School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
Food Res Int. 2023 Dec;174(Pt 1):113501. doi: 10.1016/j.foodres.2023.113501. Epub 2023 Sep 23.
30 mainstream wheat breeds from China and 2 from Australian were evaluated to analyze the correlation between grain quality traits, protein/starch properties and the comprehensive quality of fine dried noodles (FDN), with a multiple regression analysis conducted to establish predictive equations. Results showed FDN quality was both determined by the protein content and quality, as well as the starch properties, especially pasting characteristics. The balance between gluten strength and starch swelling characteristics was a key point to produce high quality FDN. Zhoumai32 and APW were found to be excellent cultivars for FDN production. Gluten content and index, SDS sedimentation value, dough extensibility, setback and peak viscosity could be served as indicators for specializing FDN flour. The established predictive equations could well explain over 60% of the variation in noodle color, cooking time, hardness, chewiness, and extensibility. These results were hoped to be a fundamental step towards developing the related standards or regulations for specializing FDN flour and rapid noodle quality prediction.
对来自中国的 30 个主流小麦品种和 2 个澳大利亚品种进行了评估,以分析谷物品质特性、蛋白质/淀粉特性与精制干面条(FDN)综合品质之间的相关性,采用多元回归分析建立预测方程。结果表明,FDN 品质既取决于蛋白质含量和质量,也取决于淀粉特性,尤其是糊化特性。面筋强度和淀粉膨胀特性之间的平衡是生产高质量 FDN 的关键。发现周麦 32 和 APW 是生产 FDN 的优良品种。谷朊粉含量和指数、SDS 沉降值、面团延伸性、回落值和峰值黏度可用作 FDN 面粉专用的指标。所建立的预测方程可以很好地解释面条颜色、烹饪时间、硬度、咀嚼性和延伸性变化的 60%以上。这些结果有望成为制定 FDN 面粉专用相关标准或法规以及快速面条质量预测的基础步骤。