Workflow for Food Classification and Authenticity using Yerba Mate and High-Resolution GC/Q-TOF

Significance of the Topic

Food fraud and adulteration pose serious risks to consumer trust, public health, and brand integrity. Reliable analytical workflows are essential to detect mislabeling, dilution, and contaminations in complex food matrices while supporting regulatory compliance and quality assurance.

Objectives and Study Overview

This study presents a novel food authenticity workflow applied to yerba mate, a popular South American herbal tea. The approach integrates high-resolution GC/Q-TOF data acquisition with specialized software tools for feature extraction, classification model building, and routine screening of pure and adulterated samples.

Methodology

Yerba mate samples from four commercial brands (A, B, C and D) were obtained and pure samples of A were spiked with C or D at varying levels (5–80%). A standard EN QuEChERS protocol was used to extract volatiles and potential contaminants. Extracts were analyzed in randomized order by GC/Q-TOF in electron ionization mode. Full-scan accurate mass spectra were acquired, followed by deconvolution, NIST library matching, and retention index calculations. Data processing and feature finding were performed in the Unknowns Analysis tool, followed by multivariate analysis in MassProfiler Professional (MPP) and classification model validation in Classifier software using a SIMCA algorithm.

Used Instrumentation

• Agilent 7890B Gas Chromatograph with DB-5ms UI column (30 m × 0.25 mm, 0.25 µm) and splitless multimode inlet
• Agilent 7250 High-Resolution Q-TOF Mass Spectrometer in EI mode (mass range 45–650 m/z, acquisition rate 5 Hz)
• Helium carrier gas, inlet temperature 280 °C, oven program from 50 °C to 300 °C at 10 °C/min with 10 min hold
• Data acquisition and processing: Agilent MassHunter Quantitative Analysis 10.1, Unknowns Analysis, MassProfiler Professional 15.1, Classifier 1.1

Main Results and Discussion

PCA plots in MPP demonstrated clear separation of the three model brands based on minor volatile components. Volcano plots identified key aroma compounds (furanones, ionones, terpene derivatives) differing significantly between brands A and C. Some aldehydes associated with adulterating species were elevated in C, while furanones and epoxy-ionones prevailed in A. Environmental contaminants such as PAHs were detected and varied by brand, reflecting processing differences. The SIMCA classification model distinguished pure and adulterated samples, reliably detecting as little as 5 % C adulteration. Distance-to-model metrics in Classifier confirmed the model’s ability to identify unknowns and negative controls, even for brand D not included in model training.

Benefits and Practical Applications

This workflow enables rapid, sensitive, and reproducible screening for food authenticity and fraud detection in routine analytical laboratories. By leveraging accurate mass GC/Q-TOF and automated data analysis, laboratories can streamline quality control of herbal products, oils, spices, and other complex matrices.

Future Trends and Opportunities

Integration of expanding accurate-mass libraries, machine-learning classifiers, and cloud-based data sharing will enhance detection sensitivity and model robustness. Coupling this workflow with complementary techniques (e.g., LC/Q-TOF, IMS) and miniaturized instrumentation may enable on-site authenticity testing and broader application across the food supply chain.

Conclusion

A comprehensive GC/Q-TOF-based workflow combined with advanced software tools successfully classified yerba mate brands, identified marker compounds, and detected low-level adulteration. The approach offers a versatile platform for routine food authenticity testing and fraud prevention.

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