ANALYSIS OF EXISTING TECHNOLOGIES FOR SECONDARY PROCESSING OF GRAIN RAW MATERIALS IN BEER PRODUCTION
Bibliographic record
Abstract
The article reviews and systematizes the main modern technologies for the secondary processing of grain residues generated during beer production. The main attention is paid to brewer's grains as the most massive by-product of brewing. Traditional and innovative approaches to its storage, stabilization, drying, fermentation, bioprocessing, and use in various sectors, including feed production, food industry, bioenergy, and biotechnology, are analyzed. It is shown that the most common use of brewer's grains is in the production of animal feed due to the high content of crude protein, fiber, minerals, and essential amino acids. The article discusses in detail the technologies for drying brewer's grains, which allow to increase their shelf life, prevent microbiological spoilage and ensure convenient transportation. The methods of fermentation and bioprocessing of brewer's grains using microorganisms, enzyme preparations and heat treatment are presented, which contribute to increasing their bioavailability and digestibility in feed. The latest technological solutions used in the EU, the USA, Canada and China, including extrusion, microwave processing, ultrasonic disintegration and combined methods, are separately characterized. The technologies for using brewer's grains as a raw material for the production of biogas, bioethanol, enzymes and dietary fiber are also considered. The paper emphasizes the potential for implementing a circular economy in the brewing industry. The paper emphasizes the potential of implementing a circular economy in the brewing industry, where the recycling of grain raw materials can help reduce waste, reduce the burden on the environment and generate additional economic benefits. The expediency of an in-depth study of local technological practices of brewer's grains processing in Ukrainian craft and industrial breweries, taking into account their technical capabilities, climatic conditions and market demand, is substantiated. Comparative data for wet and dried brewer's grains per 100 g of weight are presented, which demonstrate a significant concentration of proteins (up to 30%), fiber (up to 50%) and micronutrients in the dried product. Potential areas of secondary use of brewer's grains in the food, feed, bioenergy, and chemical industries, in particular as a source of natural proteins, dietary fiber, fermentation substrates, and fertilizers, are revealed. Particular attention is paid to the possibilities of adapting foreign experience to Ukrainian realities, given the limited capacity of craft production. The expediency of introducing technologies for drying and storing brewer's grains to extend their service life is emphasized. The article summarizes the main scientific approaches and offers practical recommendations for producers in order to increase the economic efficiency and innovation of the Ukrainian brewing sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".