Comprehensive Food Profiling Combining High Resolution LC/MS and GC/MS Analyses

Importance of the Topic

Food composition analysis is essential for evaluating nutritional value, ensuring safety, authenticating ingredients and understanding dietary impacts on health. A comprehensive untargeted metabolomics strategy provides deeper insight into the full complement of small molecules and lipids in diverse diets.

Study Objectives and Overview

This work applies high-resolution LC/MS and GC/MS platforms to compare three distinct diet types: fast food (FF), pesco-vegetarian (PV) and eastern vegetarian (EV). By integrating multiple chromatographic methods and ionization modes, the study aims to maximize coverage of polar metabolites, nonpolar compounds and complex lipids. Multivariate and statistical analyses identify metabolites that differentiate these dietary profiles.

Methodology and Instrumentation

Samples from each plate were homogenized, lyophilized and extracted with 80:20 methanol/water. Six technical replicates per technique were processed.

1. LC/MS Metabolomics: Agilent 1290 Infinity LC with 6230 TOF or 6550 iFunnel Q-TOF. Reversed-phase and aqueous-normal-phase separations on specialized columns. Positive and negative electrospray ionization.
2. LC/MS Lipidomics: 1290 LC coupled to 6550 Q-TOF with Jet Stream source under reversed-phase conditions in both polarities.
3. GC/MS Metabolomics: Agilent 7890B GC with 7200 Q-TOF. Derivatization using methoxyamine hydrochloride and MSTFA+TMCS. Electron ionization and chemical ionization spectra acquired.

Data processing used Agilent MassHunter Profinder for feature extraction, Mass Profiler Professional for differential analysis, METLIN, NIST14 and Fiehn Metabolomics libraries for annotation, SimLipid for lipids, and Molecular Structure Correlator for MS/MS-based confirmation when needed.

Main Results and Discussion

• Feature Coverage: Thousands of features were detected across GC/MS and LC/MS methods. Less than 8% overlap between positive and negative modes underlines the need for dual polarity acquisition.
• Reproducibility: Over 90% of LC/TOF features exhibited CV ≤30%, supporting robust statistical comparisons.
• Dietary Differentiation: PCA and correlation analyses showed clear clustering by diet. Venn diagrams revealed the PV diet had the largest number of unique metabolites.
• Compound Classes: GC/Q-TOF identified antioxidants such as sinapinic and gallic acids, sugars, amino acids and pyrolysis products in FF. LC/MS metabolomics annotated flavonoids, phenolic acids and other polar metabolites. Lipidomics detailed class distributions, free fatty acid profiles and saturation levels, with PV enriched in polyunsaturated fatty acids (DHA, EPA).
• Statistical Significance: Volcano plots and ANOVA detected over 1,000 features significantly different between diets (P ≤0.05), many showing greater than fivefold changes.

Benefits and Practical Applications

• Broad Coverage: Combined LC/MS and GC/MS untargeted workflows capture a wide range of small molecules and lipids in complex food matrices.
• Food Quality and Authentication: Detailed metabolite profiles support nutritional assessment, origin authentication and detection of processing markers.
• Regulatory Compliance and QC: Highly reproducible assays facilitate routine quality control and comparative studies across food products.
• Flexible Data Reuse: Comprehensive spectral libraries and data archives enable targeted follow-up studies and retrospective mining of archived datasets.

Future Trends and Potential Applications

• Flux and Stable Isotope Studies: Integrating isotope labeling to trace metabolic pathways in food processing and digestion.
• Machine Learning Integration: Building predictive models for biomarker discovery and dietary health impact assessment.
• Expanded Spectral Libraries: Developing in silico and experimental libraries for improved compound identification.
• Ambient and Real-Time MS: Applying rapid ambient MS methods for on-site food quality monitoring and authentication.

Conclusion

Combining high-resolution LC/MS and GC/MS with advanced data analysis enables comprehensive profiling of diverse food diets. This approach distinguishes diet-specific metabolite signatures and supports applications in nutrition research, food quality control and authentication.

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