Quality characteristics of bean paste as a confectionery ingredient and recent breeding efforts of common beans in Japan.

Bean paste is a confectionery ingredient originating in Asia made from cooked beans and sugar. In Japan, bean paste-containing products play an important role in the traditional confectionery industry. Common beans (Phaseolus vulgaris L.) are used for making white bean paste, and the tebou market class is dedicated to white paste production. Bean paste qualities include paste yield, color, stickiness, smoothness, aroma and flavor. High paste yield, whiteness and smoothness are preferred. The ideal stickiness depends on the final product to be made using bean paste. In terms of aroma and flavor, high sweetness and low beaniness are generally desired. Most of the paste qualities can only be measured by preparing bean paste, which is labor intensive and low throughput. Yuki and Kinu tebou bean varieties were developed in this manner because the highest end-use quality is indispensable to domestic varieties. Tebou bean breeding in Japan is at the stage where more research is necessary to develop faster screening methods to predict important paste quality attributes. This review summarizes the literature on research on white bean paste quality and common bean breeding efforts conducted so far written either in English or Japanese, covering: (1) bean paste production and ingredient sources, (2) the selection criteria and methods used by Japanese breeders and (3) the resulting varieties developed for bean paste. © 2019 Society of Chemical Industry.

Dietary pattern and diversity analysis using DietDiveR in R: a cross-sectional evaluation in the National Health and Nutrition Examination Survey.

BACKGROUND: There are few resources available for researchers aiming to conduct 24-h dietary record and recall analysis using R. OBJECTIVES: We aimed to develop DietDiveR, which is a toolkit of functions written in R for the analysis of recall or record data collected with the Automated Self-Administered 24-h Dietary Assessment Tool or 2-d 24-h dietary recall data from the National Health and Nutrition Examination Survey (NHANES). The R functions are intended for food and nutrition researchers who are not computational experts. METHODS: DietDiveR provides users with functions to 1) clean dietary data, 2) analyze 24-h dietary intakes in relation to other study-specific metadata variables, 3) visualize percentages of energy intake from macronutrients, 4) perform principal component analysis or k-means clustering to group participants by similar data-driven dietary patterns, 5) generate foodtrees based on the hierarchical food group information for food items consumed, 6) perform principal coordinate analysis taking food grouping information into account, and 7) calculate diversity metrics for overall diet and specific food groups. DietDiveR includes a self-paced tutorial on a website (https://computational-nutrition-lab.github.io/DietDiveR/). As a demonstration, we applied DietDiveR to a demonstration data set and data from NHANES 2015-2016 to derive a dietary diversity measure of nuts, seeds, and legumes consumption. RESULTS: Adult participants in the NHANES 2015-2016 cycle were grouped depending on the diversity in their mean consumption of nuts, seeds, and legumes. The group with the highest diversity in nuts, seeds, and legumes consumption had 3.8 cm lower waist circumference (95% confidence interval: 1.0, 6.5) than those who did not consume nuts, seeds, and legumes. CONCLUSIONS: DietDiveR enables users to visualize dietary data and conduct data-driven dietary pattern analyses using R to answer research questions regarding diet. As a demonstration of this toolkit, we explored the diversity of nuts, seeds, and legumes consumption to highlight some of the ways DietDiveR can be used for analyses of dietary diversity.

Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.

Dry bean is a nutrient-dense food targeted in biofortification programs to increase seed iron and zinc levels. The underlying assumption of breeding for higher mineral content is that enhanced iron and zinc levels will deliver health benefits to the consumers of these biofortified foods. This study characterized a diversity panel of 275 genotypes comprising the Yellow Bean Collection (YBC) for seed Fe and Zn concentration, Fe bioavailability (FeBio), and seed yield across 2 years in two field locations. The genetic architecture of each trait was elucidated via genome-wide association studies (GWAS) and the efficacy of genomic prediction (GP) was assessed. Moreover, 82 yellow breeding lines were evaluated for seed Fe and Zn concentrations as well as seed yield, serving as a prediction set for GP models. Large phenotypic variability was identified in all traits evaluated, and variations of up to 2.8 and 13.7-fold were observed for Fe concentration and FeBio, respectively. Prediction accuracies in the YBC ranged from a low of 0.12 for Fe concentration, to a high of 0.72 for FeBio, and an accuracy improvement of 0.03 was observed when a QTN, identified through GWAS, was used as a fixed effect for FeBio. This study provides evidence of the lack of correlation between FeBio estimated in vitro and Fe concentration and highlights the potential of GP in accurately predicting FeBio in yellow beans, offering a cost-effective alternative to the traditional assessment of using Caco2 cell methodologies.

Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

Food Industry Views on Pulse Flour-Perceived Intrinsic and Extrinsic Challenges for Product Utilization.

