Our first attempt to analyze RuAAC derivatization in an untargeted way is presented in the Supporting Information. It relied on a plant-specific analysis and sought to identify pairs of features, one derivatized and one underivatized. In light of the considerable signal increase obtained on RuAAC derivatization, we viewed this criterion as undesirable, as it requires that the underivatized alkyne be observable by LC-MS. To avoid imposing this assumption, we attempted to identify alkynes in our panel of plant materials by a simultaneous XCMS analysis of all of the samples, seeking to identify features which were (a) present in a RuAACderivatized plant material, and absent in (b) all RuAAC-derivatized materials from other plant species, as well as absent from (c) all mock-derivatized samples. This approach finds features that are unique to a particular plant matrix (Figure ), and does not necessitate that the underivatized feature is detected by LC-MS. It does assume that target analytes are unique to a particular plant species. We implemented this approach by modeling squareroot-tranformed feature intensity across all the samples as a linear function of the plant type, whether or not the derivatization was mock or a real RuAAC derivatization, and the interaction between plant type and RuAAC derivatization.