Simultaneous analysis of multiple food allergen and its detection from processed food
Posters | 2020 | ShimadzuInstrumentation
Food allergies pose a growing public health challenge, affecting up to 10% of the population. Accurate detection of allergenic proteins in both raw and processed foods is essential to protect sensitive consumers. While ELISA is commonly used, liquid chromatography–tandem mass spectrometry (LC-MS/MS) offers superior selectivity, sensitivity, and the ability to monitor multiple allergens simultaneously, overcoming limitations such as false positives in heat-treated products.
This study aimed to develop and validate an LC-MS/MS method capable of simultaneous quantitation of 13 major allergenic food ingredients mandated for labeling by regulatory agencies. The target analytes include milk, egg, fish, crustaceans, tree nuts, peanuts, wheat, and soy. The method was applied to both raw materials and a variety of thermally processed foods to assess its robustness and practical applicability.
Sample Preparation and Peptide Generation:
LC-MS/MS Conditions:
Instrumentation Used:
Method Development:
Quantitation:
Processed Food Analysis:
The developed LC-MS/MS approach enables comprehensive screening of multiple allergens in complex food matrices with high confidence. It addresses known ELISA shortcomings by reducing cross-reactivity and false positives, particularly in heat-treated products. This method supports quality control in food manufacturing and regulatory compliance through reliable, multiplexed detection.
Continued advancement in high-resolution mass spectrometry, isotopically labeled standards, and automated sample preparation will further enhance sensitivity and throughput. Expansion to additional allergen classes and integration with digital traceability platforms can strengthen supply chain transparency and consumer safety. Collaborative efforts in standard reference materials and interlaboratory studies will support broader adoption.
A robust LC-MS/MS method was established for simultaneous detection and quantitation of 13 major food allergens. The workflow demonstrates excellent linearity, specificity, and applicability to processed foods, offering a valuable tool for food safety laboratories and manufacturers.
No external references were provided in the source document.
LC/MS, LC/MS/MS, LC/QQQ
IndustriesFood & Agriculture
ManufacturerShimadzu
Summary
Significance of the Topic
Food allergies pose a growing public health challenge, affecting up to 10% of the population. Accurate detection of allergenic proteins in both raw and processed foods is essential to protect sensitive consumers. While ELISA is commonly used, liquid chromatography–tandem mass spectrometry (LC-MS/MS) offers superior selectivity, sensitivity, and the ability to monitor multiple allergens simultaneously, overcoming limitations such as false positives in heat-treated products.
Objectives and Study Overview
This study aimed to develop and validate an LC-MS/MS method capable of simultaneous quantitation of 13 major allergenic food ingredients mandated for labeling by regulatory agencies. The target analytes include milk, egg, fish, crustaceans, tree nuts, peanuts, wheat, and soy. The method was applied to both raw materials and a variety of thermally processed foods to assess its robustness and practical applicability.
Methodology and Instrumentation
Sample Preparation and Peptide Generation:
- Commercial allergen sources were freeze-ground and defatted with hexane.
- Proteins were extracted in Tris-HCl/urea buffer, reduced, alkylated, and digested to peptides.
- Peptide mixtures were desalted by solid-phase extraction and lyophilized.
LC-MS/MS Conditions:
- UHPLC: Shimadzu Nexera X2 with Shim-pack XR-ODS III column (2.0 mm × 75 mm, 1.6 µm).
- Mobile phase: 0.1% formic acid in water (A) and acetonitrile (B), gradient from 2% to 95% B over 8 min.
- Detection: Multiple reaction monitoring (MRM) in positive‐ion mode.
Instrumentation Used:
- UHPLC system: Shimadzu Nexera X2™.
- Mass spectrometer: Shimadzu LCMS-8050 triple quadrupole.
Main Results and Discussion
Method Development:
- Peptide candidates selected for each allergen after in silico filtering and experimental confirmation across related species.
- 245 transitions optimized for the 13 food materials, with collision energies tuned for each MRM channel.
Quantitation:
- Calibration curves constructed using light/heavy peptide ratios showed linear ranges from 3.9 to 250 mg/kg with R² > 0.995.
- Reference materials improved traceability by accounting for extraction and digestion efficiencies.
Processed Food Analysis:
- Signature peptides were successfully detected in thermally processed matrices including bread, cookies, fried fish, and frozen pasta.
- All 13 allergenic ingredients were monitored, confirming method applicability under various processing conditions.
Benefits and Practical Applications
The developed LC-MS/MS approach enables comprehensive screening of multiple allergens in complex food matrices with high confidence. It addresses known ELISA shortcomings by reducing cross-reactivity and false positives, particularly in heat-treated products. This method supports quality control in food manufacturing and regulatory compliance through reliable, multiplexed detection.
Future Trends and Opportunities
Continued advancement in high-resolution mass spectrometry, isotopically labeled standards, and automated sample preparation will further enhance sensitivity and throughput. Expansion to additional allergen classes and integration with digital traceability platforms can strengthen supply chain transparency and consumer safety. Collaborative efforts in standard reference materials and interlaboratory studies will support broader adoption.
Conclusion
A robust LC-MS/MS method was established for simultaneous detection and quantitation of 13 major food allergens. The workflow demonstrates excellent linearity, specificity, and applicability to processed foods, offering a valuable tool for food safety laboratories and manufacturers.
References
No external references were provided in the source document.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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