Friedrich Emil Fischer Center, Food Chemistry, Department of Chemistry and Pharmacy, Alexander Universität, Erlangen-Nürnberg, 91058, Erlangen, Germany.
Proteomics. 2019 Apr;19(7):e1800292. doi: 10.1002/pmic.201800292. Epub 2019 Mar 18.
This study designs a prediction model to differentiate pasteurized milk from heated extended shelf life (ESL) milk based on milk peptides. For this purpose, quantitative peptide profiles of a training set of commercial samples including pasteurized (n = 20), pasteurized-ESL (n = 13), and heated-ESL (n = 16) milk are recorded by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Seven peptides are selected as putative markers, and cutoff levels and performance measures of each marker are defined by receiver operating characteristic (ROC) analysis. The accuracy of these peptides in the training set range between 71% and 90%. A prediction model is established based on the combined cutoff levels and evaluated by an independent blind test set. The processing method of 19 out of 20 unknown milk samples is predicted correctly achieving 95% accuracy. Five peptides of the prediction model are identified as α -casein (m/z 2014.0), α -casein (m/z 2216.1), α -casein (m/z 2910.6), β-casein (m/z 2126.0), and β-casein (m/z 2391.2) indicating thermal release and the action of plasmin and cathepsins. Thus, the present study demonstrates that the milk peptide profile reflects even minor differences in production parameters.
本研究设计了一个预测模型，基于牛奶肽来区分巴氏杀菌奶和加热延长货架期（ESL）奶。为此，通过基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF-MS）记录了一个包含巴氏杀菌奶（n=20）、巴氏杀菌-ESL 奶（n=13）和加热-ESL 奶（n=16）商业样品的训练集的定量肽谱。选择了 7 个肽作为假定的标记物，并通过接收者操作特征（ROC）分析定义了每个标记物的截止值和性能指标。这些肽在训练集中的准确率在 71%到 90%之间。根据组合截止值建立预测模型，并通过独立的盲测试集进行评估。预测模型对 20 个未知牛奶样本中的 19 个处理方法预测正确，准确率为 95%。预测模型中的 5 个肽被鉴定为α-酪蛋白（m/z 2014.0）、α-酪蛋白（m/z 2216.1）、α-酪蛋白（m/z 2910.6）、β-酪蛋白（m/z 2126.0）和β-酪蛋白（m/z 2391.2），表明了热释放和纤溶酶及组织蛋白酶的作用。因此，本研究表明，牛奶肽谱甚至可以反映生产参数的微小差异。
