PSVIII-14 Investigating Variability of Spent Craft Microbreweries Grains for Classification and Incorporation Into Diet Formulation Through Multivariate Analyses

Abstract

Abstract The widespread growth in microbreweries presents a unique byproduct in spent brewer’s grains (SBG). However, incorporation of SBG into livestock production systems requires further consideration due to potential variety in end-products due to beer styles and beer makers. Herein, we aim to characterize and cluster SBG according to nutritional composition to determine if nutrient composition groups in SBG are of nutritional and environmental concern. A total of five breweries with over twenty-nine SBG were collected for a period of three years in Reno, Nevada. Proximate chemical analysis and long chain fatty acid (LCFA) analysis were performed on all SBG. A hierarchical clustering (HC) of principal components (PC; HCPC) was performed on the nutrients to characterize maximum variation explainable through PC, followed by grouping of varietals on the multivariate axes through HC. A Ward’s Linkage with scaled Euclidean distance was utilized for the HCPC. The greatest variance in the nutritional groups was explained through the first two PC (PC1, PC2). For PC1, non-fibrous carbohydrates had correlation values of 40.34% (-0.98), neutral detergent fiber assay with heat stable amylase and expressed exclusive of residual ash and protein (apNDFom) 30.39% (0.73), ether extract 17.32% (0.64) and crude protein (CP) 11.94 % (0.53). For PC2, the nutrient correlations were CP 74.26% (0.81) and apNDFom 23.77% (0.21). Ultimately, the variance explained in the PC represents energy (PC1), and protein (PC2). For the HC on the PC, four main clusters were obtained. For the PC generated from the LCFA, there were no superficial patterns detectable along the multivariate space. The distribution into the clusters represent an overlooked reality when considering SBG as livestock feed highlighting the need for a better classification of the varietals as livestock feed. Better classification of SBG will allow producers to better utilize the feed to maximize both productivity and reduce potential nitrogen runoff.