摘要
应用BP神经网络,通过苹果质构特性指标(硬度、可恢复形变、黏着性、内聚性、咀嚼性)来预测苹果贮藏品质(出汁率、可溶性固形物、总酸、固酸比)的方法,建立苹果品质的预测模型。本实验将寒富苹果分别置于温度为0℃和20℃的贮藏条件下,分别测定苹果在贮藏期间品质的变化。以苹果质构特性指标为输入,品质指标为输出确定网络拓扑结构,训练所建立的苹果品质神经网络模型。仿真结果表明:该神经网络模型用质构特性指标能预测苹果品质,同时通过2组非样本数据来验证该模型,其预测值与实测值的相对误差在5%以下,故能够实现用质构值评价苹果品质的目的。
A BP neutral network model for predicting storage quality traits of ＇Hanfu＇ apple including juice yield, soluble solids, total acid and solid/acid ratio based on texture properties such as hardness, resilience, adhesiveness, cohesiveness and chewiness was established. Quality changes of ＇Hanfu＇ apple were measured during storage at 0 ℃ and 20 ℃. A topological network structure was constructed to train the established predictive model. The results of simulation demonstrated that the BP neutral network model allowed the prediction of apple quality based on texture properties. The model was validated using two sets of non-sample data and relative errors lower than 5% between the predicted and the observed values were obtained.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2012年第24期335-338,共4页
Food Science
基金
沈阳市科技局项目
关键词
寒富苹果
质构特性
贮藏品质
BP神经网络
预测模型
‘Hanfu＇apple： texture properties
quality properties： BP neural network
predictive model
