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DropMS:基于网络服务器的用于高分辨率质谱的石油组学数据处理。

DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry.

机构信息

Federal Institute of Education, Science, and Technology of Espirito Santo, 29192-733 Aracruz, ES, Brazil.

Federal University of Espírito Santo, 29190-333 Vitória, ES, Brazil.

出版信息

J Am Soc Mass Spectrom. 2020 Jul 1;31(7):1483-1490. doi: 10.1021/jasms.0c00109. Epub 2020 Jun 17.

Abstract

We have built an online tool with a user-friendly and browser-based interface to facilitate the processing of high resolution and precision oil mass spectrometry data. DropMS does not require software installations. Mass spectra are sent and processed by the server using various algorithms reported in the literature, such as S/N ratio filters, recalibrations, chemical formula assimilations, and data visualization using graphs and diagrams popularly known in mass spectrometry as Van Krevelen and Kendrick diagrams and DBE vs C#. To validate the algorithms used and the processing results, the same mass spectrum of a typical Brazilian oil sample was analyzed by ESI(+)-FT-ICR/MS and processed using Sierra Analytics DropMS and Composer to obtain good agreement between the heteroatomic classes found and the number of compounds assigned. The MS has chemical information spread over the entire spectrum. The PLS multivariate regression has the main objective of decomposing the most important information into latent variables in order to quantify the most evaluated properties. Finally, 12 processed petroleum FT-ICR MS spectra were used for a partial least-squares regression with seven latent variables for = 0.971 and RMSEC of 0.997 for API density property with a reference value range of 21-42.

摘要

我们构建了一个具有用户友好的基于浏览器界面的在线工具,以方便处理高分辨率和高精度的油质质谱数据。DropMS 不需要软件安装。使用服务器上的各种算法(如 S/N 比滤波器、重新校准、化学式同化以及使用 Van Krevelen 和 Kendrick 图以及 DBE 与 C# 等质谱中常用的图形和图表进行数据可视化)发送和处理质谱。为了验证所使用的算法和处理结果,我们使用 ESI(+)-FT-ICR/MS 分析了典型的巴西油样的相同质谱,并使用 Sierra Analytics DropMS 和 Composer 进行处理,以获得发现的杂原子类和分配的化合物数量之间的良好一致性。MS 具有分布在整个光谱中的化学信息。PLS 多元回归的主要目的是将最重要的信息分解为潜在变量,以便对评估最多的性质进行量化。最后,我们使用 12 个经过处理的石油 FT-ICR MS 光谱进行偏最小二乘回归,其中有 7 个潜在变量,用于 = 0.971 和 RMSEC 为 0.997 的 API 密度属性,参考值范围为 21-42。

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