Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection

Area of Science:

• Plant metabolomics
• Cheminformatics
• Biotechnology

Background:

• Omics approaches offer diverse plant analysis applications, including classification and bioactive compound discovery.
• Metabolomics, utilizing techniques like LC-MS and NMR, provides rich phenotypic data but faces experimental complexity.
• Untargeted metabolomics presents a powerful tool for detailed plant characterization.

Purpose of the Study:

• To develop and demonstrate an untargeted metabolomics pipeline for plant classification.
• To classify Humulus lupulus (hops) cultivars using LC-MS profiling.
• To identify marker compounds responsible for cultivar discrimination.

Main Methods:

• LC-MS profiling of Humulus lupulus brewing cultivars.
• Hierarchical cluster analysis (HCA) for sample classification.
• Nested feature selection for marker compound discovery.
• Database searching and molecular network construction for compound identification.

Main Results:

• Metabolome-based classification of hops cultivars revealed distinct differences.
• Nine compounds exhibited significant classification power.
• Five of these compounds were identified as known hops bitter compounds.

Conclusions:

• Untargeted metabolomics provides a robust method for plant cultivar classification.
• The identified bitter compounds can serve as markers for Humulus lupulus discrimination.
• This pipeline aids in discovering bioactive compounds and understanding plant phenotypes.