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相关概念视频

High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation

2.7K
The fragmentation patterns observed for compounds such as carboxylic acids, esters, and amides in the mass spectra include ⍺-cleavage and McLafferty rearrangement. Fragmentation by ⍺-cleavage preferentially occurs at the carbon-carbon bond at the ⍺-position next to the carboxylic group to generate a neutral radical and a cation. Long chain compounds with hydrogen at their γ-carbon undergo McLafferty rearrangement to give a radical cation and a neutral alkene.
For example, the...
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Updated: Feb 28, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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一种机器学习和基准测试方法,用于从复杂混合物中分配超高分辨率质谱数据的分子公式分配.

Bilal Shabbir, Pablo R B Oliveira, Francisco Fernandez-Lima

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    概括
    此摘要是机器生成的。

    机器学习显著改善了复杂混合物的超高分辨率质谱学 (UHRMS) 中的分子公式分配. 与传统方法相比,这种数据驱动的方法提高了准确性和速度,有助于环境和生物研究.

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    科学领域:

    • 分析化学 分析化学
    • 计算化学计算化学
    • 环境科学 环境科学

    背景情况:

    • 超高分辨率质谱 (UHRMS) 对于分析像溶解有机物 (DOM) 等复杂混合物至关重要.
    • 准确的分子公式分配是具有挑战性的,但对于解释UHRMS数据至关重要.
    • 传统方法通常依赖于启发式调试和手动调试,限制了效率和适应性.

    研究的目的:

    • 开发和评估机器学习 (ML) 模型,以在UHRMS中增强分子公式赋值.
    • 将ML方法的性能与使用精选数据集的传统方法进行比较.
    • 提供基准数据集和代码,用于基于机器学习的公式赋值的未来研究.

    主要方法:

    • 在DOM的精心策划的UHRMS数据集上训练的k-最近邻居 (KNN) 算法的应用.
    • 评估质量精度对模型性能的影响 (0.15-1ppm).
    • 使用决策树回归器 (DTR) 和随机森林回归器 (RFR) 模型.

    主要成果:

    • 机器学习模型比传统方法分配了43%的公式 (5796比4047).
    • 模型合成实现了99.9%的分配率,注释了两倍多的公式 (8,268与4047).
    • DTR和RFR模型表现出高配方级准确度,分别为86.5%和60.4%.

    结论:

    • 机器学习方法显著增加了从UHRMS数据中分子公式赋值的数量和准确性.
    • 这些ML模型为复杂混合物分析的传统方法提供了更强大,更有效的替代方案.
    • 该研究提供了有价值的资源 (数据集和代码),以推进UHRMS在环境科学,代谢学和石油学中的数据解释.