To compete in the energy market, biofuel feedstocks need to be high yielding and carbon neutral or negative. Further, to avoid competition with existing food production systems, these crops will need to be grown on marginal lands with few inputs. Such requirements require the introduction of novel traits to increase resistance to abiotic stress associated with marginal soils and enhanced tolerance to seasonal droughts and heat. The Prenni Lab is part of a large interdisciplinary team that is using a systems approach to dissect complex genotype by environment (G x E) interactions, including the microbiome, in one of the most promising lignocellulosic feedstocks: Sorghum bicolor. Specifically, our group is focused on characterization of the metabolite phenotype of the plant tissue and integration of this data with environmental data (microbiome) and as well as agronomic outcomes. The results of this project will lead to translational strategies for enhancing growth and sustainability of sorghum through improved genetic and microbial adaptations to water and nutrient limited environments.
Learn more at https://sorghumsysbio.org/
Ambient Ionization Mass Spectrometry
The rapid nature of ambient ionization makes it an attractive technology for applications in food analysis. Specifically, our laboratory is using this approach to generate molecular fingerprints which can be coupled with sensory or other quality metrics to train machine learning algorithms for the creation of robust predictive models. We are currently using two types of ambient mass spectrometry: Rapid Evaoporative Ionization Mass Spectrometry (REIMS) and Direct Analysis in Real Time (DART). As an example, REIMS has been used to generate predictive models for quality attributes in beef and lamb such as tenderness, flavor intensity, production history, and grade.