Alecia M. Kiszonas

Brewer's spent grain is a large, highly nutritious fraction remaining after the brewing of beer. Spent grain is usually used as animal feed, when destinations are closely available. It is also discarded in landfills, leading to economic and environmental concerns. This study explored the use of spent grain to enhance the nutritional content of fresh yellow alkaline noodles while also decreasing spent grain waste. Spent grain from a malting barley variety was produced, oven‐dried, milled, and blended with wheat flour at 5%, 15%, and 25% of spent grain (w/w) to produce noodles. The impact of these test formulations on noodle quality, as well as composition, measured as total arabinoxylans and β‐glucans, was investigated for both raw and cooked noodles. A commercial sample of buckwheat noodles (BW) was also tested for comparison. On average, noodles with 25% spent grain cooked 2 min faster than BW and a wheat control made without the addition of spent grain ( p < 0.05). Spent grain noodles were slightly firmer, had lower cooking loss, and had lower weight increase compared to the control noodles. Portions of 100 g of cooked noodles made with 25% spent grain had 2.2 g more fiber than control noodles, measured as total arabinoxylans and β‐glucans. The inclusion of 25% spent grain is a viable way to enhance noodle fiber content while maintaining noodle quality. Practical Applications: Dried and ground spent grain can be incorporated into noodles, simultaneously improving noodle fiber content and reducing cooking time. Noodles represent a potential new market for spent grain, preventing byproduct waste.

Near‐infrared reflectance spectroscopy (NIRS) provides a nondestructive method for estimating physical and chemical grain properties and is widely used in breeding programs to phenotype traits such as texture, color, moisture, protein, and oil content. Club wheat ( Triticum aestivum subsp. compactum ), a specialty subclass of soft white wheat grown primarily in the Pacific Northwest, is valued for its high break flour yield and suitability for confectionery products. Current milling evaluations are destructive and require more than 200 g of grain per sample, limiting the ability to identify and cull poor‐performing lines in early generations. Using 2850 grain samples collected across five growing seasons, we evaluated the accuracy of NIRS models for predicting total flour yield and break flour yield in a set of club wheat breeding materials. Both traits were predicted with comparable accuracy using local partial least squares regression, with cross‐validation correlations of r = 0.81–0.84. Incorporating samples from a growing season as additional training observations increased validation accuracy for individual years by 13%–16% based on Pearson's correlation coefficients. Spectral calibrations for soft wheat milling yields can effectively identify genotypes with poor milling characteristics, enabling nondestructive screening in early breeding stages and improve the efficiency of enhancing wheat end‐use quality.

Background and Objectives: Non‐alpha‐amylase induced low falling number was identified in 2024 soft white wheat. The objectives of this work were to characterize these low falling number samples and determine the impact on end‐product quality. The results were analyzed to determine differences in flour characteristics that might result in a low falling number. Findings: Analysis suggests that physiochemical changes in starch properties and low protein content may be contributing to the 2024 low falling numbers. Low falling number Japanese sponge cakes were comparable to the control cakes and produced an acceptable product. Conclusions: The falling number method did not correctly determine grain end‐product quality for the 2024 samples. Starch pasting properties identified by the Rapid Visco Analyzer provided significant diagnostic information and will be important for future work. Significance and Novelty: To our knowledge, this is the first characterization of grain with low falling number in the absence of elevated alpha‐amylase activity and the impact on end‐product quality.

