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Background: Hexaploid oat (Avena sativa L.) is a commercially important cereal crop due to its soluble dietary fiber β-glucan, a hemicellulose known to prevent cardio-vascular diseases. To maximize health benefits associated with the consumption of oat-based food products, breeding efforts have aimed at increasing the β-glucan content in oat groats. However, progress has been limited. To accelerate oat breeding efforts, we leveraged existing breeding datasets (1,230 breeding lines from South Dakota State University oat breeding program grown in multiple environments between 2015 and 2022) to conduct a genome-wide association study (GWAS) to increase our understanding of the genetic control of beta-glucan content in oats and to compare strategies to implement genomic selection (GS) to increase genetic gain for β-glucan content in oat. Results: Large variation for β-glucan content was observed with values ranging between 3.02 and 7.24%. An independent GWAS was performed for each breeding panel in each environment and identified 22 loci distributed over fourteen oat chromosomes significantly associated with β-glucan content. Comparison based on physical position showed that 12 out of 22 loci coincided with previously identified β-glucan QTLs, and three loci are in the vicinity of cellulose synthesis genes, Cellulose synthase-like (Csl). To perform a GWAS analysis across all breeding datasets, the β-glucan content of each breeding line was predicted for each of the 26 environments. The overall GWAS identified 73 loci, of which 15 coincided with loci identified for individual environments and 37 coincided with previously reported β-glucan QTLs not identified when performing the GWAS in single years. In addition, 21 novel loci were identified that were not reported in the previous studies. The proposed approach increased our ability to detect significantly associated markers. The comparison of multiple GS scenarios indicated that using a specific set of markers as a fixed effect in GS models did not increase the prediction accuracy. However, the use of multi-environment data in the training population resulted in an increase in prediction accuracy (0.61–0.72) as compared to single-year (0.28–0.48) data. The use of USDA-SoyWheOatBar-3 K genotyping array data resulted in a similar level of prediction accuracy as did genotyping-by-sequencing data. Conclusion: This study identified and confirmed the location of multiple loci associated with β-glucan content. The proposed genomic strategies significantly increase both our ability to detect significant markers in GWAS and the accuracy of genomic predictions. The findings of this study can be useful to accelerate the genetic improvement of β-glucan content and other traits. [ABSTRACT FROM AUTHOR]

The β-glucan in oat grain plays an important role in preventing cardiovascular disease. Therefore, it is of great significance to realize the rapid measurement of β-glucan content in oat grain for the rational use of β-glucan. In this study, hyperspectral monitoring models of oat grain β-glucan content were constructed, and the effects of transformation algorithm, sensitive band screening algorithm, and modeling algorithm on the accuracy of the model were studied. The results showed that logarithm(Lg) and standardized normal variables(SNV) transformation can change the value of original spectral reflectance, while first-order differentiation(FD) and second-order differentiation(SD) transformation can also change the changing trend of original spectral reflectance. multivariate scattering correction(MSC), SNV, FD, and SD transformation can improve the correlation between spectral reflectance and oat grain β-glucan content to varying degrees. When using full band spectrum to construct the model, the performance of linear modeling algorithm was better than that of nonlinear modeling algorithm. Compared with uninformative variable elimination(UVE) algorithm, successive projections algorithm(SPA) had better band screening effect. Combining all the modeling results, it was found that MSC and SNV transformation algorithms had good results. Among them, the model based on SNV transformation spectrum and SPA-multiple linear regression(SPA-MLR) reached the highest accuracy, which can realize the hyperspectral monitoring of oat grain β-glucan content. [ABSTRACT FROM AUTHOR]

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The objective of this study was to propose a rapid and nondestructive method for quantitatively detecting the hardness of black highland barley kernels using hyperspectral imaging. Initially, a regression model was established to predict hardness based on β-glucan content. Spectral reflectance within the 400–1000 nm wavelength range was gathered for black highland barley, and six preprocessing techniques were applied. Once preprocessing was completed, three characteristic wavelength screening methods were employed. Finally, three different models were utilized to construct a dependable prediction model for β-glucan content. The results indicated that the one-dimensional convolutional neural network (1D-CNN), in combination with the moving average (MA) preprocessing method, exhibited the best performance. To validate the hardness prediction model, the β-glucan content prediction model was integrated with the hardness regression model. The hardness prediction model attained a coefficient of determination (R2) value of 0.8093 and root mean square error (RMSE) of 0.2643 kg. The visual images exhibit characteristics feature of hardness in different varieties of black highland barley. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of black highland barley. • We established a non-destructive testing method for black highland barley hardness. • Hyperspectral imaging and a one-dimensional convolutional neural network were used. • We verified a linear relationship between black highland barley β-glucan and hardness. • We used black highland barley β-glucan content to determine its hardness indirectly. • The visual images exhibit hardness characteristics feature of black highland barley. [ABSTRACT FROM AUTHOR]

Recent studies have revealed an interest in the composition of beer biomolecules as a colloidal system and their influence on the formation of beer taste. The purpose of this research was to establish biochemical interactions between the biomolecules of plant-based raw materials of beer in order to understand the overall structure of beer as a complex system of bound biomolecules. Generally accepted methods of analytical research in the field of brewing, biochemistry and proteomics were used to solve the research objectives. The studies allowed us to establish the relationship between the grain and plant-based raw materials used, as well as the processing technologies and biomolecular profiles of beer. The qualitative profile of the distribution of protein compounds as a framework for the formation of a colloidal system and the role of carbohydrate dextrins and phenol compounds are given. This article provides information about the presence of biogenic compounds in the structure of beer that positively affect the functioning of the body. A critical assessment of the influence of some parameters on the completeness of beer taste by biomolecules is given. Conclusion: the conducted analytical studies allowed us to confirm the hypothesis about the nitrogen structure of beer and the relationship of other biomolecules with protein substances, and to identify the main factors affecting the distribution of biomolecules by fractions. [ABSTRACT FROM AUTHOR]