Wei Ye, Varanasi Rama Srinivas, Schwarz Torsten, Gomell Leonie, Zhao Huan, Larson David J, Sun Binhan, Liu Geng, Chen Hao, Raabe Dierk, Gault Baptiste
Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany.
CAMECA Instruments, 5470 Nobel Drive, Madison, WI 53711, USA.
Patterns (N Y). 2021 Jan 21;2(2):100192. doi: 10.1016/j.patter.2020.100192. eCollection 2021 Feb 12.
Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.
质谱分析法是一种广泛应用的方法，用于确定材料的组成成分。从材料中去除并收集原子和分子，随后，一个关键步骤是根据它们的质荷比和相对同位素丰度所形成的模式推断其正确的身份。然而，这一识别步骤仍然主要依赖于个人用户的专业知识，这使得其标准化具有挑战性，并阻碍了高效的数据处理。在此，我们介绍一种利用现代机器学习技术在微秒内识别飞行时间质谱图中峰模式的方法，在不损失准确性的情况下优于人类用户。我们的方法在由不同飞行时间质谱（ToF-MS）技术生成的质谱图上进行了交叉验证，为ToF-MS领域提供了一种开源的、智能的质谱分析方法。
