Ratio between first and second Eigenvalues from GC–MS data serves as an approximation of an F-value, allowing them to be interpreted as Fisher ratios.

A moving window across a GC–MS chromatogram is used to compute Fisher ratios.

Probability of each acquisition being in a region of interest is computed from Fisher ratios.

Our algorithm is capable of detecting chromatographic regions of interest down to 5 pg on column.

Free, open-source algorithm available for implementation into existing chemometric workflows.

There are many challenges associated with analysing gas chromatography - mass spectrometry (GC–MS) data. Many of these challenges stem from the fact that electron ionization (EI) can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC–MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution (MCR), or Parallel Factor Analysis (PARAFAC/PARAFAC2) are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, k, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC–MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition (SVD) of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal. The algorithm achieved detection of features in a GC–MS chromatogram at concentrations below 10 pg on-column. The resultant probabilities can be interpreted as regions that contain features of interest.

Gas chromatography mass spectrometry

Region of interest selection

Chemometrics

Fisher ratio analysis

Data used in this study are available at the following URL: https://doi.org/10.5683/SP3/3OEMJY while the code for the algorithm can be found on GitHub at https://github.com/rylandchem/RegionOfInterest.

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