Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning.
Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning.
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DOI：
10.1038/s41598-021-82846-5
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发表时间：
2021-02-08
影响因子：
4.6
通讯作者：
Cramer R
中科院分区：
文献类型：
--
作者：
Piras C;Hale OJ;Reynolds CK;Jones AK;Taylor N;Morris M;Cramer R
监管机构和消费者对食品质量和可追溯性的兴趣日益浓厚，要求在更快速、通用和具有成本效益的分析方法方面取得进展。牛奶和大多数食物基质一样，是由代谢物、脂质和蛋白质组成的异质混合物。主要挑战之一是对这组生物分子进行同步、定量检测（分析），以收集有价值的信息，以评估食品质量、可追溯性和安全性。在这里，在Q-TOF质谱分析仪上，使用均匀液体样品液滴的常压基质辅助激光解吸/电离来分析牛奶。该方法能够产生多电荷的蛋白质离子，以及单电荷脂质/代谢物的高信息性剖面。在两个例子中，这种方法与用户友好的机器学习软件相结合。首先，以100%的分类准确率证明了牛奶（奶牛、山羊、绵羊和骆驼）的快速物种形成。其次，在羊奶中检测到掺假牛奶的浓度低至5%，灵敏度为92.5%，特异性为94.5%。
Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. One of the major challenges is to have simultaneous, quantitative detection (profiling) of this panel of biomolecules to gather valuable information for assessing food quality, traceability and safety. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. In two examples, this method is coupled with user-friendly machine-learning software. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. Second, the detection of cow milk as adulterant in goat milk is shown at concentrations as low as 5% with 92.5% sensitivity and 94.5% specificity.
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影响因子：
16.6
作者：
Cramer, Rainer;Pirkl, Alexander;Hillenkamp, Franz;Dreisewerd, Klaus
通讯作者：
Dreisewerd, Klaus
影响因子：
4.1
作者：
Hale, Oliver J.;Morris, Michael;Cramer, Rainer
通讯作者：
Cramer, Rainer
影响因子：
6.1
作者：
Sassi, Mauro;Arena, Simona;Scaloni, Andrea
通讯作者：
Scaloni, Andrea
影响因子：
6.2
作者：
Ryumin, Pavel;Cramer, Rainer
通讯作者：
Cramer, Rainer
影响因子：
4.6
作者：
England, Philippa;Tang, Wenhao;Larrouy-Maumus, Gerald
通讯作者：
Larrouy-Maumus, Gerald