Pulses such as beans, chickpeas, peas, and lentils are typically consumed whole, but pulse flours will increase their versatility and drive consumption. Beans are the most produced pulse crop in the United States, although their flour use is limited. To expand commercial applications, knowledge of pulse flour attributes important to the food industry is needed. This research aimed to understand the food industry's needs and barriers for pulse flour utilization. An online survey invitation was sent via direct email to individuals employed in food companies developing wheat flour products. A survey weblink was distributed by pulse commodity boards to their membership. Survey questions asked food manufacturers about intrinsic factors of pulse flours that were satisfactory or challenging, and extrinsic factors for use such as market demand. Of the 75 complete responses, 21 currently or had previously used pulse flours in products, and 54 were non-users of pulse flours. Ten users indicated that there were challenges with pulse flours while five did not. Two of the most selected challenges of end-product qualities were flavor and texture. Over half of the respondents were unfamiliar with bean flour. Increasing awareness of bean flours and their attributes coupled with market demand for pulse flour-based products may be the most important extrinsic factors to increasing use among food manufacturers rather than supply or cost.

Seed coat color genetics and genotype × environment effects in yellow beans via machine-learning and genome-wide association.

Common bean (Phaseolus vulgaris L.) is consumed worldwide, with strong regional preferences for seed appearance characteristics. Colors of the seed coat, hilum ring, and corona are all important, along with susceptibility to postharvest darkening, which decreases seed value. This study aimed to characterize a collection of 295 yellow bean genotypes for seed appearance and postharvest darkening, evaluate genotype × environment (G × E) effects and map those traits via genome-wide association analysis. Yellow bean germplasm were grown for 2 yr in Michigan and Nebraska and seed were evaluated for L*a*b* color values, postharvest darkening, and hilum ring and corona colors. A model to exclude the hilum ring and corona of the seeds, black background, and light reflection was developed by using machine learning, allowing for targeted and efficient L*a*b* value extraction from the seed coat. The G × E effects were significant for the color values, and Michigan-grown seeds were darker than Nebraska-grown seeds. Single-nucleotide polymorphisms (SNPs) were associated with L* and hilum ring color on Pv10 near the J gene involved in mature seed coat color and hilum ring color. A SNP on Pv07 associated with L*, a*, postharvest darkening, and hilum ring and corona colors was near the P gene, the ground factor gene for seed coat color expression. The machine-learning-aided model used to extract color values from the seed coat, the wide variability in seed morphology traits, and the associated SNPs provide tools for future breeding and research efforts to meet consumers' expectations for bean seed appearance.

Elucidation of the low resistant starch phenotype in Phaseolus vulgaris exhibited in the yellow bean Cebo Cela.

Dry beans(Phaseolus vulgaris) are rich in complex carbohydrates including resistant starch (RS). RS, the starch fraction that escapes digestion, typically ranges from 35% in raw beans to 4% in cooked beans. A low RS bean genotype, Cebo Cela, was identified with 96% less RS (1.5% RS) than normal raw beans. The goal of this research was to elucidate the factors responsible for this low RS phenotype. The low RS phenotype was evaluated in whole bean flour and starch in Cebo Cela (yellow), Canario (yellow), Alpena (navy) and Samurai (otebo). α-Amylase activation was found to be a major contributor of the low RS content phenotype of the whole bean flour for Cebo Cela (-21.9% inhibition). Total starch (43.6%-40.2%), amylose (31.0%-31.5%), molecular weight and chain length distributions of amylose and amylopectin did not contribute to the low RS phenotype. Yellow bean starches were digested nearly 1.5 times (95%-94%) faster than starch granules from otebo and navy beans (65%-73%) due to lower proportions of amylopectin chains. PRACTICAL APPLICATION: This study is of value to the food industry because the yellow bean, Cebo Cela, is easily hydrolyzed by α-amylase and also has α-amylase promotion properties. Therefore, Cebo Cela can be used as an alternate starch source for ethanol fermentation and for the production of maltodextrins and fructose/glucose syrups which are used as food thickeners and sweeteners.

Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves.

PREMISE: Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines (Vitis spp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves. METHODS: Using homologous universal landmarks found in grapevine leaves, we modeled various morphological features as polynomial functions of leaf nodes. The resulting functions were used to reconstruct modeled leaf shapes across the shoots, generating composite leaves that comprehensively capture the spectrum of leaf morphologies present. RESULTS: We found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict the species identity of previously unassigned grapevines, which were verified with genotyping. DISCUSSION: Observations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape-an assemblage of modeled leaf snapshots across the shoot-is a better representation of the dynamic and essential shapes of leaves, in addition to serving as a better predictor of species identity than individual leaves.