From smartphone lateral flow immunoassay screening to direct ms analysis: Development and validation of a semi-quantitative direct analysis in real-time mass spectrometric (dart-ms) approach to the analysis of deoxynivalenol

Ariadni Geballa-Koukoula, Arjen Gerssen, Michel W. F. Nielen

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In current food safety monitoring, lateral flow immunoassays (LFIAs) are widely used for rapid food contaminant screening. Recent advances include smartphone readouts, offering semi-quantitative analysis of LFIAs with time, location, and data transfer in case of on-site testing. Following the screening, the next step in the EU regulations is confirmation by, e.g., liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this work, using direct analysis in real time ambient ionization and triple quadrupole MS/MS (DART-QqQ-MS/MS), we achieved rapid confirmation of the identity of the substance(s) causing the LFIA result. In the workflow proposed, an individual performs the (on-site) smartphone LFIA screening, and when the result is suspect, an identification LFIA (ID-LFIA) strip is developed with the same sample extract. The ID-LFIA can be dissociated and rapidly analyzed in a control laboratory with DART-QqQ-MS/MS. The ID-LFIA consists of multiple lines of monoclonal antibodies against the mycotoxin deoxynivalenol, acting as a bioaffinity trap. The ID-LFIA/DART-QqQ-MS/MS approach has been developed and validated, along with the screening smartphone LFIA, and has demonstrated its applicability by analyzing incurred and spiked samples. The developed approach has been critically compared with our previous direct electrospray ionization MS method and was found to provide highly complementary information on the total deoxynivalenol contamination in the sample.
Original languageEnglish
Article number1861
Pages (from-to)1-17
JournalSensors
Volume21
Issue number5
DOIs
Publication statusPublished - 1 Mar 2021
Externally publishedYes

Funding

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 720325.

FundersFunder number
Horizon 2020 Framework Programme720325

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