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Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

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...

¹H NMR: Complex Splitting

¹H NMR: Complex Splitting

A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

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Related Experiment Video

Updated: Feb 18, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming

Bo-Han Su¹, Meng-Yu Shen¹, Yeu-Chern Harn²

¹Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei, 106, Taiwan.

Journal of Cheminformatics

|November 17, 2017

Summary

This summary is machine-generated.

This study introduces an efficient iterative dynamic programming algorithm for natural product structure elucidation, significantly speeding up the identification of drug candidates from complex mixtures.

Keywords:

CASE Dynamic programming Natural products Polynomial time

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Last Updated: Feb 18, 2026

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Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

Area of Science:

Natural Product Chemistry
Computational Chemistry
Drug Discovery

Background:

Identifying chemical structures in natural product mixtures is crucial for drug discovery but remains challenging due to time-consuming elucidation processes and limitations in mass spectral data.
Existing computer-aided structure elucidation (CASE) tools struggle to match the performance of experienced chemists.

Purpose of the Study:

To develop an efficient computational algorithm for precise chemical structure identification in natural product mixtures.
To overcome the limitations of existing CASE tools and improve the speed and accuracy of natural product structural elucidation.

Main Methods:

Formulated structural elucidation as a computational problem by extending scaffold lists with weighted side chains, based on analysis of 243,130 natural products.
Designed a novel iterative dynamic programming (DP) algorithm to solve the NP-complete problem in pseudo-polynomial time, addressing the limitations of traditional DP algorithms with increasing mass spectrometry precision.

Main Results:

The iterative DP algorithm precisely identifies chemical structures in natural product mixtures.
Demonstrated polynomial time performance for average cases in elucidating structures of four experimentally determined natural products.
Significantly improved the speed of natural product structural elucidation.

Conclusions:

The developed algorithm enhances the efficiency of identifying potential new drug candidates from natural product sources.
The method broadens the scope of compounds applicable for drug discovery by accelerating structural elucidation.
A web service for structural elucidation studies is available at http://csccp.cmdm.tw/.