Interactions between humulinone derived from aged hops and protein Z enhance the foamability and foam stability

Foam is one of the important characteristics of beer, including foamability, foam stability and foam texture. Protein Z (PZ) is considered to be an important component of beer foam. In this study, the interaction between PZ and humulinone, a widespread compound in aged hops, and the effect on foam properties of PZ were investigated. The fluorescence spectra showed that the stoichiometric ratio of humulinone to PZ was 4.25 ± 0.48: 1, and the binding constant was (1.64 ± 0.17) × 105 M-1. MD and FTIR results showed that the main force of interaction between PZ and humulinone was hydrogen bond, and the possible sites were Asn-37, Ser-292, Lys-290 and Pro-395. Moreover, the addition of humulinone greatly reduced the surface tension of PZ solution, and changed the secondary structure of PZ, which is beneficial for the foam stability. Under the influence of humulinone, the foamability, foam stability and foam texture of PZ all increased.

Regulating the Golgi apparatus sorting of proteinase A to decrease its excretion in Saccharomyces cerevisiae

Beer foam stability, a key factor in evaluating overall beer quality, is influenced by proteinase A (PrA). Actin-severing protein cofilin and Golgi apparatus-localized Ca²⁺ ATPase Pmr1 are involved in protein sorting at the trans-Golgi network (TGN) in yeast Curwin et al. (Mol Biol Cell 23:2327-2338, 2012). To reduce PrA excretion into the beer fermentation broth, we regulated the Golgi apparatus sorting of PrA, thereby facilitating the delivery of more PrA to the vacuoles in the yeast cells. In the present study, the cofilin-coding gene COF1 and the Pmr1-coding gene PMR1 were overexpressed in the parental strain W303-1A and designated as W + COF1 and W + PMR1, respectively. The relative expression levels of COF1 in W + COF1 and PMR1 in W + PMR1 were 5.26- and 19.76-fold higher than those in the parental strain. After increases in the expression levels of cofilin and Pmr1 were confirmed, the PrA activities in the wort broth fermented with W + COF1, W + PMR1, and W303-1A were measured. Results showed that the extracellular PrA activities of W + COF1 and W + PMR1 were decreased by 9.24% and 13.83%, respectively, at the end of the main fermentation compared with that of W303-1A. Meanwhile, no apparent differences were found on the fermentation performance of recombinant and parental strains. The research uncovers an effective strategy for decreasing PrA excretion in Saccharomyces cerevisiae.

Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER

There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.