Black Pepper Authenticity Workflow Using the High-Resolution Agilent 7250 GC/Q-TOF

Significance of the Topic

Black pepper is one of the most traded spices worldwide and its high value makes it a frequent target for economically motivated adulteration. Detecting low-level admixtures with cheaper materials such as Szechuan pepper or papaya seeds is essential for protecting consumers and maintaining product integrity. High-resolution mass spectrometry combined with non-targeted data analysis offers a powerful approach to uncover complex adulteration schemes and ensure routine food authenticity testing.

Objectives and Study Overview

This study aimed to develop and validate a comprehensive workflow using the Agilent 7250 GC/Q-TOF system coupled with Mass Profiler Professional (MPP) and MassHunter Classifier software. Key objectives included:

• Building a robust classification model to distinguish black pepper from different geographic origins (Malabar and Phu Quoc).
• Detecting adulteration with Szechuan pepper and papaya seeds at levels as low as 5 %.
• Comparing two classification algorithms (PLS-DA and SIMCA) for sensitivity and reliability in routine testing.

Methodology

Sample Preparation:
A total of pure black pepper samples (Malabar and Phu Quoc), Szechuan pepper, and papaya seeds were finely ground. Adulterated mixtures were prepared by blending Malabar pepper with 5 – 50 % Szechuan or papaya seed material. Each 0.5 g sample underwent sequential extraction with hexane and acetone, filtration, and solvent evaporation.

Data Acquisition and Processing:
Analyses were performed on an Agilent 7890B GC coupled to the 7250 Q-TOF operating in full-spectrum electron ionization (EI) and low-energy EI modes. Chromatographic deconvolution and tentative compound identification used the NIST17 library and retention index calibration. Features were aligned, normalized to an internal standard, and filtered for reproducibility.

Statistical Workflow:
Mass Profiler Professional (MPP) facilitated feature selection and model construction. Principal component analysis assessed group separation. ANOVA (p < 0.005) and fold-change filtering (FC > 10) distilled hundreds of spectral features down to the most discriminating markers. Classification models were then built using partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). Unknown validation samples bypassed MPP and were classified directly in MassHunter Classifier.

Instrumentation

• Agilent 7890B Gas Chromatograph with DB-5MS UI column (30 m × 0.25 mm, 0.25 µm).
• Agilent 7250 GC/Q-TOF Mass Spectrometer configured for 70 eV EI and 12 eV low-energy EI.
• MassHunter Quantitative Analysis with Unknowns Analysis for deconvolution.
• Agilent Mass Profiler Professional 15.1 and MassHunter Classifier 1.1 for statistical modeling and classification.

Main Results and Discussion

Chromatographic profiles of pure Malabar and Phu Quoc peppers were broadly similar, while Szechuan pepper displayed distinct abundant peaks and papaya seed extracts yielded simpler spectra. A selection of terpenes (e.g., β-pinene, limonene, caryophyllene), piperine, hydroxy-sanshool isomers, and benzyl isothiocyanate formed the core of the classification features. Mass accuracy for all major ions was within 2 ppm.

Model Performance:

• PLS-DA: Provided a confidence score for class assignments. Inclusion of papaya seed signatures in the training set notably improved detection of papaya adulteration; otherwise, low-level adulteration remained ambiguous.
• SIMCA: Reported distance metrics and detected adulteration down to 5 % levels for both Szechuan and papaya seeds—even when papaya was omitted from the model.

Visualization in the Classifier software clearly separated pure and adulterated samples on 3D PCA plots, and flagged out-of-range compound intensities for rapid decision making.

Benefits and Practical Applications

• Non-targeted HRMS profiling captures dozens of distinguishing features, making adulteration replication difficult for fraudsters.
• The combination of high‐resolution GC/Q-TOF data and robust chemometric tools supports rapid routine screening in food quality control laboratories.
• Flexibility to extend the approach to other spices or adapt to LC/Q-TOF platforms enhances utility across diverse matrices.

Future Trends and Potential Applications

Advancements in high-throughput data processing, cloud‐based chemometric platforms, and machine learning algorithms will further streamline non-targeted authenticity workflows. Integration with spectral libraries and real-time decision support systems may enable on-site or at-line monitoring of spices and other high-value commodities. Extension of this approach to detect multiple simultaneous adulterants and emerging contaminants represents a promising avenue for future research.

Conclusion

This study demonstrates a comprehensive, non-targeted GC/Q-TOF workflow for black pepper authenticity testing. Both PLS-DA and SIMCA models effectively distinguished pure geographic origins and quantified adulteration down to 5 % levels. SIMCA showed superior sensitivity when unknown adulterants were present. The methodology offers a robust solution for routine food fraud detection and can be readily adapted to other matrices and instrumentation platforms.

References

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