Metabolite fingerprinting of hops (Humulus lupulus) to track chemical variations
Date
2022
Authors
Nasiatka, Katie, author
Prenni, Jessica, advisor
Rhodes, Davina, committee member
Van Buiten, Charlene, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
In the brewing industry, identification of quality crops that provide unique organoleptic properties to beer flavor (aroma, taste) are of critical importance. Hops represent a key ingredient in beer and are utilized to impart specific flavors. India Pale Ales (IPAs) are a popular style of "hoppy beers" in the U.S. and customer expectations for consistency, quality, and unique organoleptic properties of hops are growing. While the contribution of chemical compounds in hops (Humulus lupulus) such as alpha-acids (e.g. humulone) is well-understood, the influence of the hop metabolome (e.g. composition of hop chemical compounds) is still in the early stages of discovery. There is a gap in the knowledge regarding our understanding of chemistry variations in hops among cultivars and growing locations that impact the sensory quality. Traditional sensory evaluation relies on the ability to organize a group of unbiased and trained panelists, who are also subject to sensory fatigue, which can add to the challenge of this method. An alternative approach, ambient mass spectrometry (AMS) is an objective, intuitive, analytical tool capable of rapid chemical fingerprinting. The overall goal of this research is to develop a robust, high-throughput assay using AMS technology to evaluate hop quality that is reflective of both cultivar and environmental variations impacting sensory. To address this goal, twelve hop samples were sourced from three different suppliers across four different farms located in Washington and Oregon over two growing seasons. The samples included three commercial cultivars, Cascade, Centennial, and Strata. The hop samples were extracted using an 80% ethanol solution and fingerprints were acquired by Direct Analysis in Real Time Mass Spectrometry (DART-MS). The resulting data were used to train predictive models and validation was performed to evaluate classification accuracy. Additionally, authentic standards of important hop compounds (hop alpha-acids, terpenes) were used to putatively annotate DART-MS signals reflective of sensory attributes. This study demonstrates the potential of this approach for rapid evaluation of hops quality and lays groundwork for further method optimization. Ultimately, implementation of this tool could have applications for quality assurance programs and for phenotyping of hops for producers and craft brewers.