| Title |
MRM3-based LC-MS multi-method for the detection and quantification of nut allergens
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, September 2016
|
| DOI | 10.1007/s00216-016-9888-y |
| Pubmed ID | |
| Authors |
Robin Korte, Jens Brockmeyer |
| Abstract |
Food allergies have become a global challenge to food safety in industrialized countries in recent years. With governmental monitoring and legislation moving towards the establishment of threshold allergen doses, there is a need for sensitive and quantitative analytical methods for the determination of allergenic food contaminants. Targeted proteomics employing liquid chromatography-mass spectrometry (LC-MS) has emerged as a promising technique that offers increased specificity and reproducibility compared to antibody and DNA-based technologies. As the detection of trace levels of allergenic food contaminants also demands excellent sensitivity, we aimed to significantly increase the analytical performance of LC-MS by utilizing multiple reaction monitoring cubed (MRM(3)) technology. Following a bottom-up proteomics approach, including a straightforward sample preparation process, 38 MRM(3) experiments specific to 18 proteotypic peptides were developed and optimized. This permitted the highly specific identification of peanut, almond, cashew, hazelnut, pistachio, and walnut. The analytical performance of the method was assessed for three relevant food matrices with different chemical compositions. Limits of detection were around 1 μg/g or below in fortified matrix samples, not accounting for the effects of food processing. Compared to an MRM-based approach, the MRM(3)-based method showed an increase in sensitivity of up to 30-fold. Regression analysis demonstrated high linearity of the MRM(3) signal in spiked matrix samples together with robust intersample reproducibility, confirming that the method is highly applicable for quantitative purposes. To the best of our knowledge, we describe here the most sensitive LC-MS multi-method for food allergen detection thus far. In addition, this is the first study that systematically compares MRM(3) with MRM for the analysis of complex foods. Graphical abstract Allergen detection by MRM(3). |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 24% |
| Student > Ph. D. Student | 12 | 19% |
| Other | 5 | 8% |
| Student > Bachelor | 5 | 8% |
| Student > Master | 4 | 6% |
| Other | 8 | 13% |
| Unknown | 13 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 18% |
| Chemistry | 8 | 13% |
| Medicine and Dentistry | 6 | 10% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Other | 12 | 19% |
| Unknown | 19 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 January 2023.
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#8,261,756
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Outputs from Analytical & Bioanalytical Chemistry
#1,975
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Outputs of similar age
#120,420
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Outputs of similar age from Analytical & Bioanalytical Chemistry
#28
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