Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer

Ficus hirta Vahl. (FHV) is widely consumed because of its functional and aromatic compounds. The incorporation of adjuncts contributes to the functional and flavor properties of beers. This study aims to enrich FHV extractions to develop beers with satisfactory physicochemical, antioxidant, and sensory characteristics. As a result, beers with 0.1 g/mL (P1) and 0.067 g/mL (P3) FHV extraction showed the highest values of physicochemical properties including °Brix, antioxidant activity, foam, lightness, and color intensity. Electronic nose and tongue results show that the aroma of P1 and taste of P3 were quite different from those of other FHV beers, resulting in substantially high consumer preference. The liking drivers of FHV beers were color appearance, hop and malty odor, sweet and malty flavor, thickness, and carbonation mouthfeel. However, the astringency flavor attribute was the disliking factor for beers. The results of this study may provide some references and guidelines for the development of Ficus hirta Vahl. functional beer to control the physicochemical, antioxidative, and sensory properties of the beer.

The Challenge of Cleaning Woven Filter Cloth in the Beverage Industry—Wash Jets as an Appropriate Solution

Beverage production requires many different and complex unit operations. One crucial procedural step is filtration. Typical filters are filter presses, candle filters, membrane filters, belt filters, and drum filters, which require considerable hygienic precaution and the application of appropriate cleaning concepts. In the last decades, the hygienic design has become a central design feature of equipment in the beverage and food industries. Today, also correspondent concepts regarding filter cloth increasingly come to the fore. However, filter cloth cleaning is rapidly facing limitations. Complex filter geometries originating from different gauzes and sensitive polymeric materials hinder efficient cleaning. Additionally, extensive biological residues adhering to the filter surface increase the challenge of cleaning. The goal of this paper is to outline the cleaning of woven filter cloths systematically with a particular focus on beverages and correspondent biophysical interactions between filter and residue. Based on these elemental cleaning limits of filter cloths, this paper focuses mainly on jet cleaning as one of the most appropriate cleaning methods. The flow-mechanical properties are discussed in detail since these are precisely the parameters that, on the one hand, describe the understanding of the cleaning process and, on the other hand, show how a wash jet can be adjusted precisely. In contrast to conventional cleaning techniques, such wash jets are expeditious to adapt and offer the best prerequisites to enable demand-oriented and optimized cleaning concepts. The latest research and approaches are enhancing jet efficiency and highlight their potentials for future process strategies.

Phenolic Substances in Beer: Structural Diversity, Reactive Potential and Relevance for Brewing Process and Beer Quality

For the past 100 years, polyphenol research has played a central role in brewing science. The class of phenolic substances comprises simple compounds built of 1 phenolic group as well as monomeric and oligomeric flavonoid compounds. As potential anti- or prooxidants, flavor precursors, flavoring agents and as interaction partners with other beer constituents, they influence important beer quality characteristics: flavor, color, colloidal, and flavor stability. The reactive potential of polyphenols is defined by their basic chemical structure, hydroxylation and substitution patterns and degree of polymerization. The quantitative and qualitative profile of phenolic substances in beer is determined by raw material choice. During the malting and brewing process, phenolic compounds undergo changes as they are extracted or enzymatically released, are subjected to heat-induced chemical reactions or are precipitated with or adsorbed to hot and cold trub, yeast cells and stabilization agents. This review presents the current state of knowledge of the composition of phenolic compounds in beer and brewing raw materials with a special focus on their fate from raw materials throughout the malting and brewing process to the final beer. Due to high-performance analytical techniques, new insights have been gained on the structure and function of phenolic substance groups, which have hitherto received little attention. This paper presents important information and current studies on the potential of phenolics to interact with other beer constituents and thus influence quality parameters. The structural features which determine the reactive potential of phenolic substances are discussed.

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.