefits and practical applications of the method

• High‐throughput, multi‐element fingerprinting of rice origin
• Robust performance over long analytical sequences
• Scalable to other food products vulnerable to fraud
• Compliance tool for quality control and regulatory verification

Future trends and applications

Advances may include integration of stable isotope ratio analysis, expansion of regional sample libraries, and the application of deep learning for improved classification. Miniaturized or portable MS systems could enable on‐site authenticity screening, while real-time monitoring protocols could further safeguard supply chains.

Conclusion

The combination of Agilent 7900 ICP-MS elemental profiling with chemometric modeling provides a reliable, accurate, and scalable solution for rice origin authentication. The methodology demonstrated full discrimination of test samples and is well suited for routine food fraud detection and quality assurance.

References

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