LC/Q-TOF Marker Identification to TQ LC/MS Targeted Quantitation

Importance of the Topic

The prevalence of peanut allergy worldwide poses severe health risks and mandates reliable detection methods to prevent accidental exposure. Conventional ELISA assays, while widely used, suffer from variable accuracy due to differences in antibodies, processing effects, and matrix interference. Advanced mass spectrometry workflows promise improved sensitivity, specificity, and robustness for allergen quantitation in food matrices.

Objectives and Scope of the Study

This study aimed to develop and validate a sensitive, robust, and accurate workflow combining LC/Q-TOF marker identification with triple quadrupole LC/MS (TQ LC/MS) targeted quantitation. The goal was to quantify trace levels of peanut allergens in raw and baked wheat-flour-based matrices with better performance than commercial ELISA kits.

Methodology and Instrumentation

Sample Preparation and Digestion

• Raw and cooked wheat flour, wheat flour–oil, and wheat flour–oil–salt matrices spiked with known peanut levels (80–0.01 g/kg) were prepared.
• Proteins were extracted in Tris buffer, reduced, alkylated, and digested with trypsin.
• Excess matrix proteins were removed by selective precipitation and solid-phase cleanup.

Instrumental Workflow

• LC/Q-TOF Screening: Agilent 1290 Infinity LC with AdvanceBio Peptide Mapping column coupled to Agilent 6545 Q-TOF using Auto MS/MS to identify candidate peanut peptides.
• Marker Selection: Eleven peptides from major peanut allergens (Ara h 1, h 2, h 3, h 6, h 7) were chosen based on abundance and absence in blanks.
• TQ LC/MS Quantitation: Agilent 1290 Infinity LC coupled to Agilent 6470 triple quadrupole MS with optimized MRM transitions generated by MassHunter Optimizer for Peptides and synthesized isotopically labeled internal standards.

Main Results and Discussion

Performance Characteristics

• Linearity: 0.31–40 mg/kg total peanut, R² > 0.99, surpassing ELISA dynamic ranges.
• Sensitivity: LOQ of 0.31 mg/kg, lower than most commercial kits (1–2.5 mg/kg).
• Recovery: 85–115% across all matrices at spiking levels of 0.5, 2, and 10 mg/kg.
• Accuracy: Measured the target 10 mg/kg spike within 10–20% of true value; matrix effects noted in complex blends.
• Precision: Intra-batch RSDs <15%, inter-batch RSDs <10% in most matrices.

The workflow demonstrated superior sensitivity and reproducibility compared to ELISA, and the integrated LC/Q-TOF to TQ LC/MS strategy streamlined marker discovery and targeted method development.

Benefits and Practical Applications

• Enhanced sensitivity and specificity for peanut allergen quantitation in complex food matrices
• Wide dynamic range and low LOQ for regulatory compliance and risk assessment
• Robust method applicable to both raw and processed samples
• Streamlined marker selection and MRM optimization accelerates method development

Future Trends and Potential Applications

• Extension of the workflow to other food allergens and multi-allergen screening
• Integration with automated sample preparation and high-throughput platforms
• Use of high-resolution mass spectrometry data for retrospective screening and method refinement
• Development of standardized MS-based reference methods for regulatory enforcement

Conclusion

The combined LC/Q-TOF marker identification and TQ LC/MS targeted quantitation workflow offers a powerful approach for accurate, sensitive, and reproducible detection of peanut allergens in wheat flour matrices. It outperforms conventional ELISA assays in sensitivity and precision, providing a reliable tool for the food industry and regulatory laboratories.

