Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize

Near infrared reflectance spectroscopy (NIRS) and reference data were used to determine the amylose contents of single maize seeds to enable rapid, effective selection of individual seeds with desired traits. To predict the amylose contents of a single seed, a total of 1069 (865 as calibration set, 204 as validation set) single seeds representing 120 maize varieties were analyzed using chemical methods and performed calibration and external validation of the 150 single seeds set in parallel. Compared to various spectral pretreatments, the regression of partial least squares (PLS) with mathematical treatment of Harmonization showed the final optimization. The single-seed amylose contents showed the root mean square error of calibration (RMSEC) of 2.899, coefficient of determination for calibration (R2) of 0.902, and root mean square error of validation (RMSEV) of 2.948. In external validations, the coefficient of determination in cross-validation (r2), root mean square error of the prediction (RMSEP) and ratio of the standard deviation to SEP (RPD) were 0.892, 2.975 and 3.086 in the range of 20–30%, respectively. Therefore, NIRS will be helpful to breeders for determining the amylose contents of single-grain maize.

Proteomics Variation in Nigella Sativa L. (Rananculaceae) Germplasm

Study revealed a first report of proteomics variation in Nigella sativa L. based on analyzing 32 accessions through SDS-PAGE. Three prominent regions along eight subunits were identified. Intra specific variation was observed low whereas the sharpness of bands was high between first and second regions. It was noted that in second region there was no clear evidence of band formation in N. sativa. Prominent and sharp protein peptide bands were recorded in four accessions, namely PK-020561, PK-020609, PK-020620 and PK-020646. Further investigation of single seeds showed almost similar genetic pattern within the single accession. Five clusters were formed on the basis of Euclidean distance. Cluster-I & II contain 1, 1 accession each, likewise Cluster-III and C-IV contain 2, 2 accessions whereas Cluster-V was found diversified as consisted of 26 accessions. Two accessions PK-020878 and PK-020877 were recommended for polymorphism and crop improvement programs. Bangladesh J. Bot. 50(2): 289-294, 2021 (June)

An Assessment of the Lolium perenne (Perennial Ryegrass) Seedborne Microbiome across Cultivars, Time, and Biogeography: Implications for Microbiome Breeding

Research into the bacterial component of the seed microbiome has been intensifying, with the aim of understanding its structure and potential for exploitation. We previously studied the intergenerational seed microbiome of one cultivar of perennial ryegrass with and without one strain of the commercially deployed fungal endophyte Epichloë festucae var. lolii. The work described here expands on our previous study by exploring the bacterial seed microbiome of different commercial cultivar/Epichloë festucae var. lolii combinations in collections of single seeds from the harvest year 2016. In this dataset, a cultivar effect could be seen between the seed microbiomes from cultivars Alto and Trojan. The bacterial component of the seed microbiome from pooled seeds from a single cultivar/E. festucae var. lolii combination harvested from 13 seed production farms around Canterbury in the year 2018 was also studied. This dataset allows the effect of different production locations on the bacterial seed microbiome to be examined. By comparing the two sets of data, bacteria from the genera Pantoea, Pseudomonas, Duganella, Massilia, and an unknown Enterobacteriaceae were observed to be in common. This core bacterial microbiome was stable over time but could be affected by supplemental taxa derived from the growth environment of the parental plant; differing microbiomes were seen between different seed production farms. By comparison to a collection of bacterial isolates, we demonstrated that many of the members of the core microbiome were culturable. This allows for the possibility of exploiting these microbes in the future.

Development and characterization of an EMS-mutagenized wheat population and identification of salt-tolerant wheat lines

