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The nutritional integrity of wheat is jeopardized by rapidly rising atmospheric carbon dioxide (CO 2 ) and the associated emergence and enhanced virulence of plant pathogens. To evaluate how disease resistance traits may impact wheat climate resilience, 15 wheat cultivars with varying levels of resistance to Fusarium Head Blight (FHB) were grown at ambient and elevated CO 2 . Although all wheat cultivars had increased yield when grown at elevated CO 2 , the nutritional contents of FHB moderately resistant (MR) cultivars were impacted more than susceptible cultivars . At elevated CO 2 , the MR cultivars had more significant differences in plant growth, grain protein, starch, fructan, and macro and micro-nutrient content compared with susceptible wheat. Furthermore, changes in protein, starch, phosphorus, and magnesium content were correlated with the cultivar FHB resistance rating, with more FHB resistant cultivars having greater changes in nutrient content. This is the first report of a correlation between the degree of plant pathogen resistance and grain nutritional content loss in response to elevated CO 2 . Our results demonstrate the importance of identifying wheat cultivars that can maintain nutritional integrity and FHB resistance in future atmospheric CO 2 conditions., (© 2022. The Author(s).)

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R 2 in the range of 0.798-0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021-0.045 and 12.56-46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R 2 in the range of 0.841-0.920. The sphericity of the refractions varied in the range of 0.52-0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R 2 in the range of 0.845-0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions., (© 2021. The Author(s).)

Background: The resistance of proteins to gastrointestinal digestion contributes to their ability to act as allergens. Near-complete digestion of protein in wheat products is important with respect to avoiding the potential immunogenic effects of undigested peptides. Five modern US wheat classes (soft red winter, hard winter, hard red spring, club and durum) including 17 wheat varieties, as well as three ancient wheat classes (spelt, emmer and einkorn) including nine wheat varieties, were analyzed for kernel hardness (KH) and flour protein characteristics, in addition to in vitro protein digestibility (IVPD) of cooked flour, flour without albumins and globulins (FWOAG), gluten, albumins, and globulins, aiming to identify the factors influencing the protein digestibility of flour., Results: IVPDs of flour, FWOAG, gluten and albumins of wheat varieties ranged from 86.5% to 92.3%, 85.8% to 90.3%, 90.6% to 94.6% and 74.8% to 85.1%, respectively. The IVPD of gluten was significantly higher than the IVPDs of flour and FWOAG, indicating that non-protein components substantially affect protein digestibility. Significant differences were observed in IVPDs of flour and albumins among eight wheat classes, but not in the IVPDs of FWOAG, gluten and globulins. There were apparent differences in undigested protein bands and intensities of wheat classes with low and high flour IVPDs. KH and albumin proportion exhibited negative and positive relationships, respectively, with flour IVPD., Conclusion: The results of the present study demonstrate that KH, non-protein components and albumin proportion have a major influence on protein digestion and need to be considered when developing wheat cultivars with higher protein digestibility., (Published 2021. This article is a U.S. Government work and is in the public domain in the USA.)

Background: Interest in alternatives to the traditional wheat Triticum aestivum among farmers, millers, bakers, and consumers is increasing. The Altergrain project aimed to compare the Belgian-soil cultivated Einkorn (1K), Emmer (EMM), Khorasan (KH), and modern wheat (MW) with respect to nutritional values of kernels, breads made from these cereals, and population-level nutrient intake implications., Results: Ancient wheats 1K, EMM, and KH contain lower total carbohydrate content than MW. Further, ancient wheats are higher in both protein and crude ash content. Vitamin E levels in breads prepared using 1K and EMM were higher than those in MW, but those prepared from KH had lower vitamin E levels than MW. Breads prepared using ancient wheats have higher total phenol content (TPC) than those from MW. Baking caused a decrease in vitamin E and TPC in bread prepared from ancient wheat, the exception being the one prepared using KH, which had a higher TPC than MW. When replacing bread made from MW with those made from ancient grains, no differences were observed with respect to conformance with the Belgian Recommend Daily Requirements., Conclusion: Ancient wheats from Belgian soil are as nutritive as MWs even after being processed into bread. At the kernel level, nutritional differences are present, but only small differences are present in terms of nutritional intake when nutrition parameters are calculated for consumed bread. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

Heavy-metal-associated (HMA) isoprenylated plant proteins (HIPPs) only exist in vascular plants. They play important roles in responses to biotic/abiotic stresses, heavy-metal homeostasis, and detoxification. However, research on the distribution, diversification, and function of HIPPs in Triticeae species is limited. In this study, a total of 278 HIPPs were identified from a database from five Triticeae species, and 13 were cloned from Haynaldia villosa . These genes were classified into five groups by phylogenetic analysis. Most HIPPs had one HMA domain, while 51 from Clade I had two, and all HIPPs had good collinear relationships between species or subgenomes. In silico expression profiling revealed that 44 of the 114 wheat HIPPs were dominantly expressed in roots, 43 were upregulated under biotic stresses, and 29 were upregulated upon drought or heat treatment. Subcellular localization analysis of the cloned HIPPs from H. villosa showed that they were expressed on the plasma membrane. HIPP1-V was upregulated in H. villosa after Cd treatment, and transgenic wheat plants overexpressing HIPP1-V showed enhanced Cd tolerance, as shown by the recovery of seed-germination and root-growth inhibition by supplementary Cd. This research provides a genome-wide overview of the Triticeae HIPP genes and proved that HIPP1-V positively regulates Cd tolerance in common wheat.

