Multi-Allergen Detection: Determining Unique Nut and Tree Nut Peptide Markers Using Accurate-Mass Q-TOF LC/MS

Importance of the topic

Hidden nut allergens pose serious health risks for allergic individuals. Their stability to heat and digestion and potential cross-contamination during food processing create challenges in detection. A reliable assay capable of identifying and quantifying multiple nut and tree nut allergens at trace levels in a single analysis is essential for food safety and accurate labeling.

Objectives and Overview

This study aimed to develop a unified LC-MS/MS method for detecting 11 tree nuts and peanuts in food products. Proteins from raw and roasted nuts were digested with trypsin, and unique peptide markers were identified through accurate-mass quadrupole time-of-flight liquid chromatography mass spectrometry (Q-TOF LC/MS) and database searches. The goal was to select peptides conserved across processing conditions and exclusive to each nut.

Methodology

Samples of raw and oven-roasted almonds, pecans, cashews, walnuts, hazelnuts, pine nuts, brazil nuts, macadamias, pistachios, chestnuts, coconuts, and peanuts were extracted in Tris-HCl buffer. Extracts were analyzed intact by LC/MS to assess protein changes upon roasting. Digested extracts were processed by trypsin for peptide mapping. Data analysis employed Spectrum Mill searches against an NCBI nonredundant green plant database to verify peptide identity and uniqueness.

Used Instrumentation

• Agilent 1290 Infinity high-performance liquid chromatograph
• Poroshell C18 columns for protein and peptide separation
• Agilent 6530 Accurate-Mass Q-TOF mass spectrometer with Jet Stream electrospray source
• MassHunter Workstation and Spectrum Mill software for data acquisition and peptide identification

Main Results and Discussion

Peptide markers displayed consistent presence and intensity in raw and roasted nuts despite protein modifications from the Maillard reaction. Two peptides per tree nut and four for peanuts met criteria of high abundance, sequence length (6–20 residues), and processing stability. No interferences were observed when screening digests of other nuts or grains such as barley, corn, rice, quinoa, soy, and wheat. Application to commercial foods confirmed specificity: products labeled with nuts showed correct markers, while blanks and unrelated foods yielded no false positives. However, items processed on shared equipment occasionally revealed cross-contamination signatures.

Benefits and Practical Applications

The developed LC-MS/MS assay offers high specificity and sensitivity for multi-allergen detection at sub-ppm levels in a single run. It overcomes limitations of ELISA and PCR techniques, reducing false positives from cross-reactivity and negatives from processing-induced protein changes. This method enhances quality control in food manufacturing, allergen risk assessment, and regulatory compliance.

Future Trends and Opportunities

Advancements may include quantitative workflows using isotopically labeled peptide standards, expansion to other allergenic proteins, and integration with high-throughput platforms. Coupling with targeted data-independent acquisition (DIA) techniques could further improve multiplexing and sensitivity. Adoption in routine food analysis labs and certification bodies will strengthen allergen management.

Conclusion

A comprehensive Q-TOF LC/MS-based approach was established to detect 11 tree nut species and peanuts in diverse food matrices. By focusing on unique, conserved peptide markers, the assay achieves robust performance across raw, roasted, and processed samples. This single-assay solution addresses critical gaps in multi-allergen detection and supports safer food labeling practices.

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