MSident: Straightforward identification of chemical compounds from MS-resolved spectra
Recent advances in chromatography and mass spectrometry analytical techniques have led to the development of statistical and chemometric workflows for filtering and analyzing the vast amount of data acquired. The ROIMCR method has become increasingly important in data analysis of complex chemical mixtures analyzed by MS chromatographic and imaging analytical methods, such as in the analysis of natural mixtures of biomolecules or emerging environmental contaminants, among others. However, the final interpretation of the results of ROIMCR can be cumbersome and time-consuming, due to the manual filtering of the ROIMCR-resolved species spectra and of their matching within spectral repositories. In this study, we describe the MSident MATLAB app aimed at automatizing two aspects, the MS spectra filtering and their identification processes. MSident has been tested in three real data examples, demonstrating its capability to automatically identify chemical compounds from ROIMCR-resolved spectra using public repositories. These examples cover three types of mass spectrometry acquisition: i) MS1 signals, ii) tandem MS/MS (MS1 and MS2) signals, and iii) both MS1 and MS2 signals when they are acquired in positive and negative ionization modes. In these three cases, MSident successfully identified most of the chemical compounds present in their analyzed mixtures, yielding similar results to those obtained from standard manual searching procedures, but in a more straightforward way and in a shorter time.
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2024-02-15
|
| Subjects: | Chromatography, Mass spectrometry, ROIMCR, Ensure healthy lives and promote well-being for all at all ages, Ensure availability and sustainable management of water and sanitation for all, |
| Online Access: | http://hdl.handle.net/10261/348248 http://dx.doi.org/10.13039/501100004837 https://api.elsevier.com/content/abstract/scopus_id/85184995579 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|