The results of an inter-laboratory study with five commercially available peanut ELISA test kits to detect and quantify peanut residues in two food matrices (biscuit and dark chocolate) at four different concentrations (0–10 mg peanut kg⁻¹ matrix corresponding to about 0–2.5 mg peanut protein kg⁻¹ matrix) are reported. In general the five ELISA test kits evaluated could detect peanut protein in the two food matrices. In three cases, the study challenged the test kits beyond their intended use for quantification below the manufacturers’ defined cut-off limits. Generally, all five ELISA test kits performed well in the concentration range 5–10 mg kg⁻¹ rather than in the low concentration range (2.0 or 2.5 mg kg⁻¹). The variation in the found recoveries of peanut between the different test kits had a spread of 44–191% across all concentrations. The quantification characteristics between test kits differed significantly at the very low mg kg⁻¹ level. Two test kits performed well even at concentrations below 5 mg kg⁻¹ with reproducibilities of 27–36% for biscuits and 45–57% for chocolate.

Accurate, reliable and sensitive detection methods for gluten are required to support current EU regulations. The enforcement of legislative levels requires that measurement results are comparable over time and between methods. This is not a trivial task for gluten which comprises a large number of protein targets. This paper describes a strategy for defining a set of specific analytical targets for wheat gluten. A comprehensive proteomic approach was applied by fractionating wheat gluten using RP-HPLC (reversed phase high performance liquid chromatography) followed by a multi-enzymatic digestion (LysC, trypsin and chymotrypsin) with subsequent mass spectrometric analysis. This approach identified 434 peptide sequences from gluten. Peptides were grouped based on two criteria: unique to a single gluten protein sequence; contained known immunogenic and toxic sequences in the context of coeliac disease. An LC-MS/MS method based on selected reaction monitoring (SRM) was developed on a triple quadrupole mass spectrometer for the specific detection of the target peptides. The SRM based screening approach was applied to gluten containing cereals (wheat, rye, barley and oats) and non-gluten containing flours (corn, soy and rice). A unique set of wheat gluten marker peptides were identified and are proposed as wheat specific markers. The measurement of gluten in processed food products in support of regulatory limits is performed routinely. Mass spectrometry is emerging as a viable alternative to ELISA based methods. Here we outline a set of peptide markers that are representative of gluten and consider the end user's needs in protecting those with coeliac disease. The approach taken has been applied to wheat but can be easily extended to include other species potentially enabling the MS quantification of different gluten containing species from the identified markers.

There is a need for selective and sensitive methods to detect the presence of food allergens at trace levels in highly processed food products. In this work, a combination of non-targeted and targeted proteomics approaches are used to illustrate the difficulties encountered in the detection of the major peanut allergens Ara h 1, Ara h 2 and Ara h 3 from a representative processed food matrix. Shotgun proteomics was employed for selection of the proteotypic peptides for targeted approaches via selective reaction monitoring. Peanut presence through detection of the proteotypic Ara h 3/4 peptides AHVQVVDSNGNR (m/z 432.5, 3+) and SPDIYNPQAGSLK (m/z 695.4, 2+) was confirmed and the developed method was able to detect peanut presence at trace levels (≥10 μg peanut g⁻¹ matrix) in baked cookies.

The capability of a capillary LC-Q-TOF mass spectrometer system as a qualitative tool for the identification and confirmation of milk allergens in thermally processed food was investigated. Milk powder incurred cookies were produced in-house and chosen as the model food matrix. To unequivocally assess the presence of milk allergens, samples testing positive to ELISA were analysed by a capillary LC-Q-TOF MS/MS method in order to identify specific peptides that can be used as markers for milk allergens. Results show that α-S1 casein was the protein identified with the highest score (in 100 μg g⁻¹ milk powder incurred cookies) and its identity was confirmed by detection of the peptides m/z 692.86 and 634.34 and their specific MS/MS ions providing a fingerprint for α-S1 casein. Besides that, other milk proteins were highlighted by performing database searching. The proteomic MS-based method employing a capillary LC-Q-TOF system proved to be a valuable tool to carry out qualitative and confirmative analysis to trace contamination of milk allergens in processed food matrices.