Accurate identification of crop varieties grown by farmers is crucial, among others, for crop management, food security and varietal development and dissemination purposes. One may expect varietal identification to be more challenging in the context of developing countries where literacy and education are limited and informal seed systems and seed recycling are common. This paper evaluates the extent to which smallholder farmers misidentify their wheat varieties in Ethiopia and explores the associated factors and their implications. The study uses data from a nationally representative wheat growing sample household survey and DNA fingerprinting of seed samples from 3,884 wheat plots in major wheat growing zones of Ethiopia. 28-34% of the farmers correctly identified their wheat varieties. Correct identification was positively associated with farmer education and seed purchases from trusted sources (cooperatives or known farmers) and negatively associated with seed recycling. Farmers' varietal identification thereby is problematic and leads to erroneous results in adoption and impact assessments. DNA fingerprinting can enhance varietal identification but remains mute in the identification of contextual and explanatory factors. Thus, combining household survey and DNA fingerprinting approaches is needed for reliable varietal adoption and impact assessments, and generate useful knowledge to inform policy recommendations related to varietal replacement and seed systems development., Competing Interests: Diversity Arrays Technology is a commercial entity and Andrzej Kilian is the CEO of this company. The funder provided support in the form of salaries for authors [MJ, KT, OE], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. DArT does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG and 150 SDG being used for classification. Thanks to the constructed imaging setup, photos of the 300 wheat grains are obtained. Seventeen visual features of each wheat grain are extracted by image-processing techniques and evaluated in three different groups of dimension, texture and pattern as visual parameters. Artificial bee colony (ABC) optimization-based artificial neural network (ANN) and extreme learning machine (ELM) algorithms are implemented to classify the damaged wheat grains., Results: A correlation-based feature selection (CFS) technique is also utilized to find the most effective among the 17 features. In the classification process using five selected features, the mean absolute error (MAE) and root mean square error (RMSE) values for ABC-based ANN are calculated as 0.00174 and 0.00433 respectively. The proposed technique is integrated into graphical user interface (GUI) software to construct an effective detection system for practical use., Conclusion: The results indicate that, thanks to the modified ANN algorithm and implemented CFS algorithm, the detection accuracy of damaged wheat grains is considerably increased. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

Background: The sustainable management of agricultural resources requires the integration of cutting-edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible-near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total., Results: After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively., Conclusion: The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

The increasing demand for spelt products requires the baking industry to develop accurate and efficient tools to differentiate between spelt and bread wheat grains. We subjected a 272-sample spelt-bread wheat set to several potential diagnostic methods. DNA markers for γ-gliadin-D ( GAG56D), γ-gliadin-B ( GAG56B), and the Q-gene were used, alongside phenotypic assessment of ease-of-threshing and near-infrared spectroscopy (NIRS). The GAG56B and GAG56D markers demonstrated low diagnostic power in comparison to the Q-gene genotyping, which showed full accordance with the threshing phenotype, providing a highly accurate distinction between bread wheat and spelt kernels. A highly reliable Q classification was based on a three-waveband NIR model [Kappa (0.97), R-square (0.93)], which suggested that this gene influences grain characteristics. Our data ruled out a protein concentration bias of the NIRS-based diagnosis. These findings highlight the Q gene and NIRS as important, valuable, but simple tools for distinguishing between bread wheat and spelt.

Background: Zinc (Zn) is essential for all life forms and its deficiency is a major issue of malnutrition in humans. This study was carried out to characterize 28 wheat genotypes of Pakistani origin for grain zinc biofortification potential, genetic diversity and relatedness., Results: There was low genetic differentiation among the tested genotypes. However, they differed greatly in yield-related traits, grain mineral (Zn, calcium (Ca) and protein) concentrations and Zn bioavailability. Zinc application increased the concentration of Zn in wheat grain (32.1%), embryo (19.8%), aleurone (47%) and endosperm (23.7%), with an increase in bioavailable Zn (22.2%) and a reduction in phytate concentration (6.8%). Application of Zn also enhanced grain protein and Ca concentrations. Among wheat genotypes, Blue Silver had the highest concentration of Zn in grain, embryo, aleurone and endosperm, with high bioavailable Zn, while Kohinoor-83 had low phytate concentration., Conclusion: Wheat genotypes of Pakistan are genetically less diverse owing to continuous focus on the development of high-yielding varieties only. Therefore genetically diverse wheat genotypes with high endospermic Zn concentration and better grain yield should be used in breeding programs approaches, aiming at improving Zn bioavailability. © 2018 Society of Chemical Industry., (© 2018 Society of Chemical Industry.)

