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Advanced data preprocessing for comprehensive two‐dimensional gas chromatography with vacuum ultraviolet spectroscopy detection
Author(s) -
Lelevic Aleksandra,
Souchon Vincent,
Geantet Christophe,
Lorentz Chantal,
Moreaud Maxime
Publication year - 2021
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.202100528
Subject(s) - preprocessor , noise (video) , data pre processing , analyte , dimension (graph theory) , noise reduction , signal to noise ratio (imaging) , computer science , pattern recognition (psychology) , analytical chemistry (journal) , artificial intelligence , chemistry , optics , chromatography , mathematics , physics , pure mathematics , image (mathematics)
Comprehensive two-dimensional gas chromatography with vacuum ultraviolet detection results in sizable data for which noise and baseline drift ought to be corrected. As the data is acquired from multiple channels, preprocessing steps have to be applied to the data from all channels while being robust and rather fast with respect to the significant size of the data. In this study, we have described advanced data preprocessing techniques for such data which were not available in the existing commercial software solutions and which were dedicated primarily to noise and baseline correction. Noise reduction was performed on both the spectral and the time dimension. For the baseline correction, a morphological approach based on iterated convolutions and rectifier operations was proposed. On the spectral dimension, much less noisy and reliable spectra were obtained. From a quantitative point of view, mentioned preprocessing steps significantly improved the signal-to-noise ratio for the analyte detection (circa six times in this study). These preprocessing methods were integrated into the plugim! platform (https://www.plugim.fr/).