Suppr超能文献

基于瓦片的 GC×GC-TOFMS 数据的成对分析,以促进分析物的发现和质谱纯化。

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification.

机构信息

Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195-1700, United States.

出版信息

Anal Chem. 2022 Apr 12;94(14):5658-5666. doi: 10.1021/acs.analchem.2c00223. Epub 2022 Mar 29.

Abstract

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (/) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP MCR-ALS, which removes the identified interferent / from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

摘要

提出了一种新的基于图块的两两分析工作流程,称为 1v1 分析,用于发现和识别使用全二维(2D)气相色谱与飞行时间质谱联用(GC×GC-TOFMS)采集的两个色谱图中区分的分析物。基于图块的 1v1 分析轻松地在排名前 30 的结果中发现了柴油燃料中添加的所有 18 种非天然分析物,优于标准的两两色谱分析。然而,由于背景污染,使用多元曲线分辨交替最小二乘法(MCR-ALS)和并行因子分析(PARAFAC)无法识别 8 种添加的分析物。通过启用类比较的质谱净化(CCE-MSP)进行了分析物鉴定,该方法通过将光谱归一化为从 1v1 分析中识别出的干扰质量通道(/)并减去两个光谱来获得纯分析物光谱。本报告还详细介绍了 CCE-MSP 和 MCR-ALS 的开发,该方法在分解之前从数据中去除已识别的干扰物/。总共,18 种添加的分析物中有 17 种用两种版本的 CCE-MSP 的匹配值(MV)>800。例如,MCR-ALS 和 PARAFAC 无法分解甲酯的纯光谱(MVs<200),因为其二维色谱分辨率(约 0.34)低且干扰物与分析物信号比(约 30:1)高。通过利用 1v1 分析获得的信息,CCE-MSP 和 CCE-MSP 辅助 MCR-ALS 分别获得了平均 MV 为 908 和 964 的纯光谱。此外,基于图块的 1v1 分析应用于跟踪可可豆的水分损坏,在未成型和成型样品之间发现了 86 种浓度变化至少为 2 倍的分析物。这种 1v1 分析工作流程对于无法获得或为节省分析时间而不希望使用多个重复的研究很有帮助。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验