Background: A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains., Results: Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model., Conclusion: This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry., (© 2016 Society of Chemical Industry.)

The influence of sprouting times and illumination conditions on lipophilic antioxidants (carotenoids, tocochromanols, alkylresorcinols, and steryl ferulates), chlorophylls, and α-amylase activity was investigated using four varieties each of bread wheat (Triticum aestivum ssp. aestivum), spelt (T. aestivum ssp. spelta), durum (T. durum), emmer (T. dicoccum), and einkorn (T. monococcum). Carotenoid levels significantly increased during sprouting, particularly, under light exposure. In contrast, concentrations of other lipophilic antioxidants were affected to a lesser extent. Moreover, the quantitative development of lipophilic antioxidants was evidently determined by genotype. On the basis of the levels of carotenoids newly synthesized during sprouting, a chloroplast development index indicated that chloroplast ontogenesis during sprouting occurred at different species-dependent rates. Thermal degradation of carotenoids, tocochromanols, chlorophylls, and α-amylase activity was observed during the drying of sprouts at 40 and 90 °C, while alkylresorcinol and steryl ferulate levels remained unaffected. Wheat sprouts were shown to be potential functional ingredients to increase the nutritional value of cereal products.

The effects of origin, genotype, harvest year, and their interactions on wheat near infrared (NIR) spectra were studied to find the reasons for differences in NIR fingerprints of wheat from different geographical origins and the stability of NIR fingerprints among different years. Ten varieties were grown in three regions of China for 2 years. 180 kernel samples were analysed by NIR. The spectra after pre-treatment were analysed by principal component analysis, multi-way analysis of variance, and discriminant partial least-squares. The results showed that origin, genotype, year, and their interactions all had significant effects on wheat NIR fingerprints. The second overtones of N-H and C-H stretching vibrations and a combination of stretch and deformation of C-H group in wheat were mainly influenced by the geographical origin. The wavelength ranges 975-990 nm, 1200 nm, and 1355-1380 nm contained plenty of origin information to build robust discriminant models of wheat geographical origin., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Evaluating the merits of the wheat seed is an important significance for wheat breeding. We studied analytic hierarchy process (AHP) for seeds grading by digital image processing techniques in the paper. Firstly, preprocess the collected wheat seed images; extract some parameters, such as area, plumpness, rectangular, and elongation of the seed, and then build the level model. Experiments showed the model is right, and level accuracy rate is more than 95%.

Physicochemical properties of wheat grains with largest kernel thickness always was lowest than the other sections, examination of microstructure of wheat grains can help us understand this phenomena. Two varieties of wheat, soft white winter wheat Yangmai 11 and hard white winter wheat Zhengmai 9023, were fractionated into five sections by kernel thickness. Then the fractionated wheat grains in 2.7-3.0 mm section were separated into three fractions by kernel specific density sequentially. Microstructure of the fractured surface were evaluated at different scale level to two varieties wheat with different kernel thickness and specific density by using environmental scanning electron microscopy. Compactness and size of endosperm cell tended to decrease with decreasing wheat kernel thickness and specific density. Protein matrix tended to increase with decreasing wheat kernel thickness and specific density. Size of starch granules and proportion for different type starch granules also varied with different wheat kernel thickness and specific density. Those microstructure properties of the fractured surface, formation of endosperm cells, protein matrix and starch granules were close related to rheological properties and pasting properties of wheat grains.

It is widely believed that hexaploid wheat originated via hybridization of hulled tetraploid emmer with Aegilops tauschii (genomes DD) and that the nascent hexaploid was spelt, from which free-threshing wheat evolved by mutations. To reassess the role of spelt in the evolution of Triticum aestivum, 4 disomic substitution lines of Ae. tauschii chromosome 2D in Chinese Spring wheat were developed and one of them was used to map the Tg locus, which controls glume tenacity in Ae. tauschii, relative to simple sequence repeat (SSR) and expressed sequence tag loci on wheat chromosome 2D. The segregation of SSR markers was used to assess the presence of Tg alleles in 11 accessions of spelt, both from Europe and from Asia. Ten of them had an inactive tg allele in the D genome and most had an active Tg allele in the B genome. This is consistent with spelt being derived from free-threshing hexaploid wheat by hybridization of free-threshing wheat with hulled emmer. It is proposed that the tetraploid parent of hexaploid wheat was not hulled emmer but a free-threshing form of tetraploid wheat.