Effect of Product Involvement on Panels' Vocabulary Generation, Attribute Identification, and Sample Configurations in Beer.

The aim of this study was to compare the performance of two semi-trained panels with different degrees of self-reported beer involvement in terms of beer consumption pattern. The two panels were beer non-drinkers (indicating willingness to taste beer) and craft-style beer drinkers. Eleven modified beer samples were evaluated during three separate tasks by both panels. The tasks were (1) a vocabulary generation on a sample level, (2) an attribute identification task with a list of attributes to choose from, and (3) a descriptive analysis. The performance of the two panels was evaluated and compared using three parameters, as follows: Descriptive similarity, attribute knowledge similarity, and perceptual similarity. The results showed that the craft-style beer drinkers generated the most precise vocabulary and correctly identified more attributes, compared to the beer non-drinkers. Furthermore, the sample sensory spaces generated by the two panels were different before the training period, but were perceptually similar post training. To conclude, the beer consumption pattern influenced all aspects of panel performance before training, with the craft-style panel performing better than the non-drinkers panel. However, the panels' performance became more similar after a short period of training sessions

Effect of Product Involvement on Panels’ Vocabulary Generation, Attribute Identification, and Sample Configurations in Beer

The aim of this study was to compare the performance of two semi-trained panels with different degrees of self-reported beer involvement in terms of beer consumption pattern. The two panels were beer non-drinkers (indicating willingness to taste beer) and craft-style beer drinkers. Eleven modified beer samples were evaluated during three separate tasks by both panels. The tasks were (1) a vocabulary generation on a sample level, (2) an attribute identification task with a list of attributes to choose from, and (3) a descriptive analysis. The performance of the two panels was evaluated and compared using three parameters, as follows: Descriptive similarity, attribute knowledge similarity, and perceptual similarity. The results showed that the craft-style beer drinkers generated the most precise vocabulary and correctly identified more attributes, compared to the beer non-drinkers. Furthermore, the sample sensory spaces generated by the two panels were different before the training period, but were perceptually similar post training. To conclude, the beer consumption pattern influenced all aspects of panel performance before training, with the craft-style panel performing better than the non-drinkers panel. However, the panels’ performance became more similar after a short period of training sessions

<tex>\beta$</tex>-radiation stress responses on growth and antioxidative defense system in plants : a study with strontium-90 in **Lemna minor**

Abstract: In the following study, dose dependent effects on growth and oxidative stress induced by β-radiation were examined to gain better insights in the mode of action of β-radiation induced stress in plant species. Radiostrontium (90Sr) was used to test for β-radiation induced responses in the freshwater macrophyte Lemna minor. The accumulation pattern of 90Sr was examined for L. minor root and fronds separately over a seven-day time period and was subsequently used in a dynamic dosimetric model to calculate β-radiation dose rates. Exposing L. minor plants for seven days to a 90Sr activity concentration of 25 up to 25,000 kBq·L−1 resulted in a dose rate between 0.084 ± 0.004 and 97 ± 8 mGy·h−1. After seven days of exposure, root fresh weight showed a dose dependent decrease starting from a dose rate of 9.4 ± 0.5 mGy·h−1. Based on these data, an EDR10 value of 1.5 ± 0.4 mGy·h−1 was estimated for root fresh weight and 52 ± 17 mGy·h−

Development and Analysis of an Intensified Batch-Fed Wine Fermentation Process

White wine fermentations are typically performed in an entirely batchwise manner, with yeast nutrients only added at the beginning of fermentation. This leads to slow (2+ weeks) fermentation cycle times, with large capital expenditures required to increase winery processing capacity. Prior attempts to speed fermentations via increasing temperature have resulted in unpalatable wine, and continuous fermentation processing is uneconomical and impractical in the winery setting. In this work, we measured yeast nutrient consumption as a function of fermentation progression at the 300 mL scale, and from this derived an equation to optimize yeast nutrient concentration as a function of fermentation progression. These findings were applied at the pilot scale in 150 L fermentors, which resulted in a 60% cycle time reduction versus “best practices” control fermentations. The resultant wines were compared via GC-MS as well as by a trained sensory panel. Organoleptic analysis found statistically significant, but overall, small differences in sensory characteristics between the control and process intensified wines. This intensified fermentation process shows great promise for fermented beverage producers wishing to maximize equipment utilization and debottleneck wineries or other beverage fermentation facilities

Discrimination of Flavonoids and Red Wine Varietals by Arrays of Differential Peptidic Sensors

The chemical structures and concentrations of an organism's natural products are dependent upon its genome and environmental factors. Examples are the complex metabolite solutions resulting from plant and fermentation processes. Here, we describe sensor arrays composed of supramolecular ensembles that undergo indicator displacement and discriminate selected flavonoids and mixtures thereof: wine varietals. Changes in UV-vis absorbance upon indicator displacement in the array were analyzed using pattern recognition protocols. The flavonoids were differentiated in terms of structure and concentration, while red wines were generally classified by varietals, even from different vintners. The technique highlights the power of differential sensor arrays to classify mixtures by metabolite distribution, even when the natural products are not known.National Science Foundation CHE-0716049Welch Foundation F-1151Chemistr