About the Seminar:
Hops are one of the four primary raw ingredients used to make beer and they are employed by brewers to impart specific flavor and aroma profiles, particularly in craft beer styles. These unique sensory attributes are a result of chemical compounds in hops such as alpha acids and terpenes which can vary significantly between hop cultivars and can also be influenced by the growing environment in which the hops were produced. Thus, while it is clear that understanding the chemical profile of hops is critical for beer quality (and the development of new, unique beers), the optimal analytical tools are often out of reach for the craft brewer due to cost, time, and required expertise. Instead, hop producers and brewers primarily rely on sensory evaluation for hop characterization which requires a large group of trained panelists, is often subjective, and is time consuming and fatiguing – limiting throughout. Here, we evaluate direct analysis by mass spectrometry (DART-MS) coupled with predictive computational tools as an alternative “fit for purpose” approach for hop characterization. The approach was characterized using unique hop samples sourced from three different suppliers across four different farms located in Washington and Oregon over two growing seasons. Chemical profiles generated by DART-MS were used to train multivariate predictive models for classification of both cultivar and growing location and were validated against traditional sensory evaluation. Overall, our results provide strong proof-of-concept data that support the use of DART-MS for classification of hop cultivar based on the detection of flavor and aroma (volatile and non-volatile) relevant compounds.
About the Speaker:
Dr. Prenni received her Ph.D. in Analytical Chemistry from the University of Colorado, Boulder followed by post-doctoral training at the Scripps Research Institute in La Jolla, CA. She has over 18 years of experience in biological mass spectrometry and served for over ten years at the Director of the Proteomics and Metabolomics Core Facility at Colorado State University (CSU). During this time, her group developed novel approaches in metabolomics for analytical methods and data analysis including the RAMClustR algorithm for metabolite clustering and annotation. Presently, Dr. Prenni is an Associate Professor in the Department of Horticulture at CSU where the overall theme of her research is the application of mass spectrometry to address important issues in food/crop safety and quality. Current projects are focused on the use of mass spectrometry to (1) dissect complex genotype by environment (GxE) interactions, including the microbiome, in plant and animal systems (2) develop novel approaches using ambient ionization for characterization of food quality and authenticity (3) perform sensitive and rapid quantification of drug and chemical residues in food products (4) develop novel methods for metabolomics sample preparation, data acquisition, and informatics.
