Comprehensive characterization and detection of nut allergens in bakery foods using Q–TOF mass spectrometry and bioinformatics

Abstract 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 establishing its role in 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. Protein unique to theses four ingredients were extracted, followed by trypsin digestion, quadrupole time–of–flight (Q–TOF) mass spectrometry and bioinformatics analysis. 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. Thermal stability and uniqueness of these candidate peptides were further verified using triple quadrupole mass spectrometry (QQQ–MS) under 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 mg/kg 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 less than 5.2%. Commercial bakery food samples detection confirmed existence of undeclared allergens. In conclusion, this method shed light on the field of qualitative and quantitative detection of trace levels of nut allergens in bakery foods.