BACKGROUND: The rise in popularity of field peas (Pisum sativum) can be linked to their advantageous health and nutritional properties. Field pea seeds, yellow or green, are often consumed as an ingredient after being dehulled, split, and ground into flour. This study investigated the effects of genotype, growing location, and their interaction on milling of peas and on the chemical and physical characteristics of pea seeds by testing eight genotypes of yellow peas grown in four different locations.RESULTS: The growing location influenced the contents of ash, fat, and protein in the seeds, measured by near‐infrared reflectance spectroscopy. A positive correlation was observed between seed weight and surface area, evaluated by image analysis. Seeds were milled with an ultracentrifugal mill for measurement of dehulling and splitting efficiency (DSE), and quantification of coarse flour and fine flour yield. Positive correlations were observed between both DSE and coarse fraction and DSE and flour yield. Genotype and location affected DSE and coarse fraction, with a greater influence from the growing location. Fine flour yield was impacted by pea genotype. The milling traits had significant genotype × location interaction.CONCLUSION: This study demonstrated that genotype and growing location influenced the milling of yellow peas and the fine flour yield. This information can assist breeding programs to select cultivars to achieve a more efficient milling and improve quality and use of yellow peas. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Seed development is complex, influenced by genetic and environmental factors. Understanding proteome profiles at different seed developmental stages is key to improving seed composition and quality. We used label‐free quantitative proteomics to analyze round and wrinkled pea seeds at five growth stages: 4, 7, 12, 15, and days after anthesis (DAA), and at maturity. Wrinkled peas had lower starch content (30%) compared to round peas (47%–55%). Proteomic analysis identified 3659 protein groups, with 21%–24% shared across growth stages. More proteins were identified during early seed development than at maturity. Statistical analysis found 735 significantly different proteins between wrinkled and round seeds, regardless of the growth stage. The detected proteins were categorized into 31 functional classes, including metabolic enzymes, proteins involved in protein biosynthesis and homeostasis, carbohydrate metabolism, and cell division. Cell division‐related proteins were more abundant in early stages, while storage proteins were more abundant later in seed development. Wrinkled seeds had lower levels of the starch‐branching enzyme (SBEI), which is essential for amylopectin biosynthesis. Seed storage proteins like legumin and albumin (PA2) were more abundant in round peas, whereas vicilin was more prevalent in wrinkled peas. This study enhances our understanding of seed development in round and wrinkled peas.The study highlighted the seed growth patterns and protein profiles in round and wrinkled peas during seed development. It showed how protein accumulation changed, particularly focusing on proteins implicated in cell division, seed reserve metabolism, as well as storage proteins and protease inhibitors. These findings underscore the crucial role of these proteins in seed development. By linking the proteins identified to Cameor‐based pea reference genome, our research can open avenues for deeper investigations into individual proteins, facilitate their practical application in crop improvement, and advance our knowledge of seed development.

The protein isolation process produces a protein isolate and a starch byproduct. This research focuses on characterizing the composition, pasting properties, and gel firmness (FM) of starch fractions of five yellow pea genotypes grown in six environments in the state of Washington, USA. Protein isolation yielded 17% protein and 60% starch fractions. The starch fraction had a purity range of 66.0%–84.8%. Pea starch exhibited a strong gel‐forming property. The pasting temperatures (PTs) of the starch fractions from the five pea entries ranged between 73.2 °C and 77.1 °C. Viscosity measurements indicated significant seasonal variations. Specifically, the viscosity parameters for the starches from the 2020 to 2022 seasons were significantly higher than those from the 2021 season. Differences in weather conditions partly contributed to seasonal variations. The 2021 season experienced low rainfall, primarily in May and June, followed by high temperatures during late June and the first 3 weeks of July. The principal component analysis (PCA) of 344 viscosity measurements from the Rapid Visco Analyzer (RVA) test showed a clear separation of the 2021 samples from those of 2020 and 2022, emphasizing the impact of the differences in growing conditions.

As genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of genetic variation, frequently plague attempts to make full use of GBS sequence data. Here we have developed two R scripts to serve as a tool in haplotype determination at loci of interest within biparental populations. One of these scripts, Sparse Tag Allele Caller (STAC), provides both automated calling and visual representations of the data around a locus of interest to assist in rapid data compilation decision‐making. The other script, STAC Integrate, allows automated quality control and logic‐based integration of presence/absence data with SNP data, while also rendering global overviews of recombination and coverage across the genome. These scripts are designed to be used together to maximize the utility of the available data. These tools were validated on a biparental population of wheat that was genotyped through GBS. They successfully enabled haplotype determination of a locus that was difficult to directly genotype, and their systemic accuracy was demonstrated in multiple populations and species. These scripts may serve as a tool for researchers attempting to make better use of GBS and other genetic marker data for both research and crop breeding decisions.

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar ‘Alpowa’, a soft white spring wheat, was crossed to a closely related super soft spring wheat line ‘BC2SS163’ to produce F₆ recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012–2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models. Predictive assessment using cross-validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that performs well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI’s yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80–87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production.