Untargeted 4D Lipidomics combined with Chemometrics, as a reliable tool for the classification of pork meat cuts

Significance of the Topic

Lipid profiling in pork meat is critical for ensuring product authenticity, quality control, and nutritional assessment. Variations in animal breed, feeding practices and muscle type can markedly influence lipid composition, affecting both consumer health and industry standards.

Objectives and Study Overview

This study aims to apply an untargeted four-dimensional lipidomics workflow combined with advanced chemometric analysis to classify pork meat cuts. Three muscle tissues (belly, thigh, shoulder) were compared to establish lipid-based markers for accurate segment differentiation and authenticity verification.

Methodology and Instrumentation

• Sample Preparation: 100 mg lyophilized pork tissue extracted using methyl tert-butyl ether:methanol (3:1 v/v) and water, twofold centrifugation at 10 000 rpm.
• Chromatography & MS: Reverse-phase UPLC with Thermo Acclaim RSLC 120 C18 column; Bruker timsTOF Pro in positive electrospray mode using Parallel Accumulation Serial Fragmentation (PASEF).
• Data Processing: Feature detection in MetaboScape 2023, annotation via internal lipid library and LipidBlast, Kendrick mass defect filtering.
• Chemometrics: Hierarchical clustering (HCA) for sample grouping and orthogonal partial least squares discriminant analysis (OPLS-DA) for biomarker extraction.

Instrumentation Used

• Thermo Acclaim RSLC 120 C18 column, 2.2 μm, 2.1 × 100 mm
• Bruker timsTOF Pro mass spectrometer with PASEF technology
• MetaboScape 2023 software and LipidBlast spectral library

Key Results and Discussion

Over 2000 mass features yielded 170 annotated lipids across four major classes (phospholipids, triglycerides, diglycerides) and seven subclasses. HCA segregated samples into two clusters (belly vs thigh/shoulder), while OPLS-DA models achieved ~97% correct classification. Approximately 70 lipids with VIP scores >1.0, including TAG 46:3, TAG 54:4 and PC-O 34:2, were identified as discriminant markers. Kendrick mass plots enhanced false positive removal.

Benefits and Practical Applications

• Reliable authentication of meat cuts to prevent fraud.
• Quality control in meat processing and supply chain traceability.
• Nutritional profiling to support dietary recommendations and product labeling.

Future Trends and Opportunities

• Integration with targeted lipid assays for high-throughput screening.
• Application of machine learning algorithms for automated pattern recognition.
• Expansion to diverse livestock species and processed meat products.
• Development of portable MS platforms for on-site authenticity testing.

Conclusion

The combination of untargeted 4D lipidomics and robust chemometrics presents a powerful protocol for the classification and authentication of pork meat cuts. Identified lipid biomarkers and high classification accuracy underscore its potential for routine implementation in food quality and safety laboratories.