Classification of Chamomile Flowers, Essential Oils, and Commercial Products Using Chemometrics and the Agilent 5975 GC/MSD

Significance of the topic

A reliable classification of chamomile varieties is critical for ensuring the safety, quality and therapeutic efficacy of herbal products. Misidentification or adulteration of chamomile raw materials can lead to inconsistent active ingredient profiles, allergic reactions or reduced clinical benefits. Implementing robust analytical methods enhances quality control in food, pharmaceutical and dietary supplement industries.

Study objectives and overview

This study aimed to develop and validate an untargeted gas chromatography–mass spectrometry (GC/MS) workflow combined with multivariate chemometric analysis to distinguish among the three main types of chamomile used in commercial products: German chamomile (Matricaria recutita), Roman chamomile (Chamaemelum nobile) and Juhua (Chrysanthemum morifolium). The model was then applied to 35 solid herbal preparations and 11 essential oils to evaluate commercial authenticity.

Instrumentation

The analytical platform comprised an Agilent 7890 gas chromatograph fitted with an Agilent 7693A automatic liquid sampler, coupled to an Agilent 5975 MSD. Key GC conditions included a HP-5MS 30 m × 0.25 mm column, split injection (25:1), and a multistep oven program from 45 °C to 200 °C over 90 min. MS detection was performed in electron impact mode (70 eV) scanning from 40 to 550 amu.

Methodology

Sample preparation involved n-hexane extraction of ground flowers or dilution of essential oils, spiked with a tridecane internal standard. Data were deconvoluted using AMDIS and imported into Mass Profiler Professional for peak alignment, normalization and filtering. From an initial pool of 2 560 entities, stepwise filters (presence flags, frequency threshold, coefficient of variation and ANOVA p<0.05) reduced the set to the 50 most discriminant markers. Unsupervised principal component analysis (PCA) assessed data quality and visual clustering, followed by supervised partial least squares discriminant analysis (PLS-DA) for model building.

Key results and discussion

The PLS-DA model achieved 100 % recognition and prediction accuracy in cross-validation, clearly separating German, Roman and Juhua chamomile. Application to commercial samples revealed that most solid products sold in the US aligned with German chamomile, while a subset of Chinese teas corresponded to Juhua. None of the solids were classified as Roman chamomile. Essential oils were correctly assigned regardless of extraction method, with confidence scores indicating high model robustness. A Venn diagram of marker sets highlighted minimal overlap among the three varieties and identified characteristic sesquiterpenes, esters and terpenoids.

Benefits and practical applications

• Objective authentication of chamomile species in raw materials and finished goods
• Rapid detection of adulteration or substitution
• Enhanced quality control workflows for herbal dietary supplements and cosmetics
• Automated data processing for high-throughput sample classification

Future trends and potential applications

• Extension of the chemometric GC/MS approach to other botanicals and multi-ingredient formulations
• Integration with orthogonal techniques such as LC/MS or NMR for deeper compositional profiling
• Development of portable GC/MS devices and cloud-based prediction models for on-site quality assurance
• Expansion of spectral libraries and machine learning algorithms to improve classification of complex matrices

Conclusion

The combination of untargeted GC/MS profiling and supervised chemometric modeling provides a powerful tool for distinguishing chamomile varieties with 100 % accuracy. This approach supports rigorous quality control, helps prevent adulteration and ensures consistency in commercial herbal products. Broad adoption of such workflows can raise industry standards and protect consumer safety.

References

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