Wide-Target Analysis of Yogurt Using Triple Quadrupole LC-MS/MS

Wide-Target Analysis of Yogurt Using Triple Quadrupole LC-MS/MS

Significance of the Topic

Food metabolomics offers detailed insight into the flavor, quality, and functionality of dairy products. Targeted analysis of primary metabolites in yogurt is vital for process optimization, quality control, and nutritional evaluation.

Objectives and Overview of the Study

This work aimed to develop a comprehensive workflow for profiling primary metabolites in commercially available yogurts. Two complementary LC-MS/MS strategies—ion-pairing and non-ion-pairing—were applied and compared to maximize coverage of hydrophilic metabolites.

Methodology and Instrumentation

Sample Preparation:

• Freeze-drying of yogurt, extraction with ultrapure water:methanol:chloroform (1:2.5:1).
• Vortex mixing, shaking (1200 rpm, 30 min, 37 °C), and centrifugation steps.
• Ultrafiltration (3 kDa cutoff), concentration, and reconstitution in 10 µM MES internal standard.

Instrumentation:

• UHPLC: Shimadzu Nexera X3.
• MS: Shimadzu LCMS-8060NX triple quadrupole with ESI in positive/negative MRM modes.
• Columns: Mastro2 C18 with dipentylamine/acetate ion-pairing and Shim-pack GIST PFPP for non-ion-pairing.
• Mobile phases: 10 mM dipentylamine/15 mM acetate or 0.1% formate with methanol/acetonitrile.

Main Results and Discussion

• Ion-pairing LC-MS/MS detected 68–73 of 112 targeted compounds, enriching glycolytic and pentose phosphate metabolites.
• Non-ion-pairing LC-MS/MS identified 81–88 of 141 targets, emphasizing amino acids, organic acids, and nucleotides.
• Combined analysis yielded over 110 unique primary metabolites across eight yogurt samples.
• PCA using Multi-omics Analysis Package clearly distinguished yogurt varieties based on metabolite profiles.
• Key PC1 positive loadings: nucleic acid derivatives (UDP-glucose, cytidine, TDP), pentose phosphate, urea cycle; negative loadings: phenyllactic acid, succinic acid, cystathionine, methionine and derivatives.
• PC2 differentiation driven by amino acids (tyrosine, glutamic acid, arginine) and cofactors (FAD), with opposing loadings for simple amino acids and sugar phosphates.

Benefits and Practical Applications

This workflow enables high-throughput, high-coverage profiling of yogurt metabolites, supporting quality assurance, batch comparison, and authentication in dairy research and industrial settings.

Future Trends and Applications

Future directions include integration with genomics and proteomics data, advanced bioinformatics for real-time monitoring, expanded target lists, and automation for personalized nutrition and quality assessment.

Conclusion

The combination of ion-pairing and non-ion-pairing LC-MS/MS methods, together with specialized software tools, offers a robust strategy for comprehensive primary metabolite analysis in yogurt, revealing distinct profiles among commercial products.