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Effect of feed components on quality parameters of wheat-tef-sesame-tomato based extruded products

机译:饲料组分对小麦 - TEF-番茄挤出产品质量参数的影响

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摘要

Wheat flour is one of the principal ingredients in extruded wheat products. Wheat-based extruded products have relatively low protein and high gluten contents as well as a high glycemic index. Incorporation of nutrient-rich supplements could overcome those limitations. A D-optimal statistical experimental design was used to develop high-value and nutrient-rich extruded products by supplementing wheat flour (WF), with tef flour (TF), sesame protein concentrate (SPC) and tomato powder (TM). Effects of feed compositions on physical and functional properties of the extruded products were evaluated and modeled using an artificial neural network (ANN). SPC contributed to elevate the protein and simultaneously lower the carbohydrate content of the extruded products while TF and TM contributed to improving crude fiber and antioxidant properties. Evaluated physicochemical properties were adequately predicted by the ANN models (R (2) = 0.979-0.998) with root mean square error of less than 0.008. Physical properties and sensorial evaluation correlated well and revealed that TF, SPC and TP addition to wheat flour produced distinct extruded products rich in protein and antioxidants with lowered carbohydrate and gluten contents.
机译:小麦粉是挤出小麦产品中的主要成分之一。小麦挤出产品具有相对低的蛋白质和高谷蛋白含量以及高血糖指数。富含营养素的补充剂的融合可以克服这些限制。通过补充小麦粉(TF),芝麻蛋白浓缩物(SPC)和番茄粉(TM),使用D-最佳统计实验设计通过补充小麦粉(WF),用TEF面粉(TF),芝麻蛋白浓缩物(SPC)和番茄粉(TM)来开发高价值和营养丰富的挤出产品。评估饲料组合物对挤出产物物理和功能性的影响,并使用人工神经网络(ANN)进行建模。 SPC有助于升高蛋白质并同时降低挤出产品的碳水化合物含量,而TF和TM有助于改善粗纤维和抗氧化性能。 ANN模型(R(2)= 0.979-0.998)充分预测了评估的物理化学性质,具有小于0.008的根均方误差。物理性质和情感评估良好相关,并揭示了小麦粉的TF,SPC和TP除了具有降低的碳水化合物和麸质含量的富含蛋白质和抗氧化剂的不同挤出的产品。

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