nacks was made from yellow corn and its blends had contained corn, barley, chickpea, cumin, black cumin, black pepper and lettuce seeds at different levels considerable as lettuce seeds of ingredient and products were defined as physical, chemical and sensory evaluation. The results showed that the chickpea had contained the highest in protein and total lipids 40.60 and 15.50%. The black pepper, chickpea and cumin seeds had contained higher in crude fiber (14.20, 10.71 and 10.50%, respectively). The snacks with lettuce seeds showed that the protein was the highest in group (2) it was 15.04, 16.44 and 17.82 %. Total lipids, crude fiber and ash content were decreased gradually in the snacks from group (1) 4.02, 4.87 and 5.72% and the highest in total lipids in group (2) which contained 4.23, 5.08 and 5.93%, respectively. Hunter color values of snacks control and its different blends from lettuce seeds the group (6) prepared with 60% corn grits and 15, 10 and 5% barley and also 6% lettuce seeds showed that higher in lightness and yellowness till 15% chickpea and nearly control snacks. The highest (WAI) and (WSI) were in groups (1 and 2) respectively made from 40% corn grits and 40, 35, 30 and 25% barley. The sensory properties showed that the extrusion blend (18) made from 20% chickpea and 5% barley the highest acceptability (95%) and nearly control (96%) followed were by 10 and 15% chickpea plus 15 and 10% barley were gave 93.0 and 88.0% during overall acceptability.

The impact of climate on crop production has vital importance. Climate variables affect the different crops during different stages of the growth and the development. This research aims to study the environmental factors affecting the growth and production of barley (Hordeum Sp., Gramineae) in a hydroponic system, to provide information to farmers and decision makers by using Artificial Neural Network (ANN) Model for production prediction. Multilayer feed-forward ANN (fully connected) was used in supervised manner and the training method was the back-propagation algorithm by using MATLAB program. The inputs in the ANN model of barley were: seeds density (kg/m2), lighting duration (h/day), light intensity (Lux), temperature (cº), relative humidity (%) and growing period (days). The outputs were: plant length (cm), yield (kg/m2), protein (%), dry matter (%), and conversion factor. Results revealed that the optimal configuration for the ANN model consisted of four layers (6-25-30-5). The hidden layers had 25 and 30 nodes in the first and second hidden layers respectively for the ANN model. Hyperbolic tangent transfer function was employed in hidden and output layers of the ANN model. The learning rate and the momentum parameter were 0.005 and 0.9 respectively for the ANN model. Iterations were 10000 epochs during training process for the ANN model. The results showed that the variation between target and predicted outputs was small while the correlation coefficient (R) was 0.99. Also, the results revealed that the major parameters affecting on all the outputs were seeds density and the duration of the lighting followed by the other factors i.e. temperature (cº), relative humidity (%), growing period (days) and light intensity (Lux). Seeds density has a higher percent relative importance, on yield, plant length, protein (%), DM (%) and conversion factor equal to 22.8%, 24%, 25%, 24% and 22.8% respectively. The developed ANN model was beneficial tool for barley production prediction. The barley yield prediction could be helpful for farmers, decision makers and planning to manage their crop better by providing a series of recommendations about crops planting and clarifying its impact on changes to these factors under the study in order to avoid losses and reach the best benefit (maximization of yield).

The effect of incorporation of barley whole meal (BWM) whether in ice milk (IM) as skim milk powder (SMP) substitute, in tomato soup (ST) as modified starch substitute and in Egyptian kishk (EK) as wheat flour substitute on their properties was studied. The obtained results reveal that, the substitution with BWM was associated with increases in the fiber, β-glucan, K and Mg contents in all products studied. Whereas, the protein and ash contents increased in ST samples. The freezing point of IM mixes heightened and the specific gravity lowered by BWM addition instead of SMP.  In spite of the stabilizer (CMC) level, the overrun % decreased by substitution of SMP with BWM but the melting resistance increased. Rheologically, the 50% replacement SMP with BWM achieved remarkable increase in the rheological response of IM samples especially in the presence of 0.1% CMC.  At the same time, the replacing of modified starch by 200% of BWM in ST samples resulted in increasing the shear stress value and led to a mix behavior of thixotropy and rheopectic in ST samples. Although the partial or fully replacement of wheat flour with BWM led to reduction in the thixotropy area in EK samples. Organoleptically, IM and EK with 50% BWM and ST with 200% BWM substituted gained the highest sensory scores especially in the absence of CMC in IM samples.