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Addressing Complex Matrix Interference Improves Multiplex Food Allergen Detection by Targeted LC-MS/MS.

Derek Croote1, Ido Braslavsky1,2, Stephen R Quake1,3,4

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Precautionary food allergen labeling (PAL) can be confusing. A new software, MADIC, uses mass spectrometry to detect 14 allergens in foods, revealing contamination patterns and improving food safety for allergic individuals.

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Allergen Detection

Background:

  • Precautionary allergen labeling (PAL) like "may contain" is frequently used but can be confusing for allergic consumers.
  • Targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) with scheduled multiple reaction monitoring (MRM) is a potential method for allergen detection.
  • The effectiveness of a single LC-MS/MS method across diverse food matrices is uncertain due to potential interferences.

Purpose of the Study:

  • To assess the applicability of LC-MS/MS for detecting multiple allergens in various food products.
  • To develop and apply a software tool for correcting matrix-dependent interferences in allergen quantification.
  • To investigate patterns of allergen contamination in commercial food products.

Main Methods:

  • Development of an open-source software package, MAtrix-Dependent Interference Correction (MADIC), for identifying and correcting matrix interferences.
  • Application of a targeted LC-MS/MS method analyzing 14 common allergens.
  • Analysis of 84 unique commercial food products, including those with and without precautionary labeling.

Main Results:

  • The MADIC software successfully identified matrix interferences, enabling sensitive detection of allergens.
  • Common allergen contamination patterns were observed, such as wheat in grains and milk in chocolate products.
  • Instances of contamination were found in products regardless of precautionary labeling, and variable soy content was noted even in products with only soy derivatives.

Conclusions:

  • LC-MS/MS, when combined with matrix interference correction, is a feasible approach for sensitive, multi-allergen detection in a wide range of food products.
  • The study highlights the prevalence of undeclared allergens and the challenges posed by complex food matrices.
  • This approach can improve the accuracy of allergen testing and enhance food safety for individuals with food allergies.