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The Multidimensional Chromatography (MDC) Workshop
The Multidimensional Chromatography (MDC) Workshop draws experts in this exciting field to share and discuss their research.
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Differences between aerospace fuels by GCxGC-TOF MS & chemometrics (Caitlin Cain, MDCW 2023)

Th, 7.12.2023
| Original article from: The Multidimensional Chromatography (MDC) Workshop
Herein, we demonstrate the use of an unsupervised feature selection algorithm to find analytes that correlate to the performance of aviation turbine fuels and kerosene-based rocket propellants.
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  • Photo: MDCW: Differences between aerospace fuels by GCxGC-TOF MS & chemometrics (Caitlin Cain, MDCW 2023)
  • Video: LabRulez: Caitlin Cain: Differences between aerospace fuels by GCxGC-TOF MS & chemometrics
  • 🎤 Presenter: Caitlin Cain, Grant Ochoa, Robert Synovec (University of Washington, Seattle, USA)

💡 Book in your calendar: 15th Multidimensional Chromatography Workshop (MDCW) January 2024

15th Multidimensional Chromatography (MDC) Workshop 2024

Abstract

The chemical composition of aerospace fuels directly impacts system performance, reliability, and operability. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) has become a vital platform for characterizing the composition of these fuels to ensure regulatory compliance. However, the use of GC×GC-TOFMS to characterize these aerospace fuels can create a large amount of data for an analyst to manually interpret. Therefore, chemometric workflows are necessary to quickly reveal significant chemical information regarding the composition of these fuels.

Herein, we demonstrate the use of an unsupervised feature selection algorithm to find analytes that correlate to the performance of aviation turbine fuels and kerosene-based rocket propellants. In contrast to supervised methods, which use class labels to discover analytes related to those differences, unsupervised approaches can uncover various chemical relationships hidden in the data. Visual interpretation and additional chemometric models will provide further insight into the relationship between the physical properties of the fuels and the differences discovered in their chemical composition. Ultimately, the presented workflow will aid in the continued effort to improve data analysis methods for discovery-based experimentation.

The Multidimensional Chromatography (MDC) Workshop
 

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