Factors predicting malt extract within a single barley cultivar

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024 s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images.

Association and genome analyses to propose putative candidate genes for malt quality traits

BACKGROUND

We studied the genetics of nine malt quality traits using association genetics in a panel of North Dakota, ICARDA, and Ethiopian barley lines. Grain samples harvested from Bekoji in 2011 and 2012 were used.

RESULTS

The mapping panel revealed strong population structure explained by inflorescence-type, geographic origin, and breeding history. North Dakota germplasm were superior in malt quality traits and they can be donors to improve malt quality properties. We identified 106 marker-trait associations (MTAs) for the nine traits, representing 81 genomic regions across all barley chromosomes. Chromosomes 3H, 5H, and 7H contained most of the MTAs (58.5%). Nearly 18.5% of these genomic regions contained two to three malt quality traits. Within ±250 kb of 81 genomic regions, we recovered 348 barley genes, with some potential impacting malt quality. These include invertase, β-fructofuranosidase, α-glucosidase, serine carboxypeptidase, and bidirectional sugar transporter SWEET14-like protein. Eighteen of these genes were also previously reported in the Hordeum Toolbox, and 17 of them highly expressed during the germination process.

CONCLUSION

The results from this study invite further follow-up functional characterization experiments to relate the genes with individual malt quality traits with higher confidence. It also provides germplasm resources for malt barley improvement. © 2018 Society of Chemical Industry.

Adding enzymes to improve the properties of the Korean barley Gwangmaek wort during mashing

This study investigated the properties of the Korean barley Gwangmaek wort and compared them to those of the imported Pilsner. The reducing sugar content of Gwangmaek (52.13 mg/mL) was 13% lower than that of Pilsner. The filtration time for Gwangmaek (53 min) was 2.5 times longer than that of Pilsner. The α-amylase and amyloglucosidase treatments increased the reducing sugar content of Gwangmaek up to that of Pilsner. However, the filtration time (88min) significantly increased after the enzyme treatment. In terms of β-glucan contents measured during the mashing process of Pilsner, Gwangmaek, and α-amylase and amyloglucosidase-treated Gwangmaek, the enzyme-treated Gwangmaek had the highest at 232.23mg/L, followed by Gwangmaek (190.31 mg/L) and Pilsner (82.82mg/L). When β-glucanase was added during mashing, there was no change in reducing sugar content. However, the filtration time significantly decreased from 88 to 29 min, and viscosity also declined from 1.78 to 1.42 cp.

The detection of QTLs in barley associated with endosperm hardness, grain density, grain size and malting quality using rapid phenotyping tools

Using a barley mapping population, 'Vlamingh' × 'Buloke' (V × B), whole grain analyses were undertaken for physical seed traits and malting quality. Grain density and size were predicted by digital image analysis (DIA), while malt extract and protein content were predicted using near infrared (NIR) analysis. Validation of DIA and NIR algorithms confirmed that data for QTL analysis was highly correlated (R (2) > 0.82), with high RPD values (the ratio of the standard error of prediction to the standard deviation, 2.31-9.06). Endosperm hardness was measured on this mapping population using the single kernel characterisation system. Grain density and endosperm hardness were significantly inter-correlated in all three environments (r > 0.22, P < 0.001); however, other grain components were found to interact with the traits. QTL for these traits were also found on different genomic regions, for example, grain density QTLs were found on chromosomes 2H and 6H, whereas endosperm hardness QTLs were found on 1H, 5H, and 7H. In this study, the majority of the genomic regions associated with grain texture were also coincident with QTLs for grain size, yield, flowering date and/or plant development genes. This study highlights the complexity of genomic regions associated with the variation of endosperm hardness and grain density, and their relationships with grain size traits, agronomic-related traits, and plant development loci.