WCPS: Authenticating Geographical Origin of Tea from the North- East Region of India Using ICP-MS and Agilent Mass Profiler Professional Chemometrics Software

Significance of the Topic

This study addresses the need for robust methods to verify the geographical origin of high-value teas. Tea fraud undermines consumer trust and impacts quality assurance in the global tea market. Elemental fingerprinting by ICP-MS offers a reliable analytical approach to distinguish regional signatures.

Study Objectives and Overview

The primary goal was to develop and validate a multielement ICP-MS method combined with chemometric classification for teas from eight northeast Indian regions. A total of 150 samples, including premium Darjeeling, were analyzed to build predictive models and assess their accuracy on unknown samples.

Methodology and Instrumentation

Tea leaves (0.50 g) were predigested with HNO₃/HCl and subjected to microwave digestion in PTFE vessels. Digested samples were analyzed on an Agilent 7850 ICP-MS using helium collision mode. Concentrations of 18 selected elements were processed in Agilent Mass Profiler Professional software. Linear discriminant analysis (LDA) and support vector machine (SVM) models were built and validated with a separate set of unknown teas.

Used Instrumentation

• Agilent 7850 ICP-MS with MicroMist nebulizer, Peltier-cooled spray chamber, quartz torch, nickel cones, and KED He cell mode
• Anton Paar microwave digestion system with PTFE vessels
• Agilent Mass Profiler Professional chemometrics software

Main Results and Discussion

Instrument detection limits and method detection limits were sufficiently low for all markers. Spike recoveries ranged within 100 ± 10% (RSD < 5%), confirming minimal matrix effects. Principal component analysis captured 72% of variance across three components and clearly separated tea samples by region. Both LDA and SVM models achieved 100% correct classification of 24 unknown samples with high confidence scores.

Benefits and Practical Applications

• Delivers a high-throughput, accurate tool for tea origin authentication
• Strengthens quality control and fraud prevention in tea supply chains
• Adaptable to authenticity studies of other food and agricultural products

Future Trends and Potential Uses

Future developments may integrate isotope ratio analysis, extend the approach to a broader range of food matrices, employ portable ICP-MS instruments, and apply advanced machine learning to enhance classification speed and resolution for real-time authenticity screening.

Conclusion

This work demonstrates that multielement ICP-MS fingerprinting combined with robust chemometric models reliably discriminates tea origins in northeast India. The validated LDA and SVM classifiers provide a powerful framework to authenticate high-value teas and counter food fraud.

References

1. Bappaditya Kanrar et al. Food Chemistry Advances 1, 2022
2. Jenny Nelson et al. Authentic Spices: Method for Identifying the Country of Origin, Food Quality and Safety, 2019