Food allergies, affecting up to 10% of children globally, have become a significant health concern, with immediate and delayed reactions classified into IgE-mediated and non-IgE-mediated categories. Treatment primarily involves lifelong avoidance of allergenic foods, necessitating accurate food labeling. Legislation in various regions mandates allergen labelling, but challenges persist due to cross-contamination and discrepancies in precautionary labelling. The Voluntary Incidental Trace Allergen Labelling (VITAL) system aims to address this by providing a risk-based approach. Bakery products, increasingly popular worldwide, often contain allergenic ingredients, prompting the need for accurate detection methods. Currently, protein-based and DNA-based methods are commonly used but face limitations. Mass spectrometry (MS) emerges as a promising solution, allowing multiplex allergen detection with high sensitivity and accuracy. Various MS-based approaches exist, including top-down and bottom-up strategies, with the latter gaining traction due to its suitability for complex food matrices. Advanced MS proteomic technologies focus on accurate quantitation, with strategies such as absolute quantification (AQUA) and label-free quantification (LFQ) being employed. This work aims to develop a novel analytical method for detecting allergens in bakery products, particularly nuts, with high sensitivity and to establish label-free quantification methods for nut-derived ingredients.
How was PEAKS used?
In bottom–up based approaches, the selection of proper tryptic digest peptide markers that uniquely represent each nut protein is of great importance for mass spectrometric detection of allergens in bakery foods. Raw data representing almond, cashew, peanut and walnut were submitted to the PEAKS Xpro software. After data refinement, the data were subjected to de–novo sequencing combining database searching. Afterwards, 155, 80, 306, 177 peptides from almond, cashew, peanut and walnut were identified by software. Based on the –10lgP value, 120 peptides (top 30 of each nut species) were chosen as potential peptides. The interspecies homology of these peptides was also investigated by comparing sequences with the NCBI protein database for plants, in order to rule out those sequences shared by other common plant–based and animal–based ingredients used in bakery, such as wheat, rice, corn, barley, soy, egg, milk, butter and yeast.
Xu, Daokun, et al. “Comprehensive characterization and detection of nut allergens in bakery foods using Q–TOF mass spectrometry and bioinformatics.” Food Quality and Safety (2023): fyad061. https://doi.org/10.1093/fqsafe/fyad061
Food allergy is a growing health issue worldwide and the demand for sensitive, robust and high-throughput analytical methods is rising. In recent years, mass spectrometry-based methods have been established for multiple food allergen detection. In the present study, a novel method was developed for the simultaneous detection of almond, cashew, peanut, and walnut allergens in bakery foods using liquid chromatography–mass spectrometry. Proteins unique to these four ingredients were extracted, followed by trypsin digestion, quadrupole time-of-flight (Q-TOF) mass spectrometry and bioinformatics analysis. The raw data were processed by de–novo sequencing module plus PEAKS DB (database search) module of the PEAKS software to screen peptides specific to each nut species. The thermal stability and uniqueness of these candidate peptides were further verified using triple quadrupole mass spectrometry (QQQ-MS) in multiple reaction monitoring (MRM) mode. Each nut species was represented by four peptides, all of which were validated for label-free quantification (LFQ). Calibration curves were constructed with good linearity and correlation coefficient (r2) greater than 0.99. The limits of detection (LODs) were determined to range from 0.11 to 0.31 mg/kg, and were compared with the reference doses proposed by Voluntary Incidental Trace Allergen Labelling (VITAL). The recoveries of the developed method in incurred bakery food matrices ranged from 72.5% to 92.1% with relative standard deviations (RSD) of <5.2%. The detection of undeclared allergens in commercial bakery food samples confirmed the presence of these allergens. In conclusion, this method provides insight into the qualitative and quantitative detection of trace levels of nut allergens in bakery foods.