In the present study, the objective was to evaluate the effects of barley kernel size, grind level, and extract solvent on the antioxidant activities associated with total phenolic content and phenolic acid compositions. Three barley varieties (Kindred, Azure, and Tradition), were used and the results showed that with the exception of superoxide anion radical scavenging activity, thin kernel size fraction (2.0 mm) showed much higher antioxidant activities and total phenolic content than those in the 2.4 mm and 2.8 mm fractions when averaged across barley cultivar, grind level, and extract solvent. A similar trend was found for individual phenolic acid compositions among kernel sizes. Simple correlation analysis revealed that total phenolic content showed strong correlation with DPPH· scavenging activity, ABTS + scavenging activity, and reducing power. Generally, fine grind and 80% acetone extract showed averaged highest values in antioxidant activities, total phenolic content, and individual phenolic acid compositions. Stepwise linear regression showed that extract solvent was the most important factor for DPPH· scavenging activity, ABTS + scavenging activity, reducing power, and iron chelating activity. The sum of syringic acid and caffeic acid exhibited a dominant role in explaining the major variation in antioxidant activities except for superoxide anion radical scavenging activity.

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.

The phenolic content and antioxidant potential of malting barley are important in brewing. The objective of our study was to investigate the effects of barley genotype, growing environment, and malting conditions on the total phenolic content and antioxidant activities of malting barley grown in North America. Eight barley cultivars grown at three locations over three years were used. For the malting study, a single barley cultivar, separated into different kernel size fractions, was germinated for various periods of time and then processed by kilning or freeze-drying. Total phenolic content (TPC) and some of the antioxidant activities, including DPPH, ABTS, and superoxide anion radical scavenging activities, reducing power, and iron (II) chelating activity, were significantly impacted by the barley genotype, growth environment, and their interactions. The TPC and most antioxidant activities were also influenced by malting conditions and were generally higher in the malted barleys of the thin kernel size fraction, four-day germination, and in samples processed by kilning, when compared to the plumper kernel size fractions, one and two days of germination, and freeze-dried samples, respectively. There were interactions between malting parameters, and stepwise regression analysis was used to suggest the contribution of each parameter to the TPC and antioxidant activities.

The quality of barley seeds determines the quality and flavor aspects of malts and beers, and the purity of barley seeds is one of the primary considerations in the malting process. Visual discrimination between barley varieties is difficult and requires a barley specialist with intensive experience and years of training. Therefore, computational and automatic methods are in great demand to efficiently and effectively evaluate barley seed purity among different varieties. By using digital images, this research work developed a novel, automated, deep learning-based approach to accurately classify barley seeds. It implemented and compared different artificial neural networks for the classification problem based on the shape, color, and texture attributes of the barley seed. Data augmentation and transfer learning strategies were integrated into the deep convolutional networks to maximize the model’s performance and accuracy. The results demonstrate the feasibility and effectiveness of automatic classification of barley seeds with high validation accuracy and test accuracies at 95.71% and 95.70%, respectively.

OpenMP has been quickly evolving to meet the insatiable demand for productive parallel programming on high performance computing systems. Creating a robust and optimizing OpenMP compiler has become increasingly challenging due to the expanding capabilities and complexity of OpenMP, especially for its latest 5.0 release. Although OpenMP’s syntax and semantics are very similar between C/C++ and Fortran, the corresponding compiler support, such as parsing and lowering are often separately implemented, which is a significant obstacle to support the fast changing OpenMP specification. In this paper, we present the design and implementation of a standalone and unified OpenMP parser, named ompparser, for both C/C++ and Fortran. ompparser is designed to be useful both as an independent tool and an integral component of an OpenMP compiler. It can be used for syntax and semantics checking of OpenMP constructs, validating and verifying the usage of existing constructs, and helping to prototype new constructs. The formal grammar included in ompparser also helps interpretation of the OpenMP standard. The ompparser implementation supports the latest OpenMP 5.0, including complex directives such as metadirective. It is released as open-source from https://github.com/passlab/ompparser with a BSD-license. We also demonstrate how it is integrated with the ROSE’s open-source OpenMP compiler.