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Direct analytical methods assessing lutein content (e.g., high-performance liquid chromatography [HPLC]) are expensive and time-consuming. Their utilization in broad mapping of genetic resources or selecting among hundreds of breeding lines is limited. Therefore, the development of reliable predictive models based on reflectance spectrometry for assessing lutein content would be very useful. This study aims at finding a rapid and accurate lutein quantification method for wheat breeding purposes. Reference samples were represented by hexaploid, tetraploid, and diploid wheat species (119 accessions) cultivated in 2 years. The species Triticum aestivum L. predominated, with 81 accessions. Two near-infrared (NIR) spectroscopy devices were used to collect visible (VIS) and/or NIR spectra: the model FOSS 6500 with dispersive element and wavelength range 400–2500 nm and the Nicolet Antaris II Fourier transform spectrophotometer with wavelength range 1000–2500 nm. HPLC was used as the reference method for assessing lutein content in wheat grain. Sample grinding and proper NIR and VIS spectral combinations were essential for obtaining sufficient predictive parameters in calibration models. The FOSS 6500 calibration models were the most promising (standard error of prediction 0.46–0.78; ratio of performance to deviation 2.36–2.83 mg/kg). NIR spectral instability of ground samples during 12 days in storage was detected and evoked a significant relative increase in lutein content of about 35% using the FOSS 6500 calibration model. After excluding five time-instable spectral regions, prediction differences of lutein content were markedly eliminated and did not exceed 7% in relative terms during 12 days of storage.

Fusarium head blight (FHB) disease adversely affects grain quality and final yield in small-grain cereals including barley. In the present study, the effect of an artificial infection with Fusarium culmorum and an application of deoxynivalenol (DON) on barley spikes of cultivars Chevron and Pedant during flowering was investigated at grain mid-dough stage (BBCH 73) 10days after pathogen inoculation (10 dai). Proteomic analysis using a two-dimensional differential gel electrophoresis (2D-DIGE) technique coupled with LC-MS/MS investigated 98 protein spots revealing quantitative or qualitative differences between the experimental variants. Protein functional annotation of 93 identified protein spots revealed that most affected functional groups represent storage proteins (globulins, hordeins), followed by proteins involved in carbohydrate metabolism (α-amylase inhibitor, β-amylase, glycolytic enzymes), amino acid metabolism (aminotransferases), defence response (chitinase, xylanase inhibitor, serpins, SGT1, universal stress protein USP), protein folding (chaperones, chaperonins), redox metabolism (ascorbate-glutathione cycle), and proteasome-dependent protein degradation. The obtained results indicate adverse effects of infection on plant proteome as well as an active plant response to pathogen as shown by enhanced levels of several inhibitors of pathogen-produced degradation enzymes (α-amylase inhibitor, xylanase inhibitor, serpins), chaperones, and other stress-related proteins (SGT1, USP). Genotypic differences were found in hordein abundance between Chevron and Pedant.

The grain of six standard wheat cultivars and one high-amylose wheat cultivar was ground and milled to produce common white flour used to prepare bread, biscuits and wafers. Changes in rapidly and slowly digestible starch (RDS and SDS), resistant starch (RS) and fibre caused by different milling and by the preparation of bakery products were observed. Depending on the milling process, RDS ranged from 28.3 to 32.4% of dry matter and SDS from 18.3 to 35.2%. The preparation of bakery products increased RDS by 30–80% to the detriment of SDS. RS comprised on average 6.6% of ground grain, 3.9% of wheat flour and 3.5, 5.9 and 1.0% of bread, biscuits and wafers, respectively. Variance amongst standard wheat cultivars was low, with only the high-amylose wheat showing unique technological and nutritional properties. RS and total starch (TS) were measured using two different methods. TS determinations correlated closely, although the linear regression varied amongst the different matrices. The correlation between RS values measured by the two methods was loose, and no significant linear regression was found. The article brings new information on variability in starch digestibility in cereal products depending on wheat cultivar used for production.

This paper is concerned with rheological properties of native starches isolated from six cultivars of Triticum aestivum L. and from two hulled wheat species belonging to taxon T. dicoccum Schrank and from Triticum spelta L. These results measured using the Mixolab system were compared with those of white flour. The differences identified were caused by protein's elimination and high starch content. Nevertheless, cluster analysis confirmed similar cultivar classification whether based on isolated starch or white flour. Therefore, starch was a significant carrier of typical cultivar rheological properties. Additionally, the contribution of detected starch rheological characteristics for bread-making and predicting cookie quality were analyzed. The cluster analysis was further confirmed by multiple stepwise regression models selecting the best set of flour and starch rheological parameters for testing final bread and cookie quality parameters. All seven calculated regression models included at least one starchy rheological parameter, and their multiple regression coefficients varied in a range from 0.62 to 0.87 for predicting four bread parameters and from 0.62 to 0.80 for predicting three cookie parameters.

Two sets of winter wheat landraces and obsolete cultivars were studied in three-year field experiments, and compared with 3 modern control cultivars. The higher spike productivity in modern cultivars could mainly be attributed to an increased number of grains in a spikelet and harvest index (HI), whereas thousand grain weight (TGW) has only a slight effect. Landraces and old cultivars proved to have a higher content of crude protein. Spike productivity characteristics, except for TGW, showed a negative correlation with the crude protein content in the grain. The number of kernels in a spikelet strongly affected the spike's productivity, whereas the TGW has only half the effect. The mean yield of four modern cultivars was 51% higher than the mean yield of 31 landraces and obsolete cultivars. Regression analysis proved the much stronger response of modern cultivars to environment (b = 1.63), then was the response of old cultivars (b = 0.87). Different responses to environments were found within the set of 31 landraces, as well. We could also identify potentially valuable donors of earliness and winter hardiness among the old cultivars. High crude protein content (up to 18%), and other valuable quality characteristics, were rather frequent. In both sets, HMW Glu-subunits were described, and we have additionally studied 67 selected lines. Among them, 10 lines showed the crude protein content of 17.5% to 18.3% (where the gluten index and Zeleny test varied from 28.5 to 54.0 and 36.8 to 61.7, respectively). High values of all quality characteristics showed lines gained from the cultivars Mindeszentpusztai (HUN), Szekacz 19 (HUN), Bartweizen linie a (AUT), Viglasska cervenoklasa (CZE), as well as some others.

Chip electrophoresis was used for the study of the high-molecular-weight glutenin subunit (HMW-GS) composition of wheat. A model set of samples of Czech wheat varieties was used and HMW-GSs were detected by SDS-PAGE. A part of the common HMW-GSs present in wheat were identified by chip electrophoresis. Chip electrophoresis provides a very good reproducibility of HMW-GS patterns. Moreover this approach is much faster than the conventional SDS PAGE methods requiring several hours for an analysis. Another advantage over traditional gel electrophoresis is lower sample and reagent volume requirements, as well as specialized protein standards for accurate reproducibility and quantification.