wtv

WTV 2.0 (widely targeted volatilomics) is a Python software toolkit with a user-friendly interface for high-sensitivity, high-coverage GC–MS profiling and annotation of plant volatiles (and, more broadly, primary metabolites). It couples an expanded reference library with 2,111 curated volatiles to a comprehensive selective ion monitoring strategy (cSIM) that maximizes acquisition efficiency by selecting a minimal set of characteristic qualitative ions per compound and optimizing how ions are segmented across the run. By acquiring the smallest sufficient number of ions for most targets, WTV 2.0 increases dwell time per ion and thereby improves sensitivity while maintaining broad chemical coverage.

The workflow supports automated qualitative identification and semi-quantitative analysis directly from cSIM data and is designed to be extensible: compounds absent from the library can be incorporated using information recovered from newly acquired cSIM datasets, enabling iterative self-expansion. In benchmarking, WTV 2.0 substantially improves signal-to-noise (median ~7.6× versus untargeted full-scan) and increases annotation coverage (e.g., ~2× in tomato fruit compared with untargeted and earlier WTV methods), facilitating detection of low-abundance bioactive or aroma-relevant compounds and enabling discoveries such as menthofuran as a flavor contributor in passion fruit.