ArticleAnalytical methods : advancing methods and applications2018
Automated chemical identification and library building using dispersion plots for differential mobility spectrometry.
Article in Analytical methods : advancing methods and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Differentiating superficial fungal infection from eczema using a heated dynamic-headspace skin VOC sampler: a hypothesis.Frontiers in medicine · 2026Article
- OpenDMS: An Open-Source Approach to Differential Mobility Spectrometry.Analytical chemistry · 2025Article
- Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis.Applied food research · 2023Article
- Portable chemical detection platform for on-site monitoring of odorant levels in natural gas.Journal of chromatography. A · 2023Article
- Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification.Analytical methods : advancing methods and applications · 2022Article
- Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures.Analytical chemistry · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Differential mobility spectrometry (DMS) based detectors require rapid data analysis capabilities, embedded into the devices to achieve the optimum detection capabiites as portable trace chemical detectors. Automated algorithm-based DMS dispersion plot data analysis method was applied for the first time to pre-process and separate 3-dimentional (3-D) DMS dispersion data. We previously demonstrated our AnalyzeIMS (AIMS) software was capable of analyzing complex gas chromatography differential mobility spectrometry (GC-DMS) data sets. In our present work, the AIMS software was able to easliy separate DMS dispersion data sets of five chemicals that are important in detection of volatile organic compounds (VOCs): 2-butanone, 2-propanone, ethyl acetate, methanol and ethanol. Identification of chemicals from mixtures, separation of chemicals from a mixture and prediction capability of the software were all tested. These automated algorithms may have potential applications in separation of chemicals (or ion peaks) from other 3-D data obtained by hybrid analytical devices such as mass spectrometry (MS). New algorithm developments are included as future considerations to improve the current numerical approaches to fingerprint chemicals (ions) from a significantly complicated dispersion plot. Comprehensive peak identifcation by DMS-MS, variations of the DMS data due to chemical concentration, gas phase ion chemistry, temperature and pressure of the drift gas are considered in future algorithm improvements.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.