Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Abstract

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.

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

“…Our artificial upsampling and learning pipeline that makes it possible to train machine learning algorithms on a few samples of SPME-DART-MS data, with high precision. Most previous work [ 30 , 31 , 32 , 33 ] rely on an intermediate step of peak detection and identification of VOCs using the NIST05 mass spectral library. In our work, an end-to-end pipeline is developed using python, which takes the raw MS data as input and automatically performs background subtraction, peak detection, feature identification, and classification.…”

“…show that supervised learning can be successfully used to classify microbial strains using meta-information about their VOC profiles [ 30 ]. Other works add to this by showing that SPME and gas chromatography mass spectrometry can be used to sample VOC signatures, which can be used to create VOC profiles for classification [ 31 , 32 , 33 ]. In this work, we use signal processing and ML techniques to develop a rapid, robust and end-to-end Python pipeline for classifying a pathogen as bacteria or fungi, using the raw MS spectrum.…”

Discrimination and Characterization of the Volatile Organic Compounds in Schizonepetae Spica from Six Regions of China Using HS-GC-IMS and HS-SPME-GC-MS

“…At the same time, based on the NIST database search, this technology can realize the discovery of trace volatile components in complex sample systems [ 34 ]. HS-GC-IMS is a powerful technique for the separation and sensitive detection of VOCs, with the advantages of high sensitivity and resolution [ 35 ]. In the present study, the VOCs of SS were investigated by HS-GC-IMS and HS-SPME-GC-MS. As can be seen from Table 1 and Table 2 , most volatile organics detected by HS-GC-IMS are small molecular compounds, whereas the detection range of HS-SPME-GC-MS is usually medium molecular weight VOCs.…”

“…This gives us a new idea to distinguish Chinese herbal medicines in different regions. A number of studies have indicated that HS-GC-IMS has the capacity to confirm geographical and botanical origin [ 35 ]. For example, HS-GC-IMS was successfully used for reliable classification of geographical origins for both olive oil (EVOO) [ 38 ] and wine [ 39 ].…”

“…At the same time, based on the NIST database search, this technology can realize the discovery of trace volatile components in complex sample systems [34]. HS-GC-IMS is a powerful technique for the separation and sensitive detection of VOCs, with the advantages of high sensitivity and resolution [35].…”