The impact of flavor components and their interactions on the sensory perception of beer

The influence of beer fraction interactions, chemical composition, and their combined influence on the sensory and chemical quality of beer was examined. Model beer solutions with varying levels of carbohydrate and protein content and known levels of volatile compounds were prepared. The model beer solutions were evaluated by a trained panel for sensory attributes, while the concentration of the volatile flavor compounds was determined by solid phase microextraction (SPME), solid phase dynamic extraction (SPDE) coupled to gas chromatography mass spectrometry (GC/MS), and stir bar sorptive extraction (SBSE) coupled to gas chromatography flame ionization (GC/FID). Results were modeled by principal component analysis (PCA), and relationships between the instrumental and sensory variables were analyzed by partial least squares regression (PLSR). Results showed that the non-volatile fraction had a significant effect on volatile compound release and aroma perception. PCA showed that differentiation between samples was mainly due to protein content, and PLSR analysis indicated a weak correlation between the sensory and instrumental data.;For instrumental analysis, the performance of SBSE and SPME for beer volatile fraction analysis was compared for various parameters, including linearity, limit of detection, repeatability, and recovery of four compounds (isoamyl acetate, ethyl hexanoate, myrcene, benzaldehyde) typically found in beer. Both methods were characterized by high linearity (r > 0.996) and repeatability (RSD = 1.76 to 10.66%). Higher recoveries were obtained by SBSE, and limits of detection were 1.8 to 2.8 times lower when compared to SPME. Both methods were used to analyze commercial lager beer, and results showed that SBSE has higher recovery efficiency, therefore showing promise for the analysis of beer volatiles.;A headspace SPDE-GC/MS method for the analysis of the volatile fraction of beer was developed and optimized. The optimization step was carried out using two level full factorial and Doehlert designs for the determination of the optimum extraction conditions, and the optimized method was used for the analysis of five commercial beers, and a variety of compounds from different chemical classes were extracted from each sample, reflecting the difference in volatile profile among beers brewed with different raw materials, adjuncts, and brewing conditions.