Texture is one of the main characteristics involved in the acceptance of yogurt and must be monitored for the quality control of the product and for the adequate layout of the processing units. However, the determination of these properties requires expensive equipment, such as rotational rheometers, inaccessible to many industries. Thus, the modeling of artificial neural networks (ANNs) was applied to predict the texture properties of yogurt (output) based on changes in formulation and process conditions (inputs). Non-fat yogurts were produced with different centrifugation conditions and concentrations of protein and enzyme transglutaminase. Three models were developed: ANN-TPA to predict the properties obtained in the analysis of the texture profile (TPA), and ANN-TIX and ANN-VIS to predict thixotropy and viscosity, respectively. The enzymatic and protein variables had an impact of 46.3% and 53.6%, respectively, on the response of ANN-TPA. The shear rate had an impact of 83.7% and 86.0% on the thixotropy and apparent viscosity, respectively. The ANNs were able to predict responses with good precision (R² >0.95) and low root mean square error (RMSE), showing their potential to be used as a tool to predict the properties of yogurt.

质地是接受酸奶的主要特征之一，必须对其进行监控，以确保产品的质量控制和加工单元的适当布局。然而，这些性质的确定需要许多行业难以获得的昂贵设备，例如旋转流变仪。因此，基于配方和工艺条件（输入）的变化，将人工神经网络（ANN）建模用于预测酸奶的质地特性（输出）。用不同的离心条件和不同浓度的蛋白质和转谷氨酰胺酶生产脱脂酸奶。开发了三个模型：ANN-TPA预测在纹理轮廓（TPA）分析中获得的特性，ANN-TIX和ANN-VIS分别预测触变性和粘度。酶和蛋白质变量分别对ANN-TPA的响应有46.3％和53.6％的影响。剪切速率对触变性和表观粘度的影响分别为83.7％和86.0％。人工神经网络能够准确预测响应（R² > 0.95）和低均方根误差（RMSE），表明它们有潜力用作预测酸奶特性的工具。