Malted barley is a major raw material of beer, as well as distilled spirits and several food products. In the malting process, dry barley kernels are steeped in water which initiates germination and invigorates microbial growth on the kernels. In the present study, field emission scanning electron microscopy (FESEM) was used to visualize the microbial community within the tissues of barley kernels before and after the steeping, with and without Lactobacillus plantarum E76 added as a starter culture. The results show that the community of 10(8)cfu g(-1) on dry, stored barley kernels increased 5-10 fold during the steeping forming a dense biofilm of bacteria and fungi with slimy exopolymeric matrix. FESEM revealed that crevices between the outer epidermis and the testa of sound barley kernels were heavily colonized with microbes, whereas there were only few microbes on the outer surface of the husks, in the aleurone layer or in the endosperm underneath an intact testa layer. The microbes frequently possessed appendages forming bridging them to the kernel and the individual microbial cells to each other. The L. plantarum added to the steeping water reduced the amount of exopolymeric matrix in the biofilm and improved the wort filterability.
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http://dx.doi.org/10.1016/j.fm.2009.02.004 | DOI Listing |
Heliyon
November 2024
Centro de Investigaciones Aplicadas a Polímeros, Departamento de Ciencias de los Alimentos y Biotecnología, Escuela Politécnica Nacional, Quito, 170517, Ecuador.
This study explores the production of bio-composites from barley, wheat, and cassava flours, reinforced with varying ratios of oil palm residues. The research emphasizes principles of circular economy and sustainability. Both flours and reinforcements underwent characterization to elucidate how their physicochemical properties affect the mechanical behavior of the bio-composites.
View Article and Find Full Text PDFInt J Food Microbiol
January 2025
University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, Konrad-Lorenz-Strasse 20, 3430 Tulln an der Donau, Austria; Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, University Road, Belfast BT7 1NN, Northern Ireland, United Kingdom.
The responses to artificial spike inoculation with Fusarium culmorum were compared in 11 Tritordeum lines, two durum wheat cultivars and one naked barley cultivar. Inoculation of Tritordeum spikes led to a significant decrease in spike weight, kernel weight per spike, and kernel weight (by 18, 28, and 16 %, respectively). Durum wheat responded most strongly to inoculation, particularly with regard to spike weight and kernel weight per spike (decrease of 42 % and 53 %, respectively).
View Article and Find Full Text PDFData Brief
December 2024
Mälardalen University, Department of Sustainable Energy Systems, Västerås, Sweden.
Agrivoltaic systems emerge as a promising solution to the ongoing conflict between allocating agricultural land for food production and establishing solar parks. This field experiment, conducted during the spring and summer seasons of 2023, aims to showcase barley production in a vertical agrivoltaic system compared to open-field reference conditions at Kärrbo Prästgård, near Västerås, Sweden. The dataset presented in this article encompasses both barley kernel and straw yields, kernel crude protein levels, starch content in kernels and thousand kernel weight.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Environmental Science, Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, 391760, India.
Sci Rep
September 2024
University of Warmia and Mazury in Olsztyn, ul. Oczapowskiego 11, Olsztyn, 10-710, Poland.
This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis.
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