References

1. Poms RE; Methods for Allergen Analysis in Food: A Review. Food Addit Contam. 2004, 21(1), 1–31.
2. Fu TJ; Impact of Thermal Processing on ELISA Detection of Peanut Allergens. J Agric Food Chem. 2013, 61(24), 5649–5658.
3. Koch P; Comparison of Commercially Available ELISA Kits with Human Sera-Based Detection Methods for Peanut Allergens in Foods. Food Addit Contam. 2003, 20(9), 797–803.
4. Poms RE; Inter-Laboratory Validation Study of Five Commercial ELISA Test Kits for Determination of Peanut Proteins in Biscuits and Dark Chocolate. Food Addit Contam. 2005, 22(2), 104–112.
5. Jayasena S; Comparison of Six Commercial ELISA Kits for Their Specificity and Sensitivity in Detecting Major Peanut Allergens. J Agric Food Chem. 2015, 63(6), 1849–1855.
6. Bignardi C; Particle-Packed Column vs Silica-Based Monolithic Column for LC-ESI-Ion Trap-MS/MS Multiallergen Analysis. J Chromatogr A. 2010, 1217(48), 7579–7585.
7. Heick J; Application of LC-MS/MS for Simultaneous Detection of Seven Allergenic Foods in Flour and Bread and Comparison with ELISA Kits. J AOAC Int. 2011, 94(4), 1060–1068.
8. Heick J; First Screening Method for Simultaneous Detection of Seven Allergens by LC-MS. J Chromatogr A. 2011, 1218(7), 938–943.
9. Monaci L; High-Resolution Orbitrap-MS for Rapid Detection of Peanuts in Nuts. Food Addit Contam A. 2015, 32(10), 1607–1616.
10. Planque M; Advances in UHPLC-MS/MS for Sensitive Detection of Food Allergens in Processed Foods. J Chromatogr A. 2016, 1464, 115–123.
11. Pilolli R; In-House Validation of Orbitrap-Based MS Method for Multiple Allergen Detection in a Processed Model Food. Anal Bioanal Chem. 2018, 410(22), 5653–5662.
12. Parker CH; Multi-Allergen Quantitation and Thermal Treatment Impact in Baked Goods by ELISA and LC-MS/MS. J Agric Food Chem. 2015, 63(49), 10669–10680.
13. Boo CC; Targeted LC-MS/MS for Simultaneous Detection of Egg, Milk, and Peanut Allergens in Sugar Cookies. J AOAC Int. 2018, 101(1), 108–117.
14. Daly M; Assessing Almond and Peanut Allergens Using ELISA Kits and LC-MS/MS: A Case Study. J AOAC Int. 2018, 101(1), 96–101.
15. New LS; Simultaneous Analysis of Multiple Allergens in Foods by LC-MS/MS. J AOAC Int. 2018, 101(1), 132–145.
16. Sayers RL; Microfluidic Separation Coupled to MS for Quantification of Peanut Allergens in Complex Matrices. J Proteome Res. 2018, 17(1), 647–655.
17. Chang Y; Simplified, Sensitive, and Accurate LC-MS/MS Method for Peanut Quantification in Wheat Flour Matrices. Food Addit Contam A. 2021, 38(8), 1260–1272.
18. Shefcheck KJ; Confirmation of Peanut Protein Markers in Dark Chocolate by LC-MS/MS. J Agric Food Chem. 2006, 54(21), 7953–7959.
19. New LS; Detection and Quantitation of Selected Food Allergens by LC-MS/MS: First Action 2017. J AOAC Int. 2020, 103(2), 570–583.
20. Sayers RL; Effect of Thermal Processing on Peanut Allergen Peptide Targets in MRM MS Experiments. Analyst. 2016, 141(13), 4130–4141.
21. Sealey-Voyksner J; Discovery of Highly Conserved Unique Peanut and Tree Nut Peptides by LC-MS/MS for Multi-Allergen Detection. Food Chem. 2016, 194, 201–211.
22. Careri M; Peptide Biomarkers for Quantitative Confirmation of Hidden Allergenic Peanut Proteins Ara h 2 and Ara h 3/4 by LC-MS/MS. Anal Bioanal Chem. 2007, 389(6), 1901–1907.
23. Pedreschi R; Challenges in Detecting Food Allergens by Shotgun and Targeted Proteomics: Traces of Peanut in Baked Cookies. Nutrients. 2012, 4(2), 132–150.
24. Shefcheck KJ; Confirmation of Peanut Protein Ara h 1 in Model Food Matrix by LC/MS/MS. J Agric Food Chem. 2004, 52(10), 2785–2790.
25. Faeste CK; LC and MS in Food Allergen Detection. J Food Prot. 2011, 74(2), 316–345.
26. Commission Decision 2002/657/EC of 12 August 2002 implementing Council Directive 96/23/EC on analytical method performance and result interpretation.