基于不同小麦原粮的面包感官评价结果预测模型研究（网络首发、推荐阅读）

收集了66个2021年国产高筋优质小麦样品（涉及12个品种）,分析其理化指标及面包的感官属性。对不同品种小麦进行主成分分析,结果显示,面包感官评分的高低与面筋指数、最大拉伸阻力、搅拌时间、拉伸面积以及稳定时间等存在较强的相关性;同时,通过聚类分析筛选相似度较高的小麦品种,可对烘焙专用面粉的开发和维持生产稳定性起到关键指导作用。对比逐步回归、偏最小二乘和神经网络模型三个方法建立的面包感官评分预测模型拟合质量,结果表明,使用人工神经网络建立的模型拟合性明显优于其他模型。使用神经网络模型,可快速预测不同品种小麦的烘焙特性,保证产品稳定性,也为开发更具有针对性的烘焙专用面粉提供参考。

Established prediction models of bread sensory evaluation results based on data analysis methods(Online First, Recommended Article)

In this paper, 66 samples belonging to 12 varieties of domestic high-gluten wheat in 2021 were collected, and their physicochemical, rheological and bread sensory properties were analyzed. The results of the main component analysis showed that the total sensory score of bread had a strong correlation with gluten index, maximum tensile resistance, mixing time, stretching area and stabilization time, etc. At the same time, the screening of wheat varieties with high similarity through cluster analysis could play a key guiding role in the development and maintenance of production stability of baking special flour. Comparing the fitting quality of the bread total sensory score prediction model established by the three methods of stepwise regression, partial least squares and neural network model, the model built using artificial neural network was significantly better than other models. Using neural network models, the baking characteristics of different varieties of wheat could be quickly predicted, ensuring product stability, while also facilitating the development of more targeted baking special flours.