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 to screen for peptides and searched against the NCBI database to identify peptides specific to each 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, which were validated for label–free quantification. Calibration curves were constructed with good linearity and correlation coefficient (r2) greater than 0.99. The limits of detection (LODs) were 0.11 µg/g–0.31 µg/g, with recoveries in incurred bakery food matrices from 72.5–92.1% and 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.
Objective: This study was aimed to establish a quantitative detection method for meat contamination based on specific polypeptides. Methods: Thermally stable peptides with good responses were screened by high resolution liquid chromatography tandem mass spectrometry. Standard curves of specific polypeptide were established by triple quadrupole mass spectrometry. Finally, the adulteration of commercial samples was detected according to the standard curve. Results: Fifteen thermally stable peptides with good responses were screened. The selected specific peptides can be detected stably in raw meat and deep processed meat with the detection limit up to 1%, and have a good linear relationship with the corresponding muscle composition. Conclusion: Therefore, this method can be effectively used for quantitative analysis of commercial samples.
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