Speaker
Description
Due to its high sensitivity and resolving power, gas chromatography ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for non-target screening of complex sample materials. Given the wide range of applications, such as food authenticity, custom data analysis workflows are needed. As a common basis, they necessarily share many functionalities such as file input/output, preprocessing methods, and visualizations. This poster presents a new open-source Python package for handling and analysis of GC-IMS data with special attention on the variable selection tools. A workflow to classify honey samples by botanical origin and finding relevant compounds demonstrates functionality. Key preprocessing steps, exploratory – and supervised data analysis and model-based variable selections are visualized.
Source code and documentation are freely available as open-source under the BSD 3-clause license at https://github.com/Charisma-Mannheim/gc-ims-tools.