Background Triticum aestivum (wheat) is one of the world’s oldest crops and has been used for >8000 years as a food crop in North Africa, West Asia and Europe. Today, wheat is one of the most important sources of grain for humans, and is cultivated on greater areas of land than any other crop. As the human population increases and soil salinity becomes more prevalent, there is increased pressure on wheat breeders to develop salt-tolerant varieties in order to meet growing demands for yield and grain quality. Here we developed a mutant wheat population using the moderately salt-tolerant Bangladeshi variety BARI Gom-25, with the primary goal of further increasing salt tolerance. Results After titrating the optimal ethyl methanesulfonate (EMS) concentration, ca 30,000 seeds were treated with 1% EMS, and 1676 lines, all originating from single seeds, survived through the first four generations. Most mutagenized lines showed a similar phenotype to BARI Gom-25, although visual differences such as dwarfing, giant plants, early and late flowering and altered leaf morphology were seen in some lines. By developing an assay for salt tolerance, and by screening the mutagenized population, we identified 70 lines exhibiting increased salt tolerance. The selected lines typically showed a 70% germination rate on filter paper soaked in 200 mM NaCl, compared to 0–30% for BARI Gom-25. From two of the salt-tolerant OlsAro lines (OA42 and OA70), genomic DNA was sequenced to 15x times coverage. A comparative analysis against the BARI Gom-25 genomic sequence identified a total of 683,201 (OA42), and 768,954 (OA70) SNPs distributed throughout the three sub-genomes (A, B and D). The mutation frequency was determined to be approximately one per 20,000 bp. All the 70 selected salt-tolerant lines were tested for root growth in the laboratory, and under saline field conditions in Bangladesh. The results showed that all the lines selected for tolerance showed a better salt tolerance phenotype than both BARI Gom-25 and other local wheat varieties tested. Conclusion The mutant wheat population developed here will be a valuable resource in the development of novel salt-tolerant varieties for the benefit of saline farming.

Visible + Near Infrared Spectroscopy as taxonomic tool for identifying birch species

The genus L. is composed of several species, which are difficult to distinguish in the field on the basis of morphological traits. The aim of this study was to evaluate the taxonomic importance of using visible + near infrared (Vis + NIR) spectra of single seeds for differentiating Roth and Ehrh. Seeds from several families (controlled crossings of known parent trees) of each species were used and Vis + NIR reflectance spectra were obtained from single seeds. Multivariate discriminant models were developed by Orthogonal Projections to Latent Structures – Discriminant Analysis (OPLS-DA). The OPLS-DA model fitted on Vis + NIR spectra recognized with 100% classification accuracy while the prediction accuracy of class membership for was 99%. However, the discriminant models fitted on NIR spectra alone resulted in 100% classification accuracies for both species. Absorption bands accounted for distinguishing between birch species were attributed to differences in color and chemical composition, presumably polysaccharides, proteins and fatty acids, of the seeds. In conclusion, the results demonstrate the feasibility of NIR spectroscopy as taxonomic tool for classification of species that have morphological resemblance.BetulaBetula pendulaBetula pubescensB. pubescensB. pendula

STUDY OF MASS CONDUCTIVITY PROPERTIES OF SEEDS LAYER

Data on the mass conductivity coefficient have been obtained during drying the onion seeds in a thick layer of 5 mm thick ventilated on the surface, which were calculated by the zonal method on the basis of experimental curves of drying and heating. These data are described by the dependence of the mass conductivity coefficient on temperature and moisture content of the material. The comparison of the mass conductivity coefficient values for a layer and for single seeds is given, which shows that: 1) the mass conductivity coefficient in the layer changes significantly during drying, therefore, this change must be taken into account; 2) the change in the mass conductivity coefficient is due to its dependence on both moisture content and temperature, but the influence of temperature prevails over the influence of moisture content, therefore, themass conductivity coefficient increases during drying, 3) the mass conductivity coefficient in the layer is two orders of magnitude higher than for single seeds. The curve of seeds drying in a layer of 5 mm thick have been calculated using the obtained data on the mass conductivity coefficient for the process of oscillating infrared seeds drying carried out at a material temperature fluctuation in the range from tmin = 34 °C to tmax = 40 °C, which showed satisfactory agreement of the results of calculation and experiment. Experimental and calculated curves of oscillating infrared seeds drying in a layer of 5 mm thick are compared with analogous curves of drying the seeds in a monolayer, which showed that, despite the fact that the mass conductivity coefficient of seeds in a layer of 5 mm thick is two orders of magnitude greater than in a monolayer, the drying of seeds in the first case is slower because of the greater thickness of the layer. To calculate the kinetics of the process of oscillating infrared seeds drying in a dense layer, when its surface temperature oscillates in the range from tmin = 34 °C to tmax = 40 °C, the zonal method is recommended using the obtained data on the mass conductivity coefficient, taking into account the change in the mass conductivity during the process.Forcitation:Rudobashta S.P., Zueva G.A., Dmitriev V.M. Study of mass conductivity properties of seedslayer. Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol. 2017. V. 60. N 7. P. 72-77.