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相关概念视频

Mass Spectrometers01:16

Mass Spectrometers

5.7K
This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
5.7K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

828
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...
828
Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation

1.4K
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,...
1.4K
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

1.3K
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 low-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...
1.3K
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

5.4K
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 electrospray 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...
5.4K
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

1.4K
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...
1.4K

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Updated: Jul 23, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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基于数组的机器学习用于电子电离质谱中的功能组检测.

Nicole M North1, Abigail A Enders1, Morgan L Cable2

  • 1Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.

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概括
此摘要是机器生成的。

人工智能 (AI) 模型可以从质谱数据中识别功能组. 在这种化学分析任务中,物流回归模型的准确性高于卷积神经网络 (CNN).

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科学领域:

  • 分析化学 分析化学
  • 计算化学计算化学
  • 化学中的人工智能.

背景情况:

  • 质谱法 (MS) 是化学分析的一个关键技术,产生碎片化模式,有助于识别分子结构.
  • 人工智能 (AI) 具有自动化和加快复杂MS数据分析的潜力,特别是用于识别功能组.

研究的目的:

  • 开发和评估人工智能模型,用于从电子电离质谱中自动识别功能组.
  • 在这个任务中,比较卷积神经网络 (CNN) 和后勤回归模型的性能.
  • 为了确定最有信息的质量-电荷比率 (m/z) 范围和关键片段,以准确检测功能组.

主要方法:

  • 在2D光谱图像上使用基于数组的光谱数据和CNN模型 (Inception V3) 训练后勤回归模型.
  • 利用来自NIST Webbook的21,166个质谱的数据集进行模型训练.
  • 对其识别特定 (例如,氨基,) 和概括的功能组 (例如,芳香物) 的能力进行了评估.

主要成果:

  • 与CNN转移学习模型相比,物流回归模型在识别功能组方面表现出更高的准确性.
  • 0-100 m/z的质量范围被发现是有效的功能组分析最关键的.
  • 开发了一种方法来确定有影响力的碎片,有助于准确的功能组识别.

结论:

  • 人工智能,特别是物流回归,显示出在质谱学中自动化功能组分析的重大前景.
  • 这些发现为大规模质谱数据集的有效选和分析提供了途径.
  • 了解关键的光谱区域和碎片可以提高AI驱动化学分析的可靠性.