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Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory
[Image: see text] This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326912/ https://www.ncbi.nlm.nih.gov/pubmed/37255332 http://dx.doi.org/10.1021/jasms.3c00089 |
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author | Jackson, Glen P. Mehnert, Samantha A. Davidson, J. Tyler Lowe, Brandon D. Ruiz, Emily A. King, Jacob R. |
author_facet | Jackson, Glen P. Mehnert, Samantha A. Davidson, J. Tyler Lowe, Brandon D. Ruiz, Emily A. King, Jacob R. |
author_sort | Jackson, Glen P. |
collection | PubMed |
description | [Image: see text] This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one of this two-part report describes how Rice–Ramsperger–Kassel–Marcus (RRKM) theory predicts that many branching ratios in replicate electron–ionization mass spectra will provide approximately linear correlations when analysis conditions change within or between instruments. Here, proof-of-concept general linear modeling is based on the 20 most abundant fragments in a database of 128 training spectra of cocaine collected over 6 months in an operational crime laboratory. The statistical validity of the approach is confirmed through both analysis of variance (ANOVA) of the regression models and assessment of the distributions of the residuals of the models. General linear modeling models typically explain more than 90% of the variance in normalized abundances. When the linear models from the training set are applied to 175 additional known positive cocaine spectra from more than 20 different laboratories, the linear models enabled ion abundances to be predicted with an accuracy of <2% relative to the base peak, even though the measured abundances vary by more than 30%. The same models were also applied to 716 known negative spectra, including the diastereomers of cocaine: allococaine, pseudococaine, and pseudoallococaine, and the residual errors were larger for the known negatives than for known positives. The second part of the manuscript describes how general linear regression modeling can serve as the basis for binary classification and reliable identification of cocaine from its diastereomers and all other known negatives. |
format | Online Article Text |
id | pubmed-10326912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-103269122023-07-08 Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory Jackson, Glen P. Mehnert, Samantha A. Davidson, J. Tyler Lowe, Brandon D. Ruiz, Emily A. King, Jacob R. J Am Soc Mass Spectrom [Image: see text] This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one of this two-part report describes how Rice–Ramsperger–Kassel–Marcus (RRKM) theory predicts that many branching ratios in replicate electron–ionization mass spectra will provide approximately linear correlations when analysis conditions change within or between instruments. Here, proof-of-concept general linear modeling is based on the 20 most abundant fragments in a database of 128 training spectra of cocaine collected over 6 months in an operational crime laboratory. The statistical validity of the approach is confirmed through both analysis of variance (ANOVA) of the regression models and assessment of the distributions of the residuals of the models. General linear modeling models typically explain more than 90% of the variance in normalized abundances. When the linear models from the training set are applied to 175 additional known positive cocaine spectra from more than 20 different laboratories, the linear models enabled ion abundances to be predicted with an accuracy of <2% relative to the base peak, even though the measured abundances vary by more than 30%. The same models were also applied to 716 known negative spectra, including the diastereomers of cocaine: allococaine, pseudococaine, and pseudoallococaine, and the residual errors were larger for the known negatives than for known positives. The second part of the manuscript describes how general linear regression modeling can serve as the basis for binary classification and reliable identification of cocaine from its diastereomers and all other known negatives. American Chemical Society 2023-05-31 /pmc/articles/PMC10326912/ /pubmed/37255332 http://dx.doi.org/10.1021/jasms.3c00089 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Jackson, Glen P. Mehnert, Samantha A. Davidson, J. Tyler Lowe, Brandon D. Ruiz, Emily A. King, Jacob R. Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory |
title | Expert Algorithm for
Substance Identification Using
Mass Spectrometry: Statistical Foundations in Unimolecular Reaction
Rate Theory |
title_full | Expert Algorithm for
Substance Identification Using
Mass Spectrometry: Statistical Foundations in Unimolecular Reaction
Rate Theory |
title_fullStr | Expert Algorithm for
Substance Identification Using
Mass Spectrometry: Statistical Foundations in Unimolecular Reaction
Rate Theory |
title_full_unstemmed | Expert Algorithm for
Substance Identification Using
Mass Spectrometry: Statistical Foundations in Unimolecular Reaction
Rate Theory |
title_short | Expert Algorithm for
Substance Identification Using
Mass Spectrometry: Statistical Foundations in Unimolecular Reaction
Rate Theory |
title_sort | expert algorithm for
substance identification using
mass spectrometry: statistical foundations in unimolecular reaction
rate theory |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326912/ https://www.ncbi.nlm.nih.gov/pubmed/37255332 http://dx.doi.org/10.1021/jasms.3c00089 |
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