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Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Capitain, Charlotte Weller, Philipp |
| Copyright Year | 2021 |
| Description | Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS. |
| Starting Page | 5457 |
| e-ISSN | 14203049 |
| DOI | 10.3390/molecules26185457 |
| Journal | Molecules |
| Issue Number | 18 |
| Volume Number | 26 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-08 |
| Access Restriction | Open |
| Subject Keyword | Molecules Analytical Chemistry Gas Chromatography Ion Mobility Spectroscopy (gc-ims) Volatile Organic Compounds (vocs) Non-targeted Screening (nts) Using Machine Learning |
| Content Type | Text |
| Resource Type | Article |
