Quality of brewing malt depending on the storage conditions of barley

KHOKONOVA, M.B.; Карашаева, А С; Завалин, А. А.

doi:10.3103/s1068367415060099

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…Although four two-row entries had GE of above the acceptable standard, on average, six-row entries performed better than the two-row entries. Khokonova et al (2015) stated that barley grains obtain the energy necessary for germination, which is crucial for mating purpose, during their post-harvest duration.…”

Section: Germination Energymentioning

confidence: 99%

Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models

Hashemi¹,

Zandvakili²,

Shureshjani³

et al. 2019

JAS

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Unlike feed barley, malting barley must meet a specific set of quality standards for acceptability by maltsters. Multiple quality criteria in addition to the grain yield makes ranking of genotypes challenging. The objective of this study was to apply data envelopment analysis (DEA) models to rank the efficiencies of 27 winter barley entries based on grain yield and quality indices. Four methods of DEA including Charnes, Cooper and Rhodes (CCR), Färe and Grosskopf (FG), Banker, Charnes and Cooper (BCC), and Seiford and Thrall (ST) used for the ranking. Testing trial included 14 two-rows and 13 six-rows winter barley. All entries except two, demonstrated high winter survival ratings. Overall, the six-row cultivars out-yielded the two-row cultivars by 18%. However, in terms of brewing quality, the two-row entries performed better than six-row entries and had 40% lower thinness, 12% higher plump, and lower grain protein content. The six-row entries had 32% higher germinative energy than two-row entries. The ranking by four models were not similar, however, SU-Mateo and Calypso had the highest efficiency (1.0) by all four models followed by Wintmalt and Vincenta.

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Section: Germination Energymentioning

confidence: 99%

Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models

Hashemi¹,

Zandvakili²,

Shureshjani³

et al. 2019

JAS

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“…Работы Хоконова М.Б., Кагермазовой А.Ч. также были посвящены изучению влияния несоложеного ячменя на качество солода и выход экстракта [6]. Результаты их исследований свидетельствуют, что увеличение продолжительности термической обработки ячменя повышает выход экстракта, причем увеличение продолжительности с 45 до 60 мин влияет незначительно на количество выделяемого экстракта, а увеличение продолжительности термической обработки несоложеного затора с 15 до 30 мин способствует повышению содержания в сусле редуцирующих веществ, а при увеличении до 60 мин наблюдается снижение вязкости, содержания общего, аминного азота и незначительный рост редуцирующих веществ.…”

Section: − повышение коллоидной и вкусовой стойкости пива;unclassified

Prospects of Using Non-Traditional Raw Material in Brewing Technology: Domestic and Foreign Experience

Кретова¹,

Kretova²

2017

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Перспективы использования нетрадиционного сырья в технологии пивоварения: отечественный и зарубежный опыт Вестник ЮУрГУ. Серия «Пищевые и биотехнологии». 2017. Т. 5, № 4. С. 12-18 Кретова Ю.И. Перспективы использования нетрадиционного сырья в технологии пивоварения: отечественный и зарубежный опыт Вестник ЮУрГУ. Серия «Пищевые и биотехнологии». 2017. Т. 5, № 4. С. 12-18 Кретова Ю.И. Перспективы использования нетрадиционного сырья в технологии пивоварения: отечественный и зарубежный опыт

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“…Studies based on implementation of traditional methods for evaluating malt aromas to identify malt types have shown them to be time-consuming and requiring use of costly equipment and trained personnel [33,34]. Accomplishing this task using a non-invasive electronic nose (e-nose) system may be of great interest within the brewing industry because malts, as one of the vital raw materials, significantly impact the beer quality and the brewing process [35]. However, achieving this presents a nontrivial classification task because, as is the case with most aromatic compounds, the instrumental odor characteristics of a malt sample may overlap even if their aroma profiles may seem different for human olfaction [36,37].…”

Section: Introductionmentioning

confidence: 99%

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Vanarse¹,

Osseiran²,

Rassau

et al. 2022

Sensors

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Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

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Quality of brewing malt depending on the storage conditions of barley

Cited by 5 publications

References 1 publication

Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models

Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models

Prospects of Using Non-Traditional Raw Material in Brewing Technology: Domestic and Foreign Experience

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

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