Kang Seokho, Kim Yonggik, Ajani Oladayo S, Mallipeddi Rammohan, Ha Yushin
Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Sciences, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Department of Artificial Intelligence, College of IT Engineering, Kyungpook National University, Daehak-ro 80, Bukgu, 41566, Daegu, Republic of Korea.
Heliyon. 2024 Aug 17;10(17):e36472. doi: 10.1016/j.heliyon.2024.e36472. eCollection 2024 Sep 15.
In the food industry, meeting food quality demands is challenging. The quality of wheat flour, one of the most commonly used ingredients, depends on the extent of debranning done to remove the aleurone layer before milling. Therefore, the end product management can be simplified by predicting the properties of wheat flour during the debranning stage. Therefore, the chemical and rheological properties of grains were analyzed at different debranning durations (0, 30, 60 s). Then the images of wheat grain were taken to develop a regression model for predicting the chemical quality (i.e., ash, starch, fat, and protein contents) of the wheat flour. The resulting regression model comprises a convolutional neural network and is evaluated using the coefficient of determination ( ), root-mean-square error, and mean absolute error as metrics. The results demonstrated that wheat flour contained more fat and protein and less ash with increasing debranning time. The model proved reliable in terms of root-mean-square error, mean absolute error, and for predicting ash content but not starch, fat, or protein contents, which can be attributed to the lack of features in the collected images of wheat kernels during debranning. In addition, the selected method, debranning, was beneficial to the rheological characteristics of wheat flour. The proportion of fine particles increased with the debranning time. The study experimentally revealed that the end product diversity for wheat flour can be controlled to provide selectable ingredients to customers.
在食品工业中，满足食品质量要求具有挑战性。小麦粉作为最常用的原料之一，其质量取决于在研磨前去除糊粉层的去皮程度。因此，通过预测去皮阶段小麦粉的特性，可以简化最终产品管理。因此，分析了不同去皮时间（0、30、60秒）下谷物的化学和流变特性。然后拍摄小麦籽粒图像，以建立预测小麦粉化学质量（即灰分、淀粉、脂肪和蛋白质含量）的回归模型。所得回归模型包括一个卷积神经网络，并使用决定系数（ ）、均方根误差和平均绝对误差作为指标进行评估。结果表明，随着去皮时间的增加，小麦粉中脂肪和蛋白质含量增加，灰分含量降低。该模型在预测灰分含量方面的均方根误差、平均绝对误差和 方面被证明是可靠的，但在预测淀粉、脂肪或蛋白质含量方面不可靠，这可归因于去皮过程中小麦籽粒采集图像中缺乏特征。此外，所选的去皮方法有利于小麦粉的流变特性。细颗粒的比例随着去皮时间的增加而增加。该研究通过实验表明，可以控制小麦粉的最终产品多样性，为客户提供可选择的原料。
