Addressing complex matrix interference improves multiplex food allergen detection by targeted LC-MS/MS

Derek Croote, Ido Braslavsky, Stephen R. Quake*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The frequent use of precautionary food allergen labeling (PAL) such as "may contain" frustrates allergic individuals who rely on such labeling to determine whether a food is safe to consume. One technique to study whether foods contain allergens is targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) employing scheduled multiple reaction monitoring (MRM). However, the applicability of a single MRM method to many commercial foods is unknown as complex and heterogeneous interferences derived from the unique composition of each food matrix can hinder quantification of trace amounts of allergen contamination. We developed a freely available, open source software package MAtrix-Dependent Interference Correction (MADIC) to identify interference and applied it with a method targeting 14 allergens. Among 84 unique food products, we found patterns of allergen contamination such as wheat in grains, milk in chocolate-containing products, and soy in breads and corn flours. We also found additional instances of contamination in products with and without PAL as well as highly variable soy content in foods containing only soybean oil and/or soy lecithin. These results demonstrate the feasibility of applying LC-MS/MS to a variety of food products with sensitive detection of multiple allergens in spite of variable matrix interference.

Original languageAmerican English
Pages (from-to)9760-9769
Number of pages10
JournalAnalytical Chemistry
Volume91
Issue number15
DOIs
StatePublished - 6 Aug 2019

Bibliographical note

Funding Information:
We would like to acknowledge the staff at the Vincent Coates Foundation Mass Spectrometry Laboratory at Stanford University (SUMS): Karolina Krasinska, Allis Chien, and Theresa McLaughlin for helpful discussions related to quantitative method development and instrument performance, as well as Christopher Adams and Ryan Leib for helpful discussions related to shotgun proteomics and data analysis. This research was supported by the Simons Foundation (SFLIFE #288992 to S.R.Q.), a SUMS seed grant, and the Chan Zuckerberg Biohub. D.C. is supported by an NSF Graduate Research Fellowship and the Kou-I Yeh Stanford Graduate Fellowship. I.B. acknowledges support from Stanford University and from The Hebrew University of Jerusalem.

Publisher Copyright:
© 2019 American Chemical Society.

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