Authenticating Geographical Origin of Tea Using ICP-MS and Agilent Mass Profiler Professional Software

Importance of the Topic

Tea is a globally consumed beverage with significant economic value. Authenticating its geographical origin protects consumers and producers from fraud and helps maintain product quality and market trust.

Objectives and Study Overview

This study aimed to develop and validate a multielemental fingerprinting approach using ICP-MS and chemometric analysis to distinguish Indian teas from eight regions. A total of 150 tea samples, including premium varieties like Darjeeling, were analyzed to build classification models and test unknown samples.

Methodology

• Sample Collection: 150 tea samples from Dooars, Cachar, Terai, Darjeeling, Tocklai, Tripura, Upper Assam, and North Bank.
• Sample Preparation: Drying, grinding, and microwave-assisted acid digestion (5 mL HNO3 + 0.1 mL HCl) with stepwise heating up to 190 °C, diluted to 40 mL.
• Calibration: Eighteen indicator elements selected based on variability in 68 measured. Standards prepared in 5 % HNO3, internal standard Rh (1 mg/L).
• Instrumentation Conditions: Agilent 7850 ICP-MS with UHMI, ORS4 in He KED mode; RF power 1550 W; gas flows and sampling depth optimized; single-run acquisition for all elements.
• Data Processing: MassHunter for data acquisition; Agilent Mass Profiler Professional (MPP) for PCA and class prediction.

Instrumentation

• ICP-MS: Agilent 7850 with Ultra High Matrix Introduction and ORS4 collision cell.
• Sample Introduction: Agilent SPS 4 autosampler, MicroMist nebulizer, quartz spray chamber, standard torch and cones.
• Chemometrics Software: Agilent Mass Profiler Professional (v15.1).

Main Results and Discussion

• Analytical Performance: Instrument detection limits in the low μg/kg range; method detection limits accounting for dilution; QC drift < 5 %; spike recoveries 92–107 % with < 5 % RSD.
• Regional Variation: Within-region variation < 5 % for measured elements. PCA captured 72 % of variance in first three components, separating regions based on Sr, Ba, B, Cs, La, Rb, Mo, Ce, and Nd.
• Classification Models: LDA and SVM built with randomly selected calibration sets; both models correctly assigned all 24 unknown samples with high confidence (> 0.88).

Benefits and Practical Applications

• Provides a rapid, robust method for verifying tea origin and preventing mislabeling or adulteration.
• Single-run multi-element analysis with wide dynamic range reduces reruns and simplifies method setup.
• Compatible with standard ICP-MS instrumentation and chemometric tools for routine food authenticity testing.

Future Trends and Opportunities

• Extension to other foodstuffs and beverages to build comprehensive authenticity databases.
• Integration of advanced machine learning and data fusion with isotopic or molecular markers.
• Development of portable or benchtop ICP-MS systems for on-site testing and real-time verification.

Conclusion

The combined use of Agilent 7850 ICP-MS and MPP chemometric software delivers a reliable elemental fingerprinting approach for authenticating tea origin. High analytical performance and accurate classification models demonstrate its value for routine quality control and anti-fraud measures.

References

1. FAO. Commodities: Tea. Food and Agriculture Organization of the United Nations.
2. Indian Chamber of Commerce. Tea Sector Overview.
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7. Nelson J et al. Metal Content of Spices and Origin Identification. Food Quality & Safety, 2019.
8. Xu F et al. Authenticating Rice by Elemental Profiling. Agilent publication, 5994-4043EN.
9. Agilent. Octopole Collision/Reaction Cell and Helium mode. Agilent publication, 5994-1172EN.