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Wheat is a basic food raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flour yield determination requires large amounts of samples, costly machines, grinding applications that require a long working time and a considerable amount of workload. In this study, Artificial Neural Network (ANN) approach has been employed to predict flour milling yield. The ANN was designed in the Matlab using such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 yield data and tested with the remaining 15% data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two datasets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardness which reduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146 gave better result. The results proved that the developed ANN model can be used to estimate flour yield using wheat physical properties.

Landraces are significant genetic resources for wheat breeding as they can adapt to their regions of origin. However, for this genetic resource to be used effectively in wheat breeding, it should be screened molecularly for some functional genes. The study used 123 landraces and modern bread wheat varieties grown in Turkey. We screened the genetic materials for the wbm , waxy genes, High Molecular Weight Glutenin Subunits (HMW-GS), and the Lr34 gene, which provides adult plant resistance to rust disease. There were three different alleles for the Glu-A1 locus, six different alleles for the Glu-B1 locus, and five different alleles for the Glu-D1 locus. For the Glu-A1 locus, a null subunit was found in 73 genotypes (59.3%) and that is the most common subunit. 7+8 subunit is the most common alleles (65.8%) in the Glu-B1 locus. In the Glu-D1 locus, 2+12 is the most common (63.4%) subunit associated with poor gluten quality, and 78 genotypes contain this subunit. When the three loci were evaluated, 23 combinations were found among all the genotypes screened. The two combinations include two new subunits (2+12ˈ and 2+12*) whose effects on bread quality have not yet been evaluated. Halbert and Gülümbür-Makas wheat cultivars contain the wbm gene, while six cultivars contain the Lr-34 gene. Six genotypes have only Wx-A1 and Wx-D1 alleles for waxy alleles. The results revealed that the landraces did not contain the genes screened within the scope of the study in terms of functional genes used in wheat breeding. The results indicated that we should use modern cultivars containing target genes in breeding programs when these landraces are used as the parent.

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