A Metabolomics Approach to Multivariate Analysis of Black Pepper Using LC/Q-TOF

Importance of the topic

Black pepper is one of the most widely traded spices worldwide and commands premium prices when provenance and quality are established accurately.
Adulteration or mislabeling of geographic origin can reduce product value and pose health and trust concerns for consumers and producers alike.
Applying untargeted metabolomics and chemometric classification addresses these challenges by providing a high-throughput fingerprinting approach for food authenticity.

Study objectives and overview

The study aimed to discriminate black pepper samples from three Vietnamese regions (Phu Quoc Island, DakLak, Binh Phuoc) using a combined workflow of UHPLC/Q-TOF metabolite profiling and multivariate analysis.
The approach also evaluated the capability to detect intentional adulteration at defined mixing levels.

Methodology and instrumentation

Sample collection and preparation:

• 125 pepper samples (40 Binh Phuoc, 40 Phu Quoc, 45 DakLak) collected during Feb–Mar 2022.
• Freeze-drying, grinding to 400 µm, ultrasound-assisted extraction in water/methanol, centrifugation, filtration and 10× dilution with internal standard (Carbendazim-D3).

Chromatography:

• Agilent 1290 Infinity II UHPLC with ZORBAX RRHD Eclipse XDB-C18 (2.1×100 mm, 1.8 µm).
• Gradient elution (H2O/MeOH with 5 mM ammonium formate and 0.1% formic acid) at 0.3 mL/min over 23 min, 35 °C column.

Mass spectrometry:

• Agilent 6546 Q-TOF with Dual Jet Stream ESI in positive and negative modes.
• Mass range m/z 50–1000, resolution 60 000, reference masses for realtime calibration.

Data analysis software:

• MassHunter Profinder 10.0 for recursive feature extraction.
• Mass Profiler Professional 15.1 for normalization, filtering, and multivariate analysis.
• Classifier 1.0 for model validation and sample prediction.

Key results and discussion

Data quality and reproducibility:

• Total ion chromatograms were consistent across regions.
• Mass accuracy better than 2 ppm, retention time drift <0.03 min, internal standard RSD <10%.

Multivariate analysis workflow:

• 4 791 initial features reduced to 4 608 by frequency filter and to 132 by ANOVA, fold change and volcano plot.
• PCA showed clear separation along PC1 and PC2 (57% cumulative variance), with Phu Quoc distinct from others.
• HCA dendrogram confirmed clustering in three geographic groups.

PLS-DA classification model:

• 132 features, 64 with VIP >1 contributed most to discrimination.
• R2Y=0.919 and Q2=0.833 demonstrating high fit and predictive power.
• Cross-validation achieved 100% accuracy on training and validation sets.

Validation and adulteration detection:

• Classifier 1.0 predicted pure and QC samples with 100% confidence values >0.88 (RSD <8%).
• Adulterated mixtures (70:30, 50:50) were accurately flagged by reduced confidence scores using a 0.8 cutoff.

Identification of unique markers:

• Venn analysis revealed 37 features unique to Phu Quoc, 15 to Binh Phuoc, and 12 to DakLak.
• Tentative annotation via METLIN assigned 22 compounds including amides, phenolics, alkaloids and lipids.

Benefits and practical applications

This workflow provides a rapid and robust tool for food authenticity labs to verify black pepper origin and detect adulteration.
High resolution LC/Q-TOF combined with chemometrics supports quality control, regulatory compliance, and brand protection.

Future trends and potential applications

Integration of metabolomics with spectroscopic techniques and data fusion strategies could enhance throughput and scope.
Expansion to larger sample sets and machine learning approaches may improve detection limits and predictive models.
Real-time monitoring and portable LC-MS devices may enable on-site authentication in supply chains.

Conclusion

The combination of UHPLC/Q-TOF metabolite profiling and multivariate classification delivers accurate, reproducible discrimination of black pepper geographic origin.
Adulterated samples are reliably detected, demonstrating the method s value for industry and food safety monitoring.

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