High resolution Mass Spectrometry for multi-allergen detection in processed foods: targeted vs untargeted approach

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Abstract

Mass spectrometry (MS) has played a pivotal role in proteomic research being the election method for protein identification in complex mixtures. In the last decade, the MS-based proteomic approaches have demonstrated to be a valuable confirmatory tool for allergen contamination management [1]. Our research group, actively contributed to such field developing label-free quantitative methods for the multiple allergen detection in several food matrices based on both low and high-resolution mass spectrometers [2-8]. Thanks to the advances provided by last-generation high resolution mass spectrometers (HR-MS) based on OrbitrapTM technology very high selectivity and sensitivity were achieved by the developed methods.In the present communication, performance provided by a hybrid quadrupole-OrbitrapTM MS platform will be presented. In particular, different acquisition modes were compared: Full-MS acquisition, targeted-Selected Ion Monitoring with data-dependent fragmentation (t-SIM/dd2) and Parallel Reaction Monitoring (PRM). The different acquisition modes were tested towards the detection of specific peptide markers arising from five different allergenic ingredients (milk, egg, soy, hazelnut, peanut) in home-made incurred cookies, selected as model processed matrix. In order to challenge the HR-MS platform, the sample pretreatment was kept as simply as possible, limited to a 30 min protein extraction followed by quick purification based on size exclusion chromatography by disposable cartridges. The three acquisition modes were independently optimized and compared in term of sensitivity, by means of ad-hoc calibration curves. In addition, performances provided by such hybrid HR-MS platform were compared with an optimized HPLC-ESI-SRM method we recently developed based on linear ion trap MS spectrometer [7] for the same kind of processed food matrix.

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  • Pilolli R.; De angelis E.; Bavaro S.L.; Monaci L.

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2017

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