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Related Concept Videos

High-Performance Liquid Chromatography: Types of Detectors01:15

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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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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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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution

Michael Woldegebriel1, Eduard Derks2

  • 1Analytical Chemistry, Van't Hoff Institute for Molecular Sciences, University of Amsterdam , P.O. Box 94720, 1090 GE Amsterdam, The Netherlands.

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|December 31, 2016
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Summary

A new artificial neural network (ANN) algorithm enhances untargeted feature detection in liquid chromatography-high resolution mass spectrometry (LC-HRMS). This probabilistic method improves sensitivity and compound identification without arbitrary data thresholds.

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry

Background:

  • Untargeted feature detection in LC-HRMS is crucial for identifying unknown compounds.
  • Existing algorithms often rely on computationally expensive regression or arbitrary thresholds, limiting sensitivity and data utilization.

Purpose of the Study:

  • To present a novel probabilistic untargeted feature detection algorithm for LC-HRMS.
  • To leverage artificial neural networks (ANNs) for pattern recognition in chromatographic data.
  • To improve sensitivity and data utilization in compound identification.

Main Methods:

  • Developed a probabilistic untargeted feature detection algorithm using ANNs.
  • Trained the ANN to recognize chromatographic peak shapes as a pattern recognition problem.
  • Applied the algorithm to LC-HRMS data without arbitrary thresholds or data reduction.

Main Results:

  • The ANN-based algorithm effectively detects features by recognizing chromatographic profiles.
  • High-resolution data is fully utilized, enhancing sensitivity for compound identification.
  • The probabilistic approach assigns probabilities to features, allowing for nuanced data analysis.
  • Promising results were obtained in forensic and food safety applications with spiked samples.

Conclusions:

  • The novel ANN algorithm offers an efficient and sensitive approach to untargeted feature detection in LC-HRMS.
  • This probabilistic method overcomes limitations of traditional deterministic algorithms and computationally intensive models.
  • The algorithm demonstrates potential for improved compound identification in complex matrices.