Beer Fermentation, Modern Fermentation Processes

1953 ◽  
Vol 1 (3) ◽  
pp. 241-245 ◽  
Author(s):  
Ruben. Schneider
Keyword(s):  
Fermentation Processes ◽  
Beer Fermentation

Fusel Alcohols Production in Beer Fermentation Processes

1994 ◽  
Vol 29 (4) ◽  
pp. 303-309 ◽  
Author(s):  
Ana I. García ◽  
Luis A. García ◽  
Mario Díaz
Keyword(s):  
Fermentation Processes ◽  
Beer Fermentation ◽  
Fusel Alcohols

scholarly journals Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 253
Author(s):  
Alexander L. Bowler ◽  
Michael P. Pound ◽  
Nicholas J. Watson
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Fine Tuning ◽  
Sensor Data ◽  
Industrial Scale ◽  
Fermentation Processes ◽  
Training Models ◽  
Beer Fermentation ◽  
Machine Learning Model ◽  
On Line

Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling identification of lagging fermentations or prediction of ethanol production end points. Ultrasonic sensors have previously been used for in-line and on-line fermentation monitoring and are increasingly being combined with machine learning models to interpret the sensor measurements. However, fermentation processes typically last many days and so impose a significant time investment to collect data from a sufficient number of batches for machine learning model training. This expenditure of effort must be multiplied if different fermentation processes must be monitored, such as varying formulations in craft breweries. In this work, three methodologies are evaluated to use previously collected ultrasonic sensor data from laboratory scale fermentations to improve machine learning model accuracy on an industrial scale fermentation process. These methodologies include training models on both domains simultaneously, training models in a federated learning strategy to preserve data privacy, and fine-tuning the best performing models on the industrial scale data. All methodologies provided increased prediction accuracy compared with training based solely on the industrial fermentation data. The federated learning methodology performed best, achieving higher accuracy for 14 out of 16 machine learning tasks compared with the base case model.

Ethnoarchaeological Study of Brewing Technology in Wallaga Region of Western Oromia, Ethiopia

2015 ◽  
Vol 13 (1) ◽  
pp. 99-114 ◽  
Author(s):  
Bula Sirika Wayessa ◽  
Diane Lyons ◽  
Brian Kooyman
Keyword(s):  
Fermentation Process ◽  
Alcoholic Beverages ◽  
Archaeological Ceramics ◽  
Fermentation Processes ◽  
Beer Fermentation ◽  
Beer Brewing ◽  
Brewing Process ◽  
Ceramic Vessels ◽  
Interior Walls

In Wallaga, local beer (farso) is one of the most common alcoholic beverages. The beverage is prepared from cereals such as sorghum, millet, maize and barley and an additive plant known as gesho (Rhamnus prinoides). The beer is fermented in a ceramic jar known as huuroo. The brewing process causes pitting in the interior walls. Because most fermentation processes cause pitting of ceramic vessels, use alteration analysis cannot specifically identify past beer brewing practice. Ethnoarchaeological research of beer fermentation in Wallaga shows that in addition to erosion of interior walls of beer jars, the beer fermentation process results in the deposition of residues on the interior walls of the vessels. This residue from beer brewing is different from residue left by other processes because it includes ingredients not incorporated into other foods. As a result, plant microresidue analysis of archaeological ceramics can help to identify past brewing practices and major ingredients of indigenous beer.

The influence of combined extrusion and fermentation processes on the chemical and biosafety parameters of wheat bran

LWT ◽  
2021 ◽  
Vol 146 ◽  
pp. 111498
Author(s):  
Egle Zokaityte ◽  
Vita Lele ◽  
Vytaute Starkute ◽  
Paulina Zavistanaviciute ◽  
Dovile Klupsaite ◽  
...  
Keyword(s):  
Wheat Bran ◽  
Fermentation Processes

scholarly journals Metabolomic-Based Comparison of Traditional and Industrial Doenjang Samples with Antioxidative Activities

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1377
Author(s):  
Song-Hui Soung ◽  
Sunmin Lee ◽  
Seung-Hwa Lee ◽  
Hae-Jin Kim ◽  
Na-Rae Lee ◽  
...  
Keyword(s):  
Amino Acids ◽  
Statistical Analysis ◽  
Organic Acids ◽  
Biogenic Amines ◽  
Multivariate Statistical Analysis ◽  
Metabolite Profiling ◽  
New Products ◽  
Multivariate Statistical ◽  
Fermentation Processes

Numerous varieties of doenjang are manufactured by many food companies using different ingredients and fermentation processes, and thus, the qualities such as taste and flavor are very different. Therefore, in this study, we compared many products, specifically, 19 traditional doenjang (TD) and 17 industrial doenjang (ID). Subsequently, we performed non-targeted metabolite profiling, and multivariate statistical analysis to discover distinct metabolites in two types of doenjang. Amino acids, organic acids, isoflavone aglycones, non-DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4- one) soyasaponins, hydroxyisoflavones, and biogenic amines were relatively abundant in TD. On the contrary, contents of dipeptides, lysophospholipids, isoflavone glucosides and DDMP-conjugated soyasaponin, precursors of the above-mentioned metabolites, were comparatively higher in ID. We also observed relatively higher antioxidant, protease, and β-glucosidase activities in TD. Our results may provide valuable information on doenjang to consumers and manufacturers, which can be used while selecting and developing new products.

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