@article{BoenickHuschekRawel2017, author = {B{\"o}nick, Josephine and Huschek, Gerd and Rawel, Harshadrai Manilal}, title = {Determination of wheat, rye and spelt authenticity in bread by targeted peptide biomarkers}, series = {Journal of Food Composition and Analysis}, volume = {58}, journal = {Journal of Food Composition and Analysis}, publisher = {Elsevier}, address = {San Diego}, issn = {0889-1575}, doi = {10.1016/j.jfca.2017.01.019}, pages = {82 -- 91}, year = {2017}, abstract = {Adulteration of food and mislabeled products in global market is a major financial and reputational risk for food manufacturers and trade companies. Consequently, there is a necessity to develop analytical methods to meet these issues. An analytical strategy to check the authenticity of wheat, spelt and rye addition in bread products was developed based on database research, in silico digestion confirming peptide specificity and finally quantification by liquid chromatography-tandem mass spectrometry analysis. Peptide markers for wheat (SQQQISQQPQQLPQQQQIPQQPQQF; QQHQIPQQPQQFPQQQQF and QPHQPQQPYPQQ), spelt (ASIVVGIGGQ; SQQPGQIIPQQPQQPSPL) and rye (LPQSHKQHVGQGAL; AQVQGIIQPQQL and QQFPQQPQQSFPQQPQQPVPQQPL) were identified, verified by protein Basic Local Alignment Search Tool and database research and used for quantification in bread. The specific use of multi-reaction monitoring transitions of selected peptides permitted the identification of closely related species wheat and spelt. Other cereal species (emmer, einkorn, barley, maize, rye and oat) were also checked. The target peptides were quantified at different levels using own reference baked products (bread) after in-solution chymotryptic digestion. Sensitivity of the identification was 0.5-1\% using flour-based (0-25\%) matrix calibration and the analytical recovery in bread was 80-125\%. The analytical strategy described here supplies an emerging, independent and flexible tool in controlling the labeling of bread.}, language = {en} } @article{HuschekBoenickLoewensteinetal.2016, author = {Huschek, Gerd and Boenick, Josephine and Loewenstein, Yvonne and Sievers, Steven and Rawel, Harshadrai Manilal}, title = {Quantification of allergenic plant traces in baked products by targeted proteomics using isotope marked peptides}, series = {LWT - food science and technology : an official journal of the Swiss Society of Food Science and Technology (SGLWT/SOSSTA) and the International Union of Food Science and Technology (IUFoST)}, volume = {74}, journal = {LWT - food science and technology : an official journal of the Swiss Society of Food Science and Technology (SGLWT/SOSSTA) and the International Union of Food Science and Technology (IUFoST)}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0023-6438}, doi = {10.1016/j.lwt.2016.07.057}, pages = {286 -- 293}, year = {2016}, abstract = {The right choice of analytical methods for plant allergen quantification is a deciding factor for the correct assessment and labeling of allergens in processed food in view of consumer protection. The aim of the present study was to develop a validated target peptide multi-method by LC/MS/MS providing high specificity and sensitivity for plant allergen protein detection, plant identification in vegan or vegetarian products using peptide markers for quantification. The methodical concept considers the selection of target peptides of thermostable allergenic plant proteins (Gly m6 soy, Ses i6 sesame and (beta-conglutin from white lupine) by data base research, BLAST and in silico digestion using Skyline software. Different allergenic concentration levels of these proteins were integrated into our own reference bakery products and quantified with. synthesized isotopically labeled peptides after in-solution digestion using LC/MS/MS. Recovery rates within the range of 70-113\% and LOQ of 10 ppm-50 ppm (mg allergenic food/kg) could be determined. The results are independent of thermal processing applied during baking and of epitope binding site for the tested allergens. (C) 2016 Elsevier Ltd. All rights reserved.}, language = {en} }