Spörl Johannes, Speer Karl, Jira Wolfgang
Department of Safety and Quality of Meat, Max Rubner-Institut (MRI), E.-C.-Baumann-Straße 20, 95326 Kulmbach, Germany.
Faculty of Chemistry and Food Chemistry, Technical University of Dresden, Helmholtzstraße 10, 01069 Dresden, Germany.
Foods. 2022 Jul 20;11(14):2155. doi: 10.3390/foods11142155.
Food fraud is a common issue in the modern food industry. The undeclared use of foreign proteins in meat products is a major concern in this context. Oilseeds are ideal for this purpose due to their high protein content and since huge amounts of oil meal are obtained as a by-product of oil production. Therefore, a UHPLC-MS/MS method was developed for the simultaneous detection of chia, coconut, flaxseed, hemp, peanut, pumpkin, rapeseed, sesame, soy, and sunflower proteins in meat products. Potential tryptic peptide markers were identified by high-resolution mass spectrometry. The final twenty peptide markers selected, which are specific for one of the ten species targeted, were each measured by multiple reaction monitoring. To the best of our knowledge, twelve new heat-stable marker peptides for chia, coconut, flaxseed, pumpkin, rapeseed, sesame and sunflower have not been reported previously. Emulsion-type sausages with 0.01, 0.25, 0.50, 0.75 and 1.00% protein addition by each oilseed species were produced for matrix calibration. No false-positive results were recorded. In the quantification of the ten oilseed species, 466 of 480 measuring data points of the recovery rate in unknown sausages (0.15 and 0.85% protein addition by each oilseed species) were in the accepted range of 80-120%.
食品欺诈是现代食品行业中的一个常见问题。在这种情况下，肉类产品中未申报使用的外源蛋白质是一个主要问题。由于油籽蛋白质含量高，且作为油脂生产的副产品可获得大量油粕，因此油籽非常适合用于此目的。因此，开发了一种超高效液相色谱-串联质谱法（UHPLC-MS/MS），用于同时检测肉类产品中的奇亚籽、椰子、亚麻籽、大麻、花生、南瓜、油菜籽、芝麻、大豆和向日葵蛋白。通过高分辨率质谱法鉴定了潜在的胰蛋白酶肽标记物。最终选择的二十种肽标记物，分别针对十种目标物种中的一种，通过多反应监测进行测量。据我们所知，奇亚籽、椰子、亚麻籽、南瓜、油菜籽、芝麻和向日葵的十二种新的热稳定标记肽此前尚未见报道。制备了分别添加0.01%、0.25%、0.50%、0.75%和1.00%每种油籽蛋白的乳化型香肠用于基质校准。未记录到假阳性结果。在对十种油籽物种的定量分析中，未知香肠（每种油籽物种添加0.15%和0.85%蛋白质）回收率的480个测量数据点中的466个在80%-120%的可接受范围内。
