【摘要】 准确预测剩余货架期是降低苹果过长贮藏风险的有效途径,目前基于传统动力学模型的预测准确度较低,提出一种基于生成式对抗网络(GAN)改进的反向传播人工神经网络(BP-ANN)苹果货架期预测方法。以0、5、15、25℃下贮藏的"富士"苹果为研究对象,获取果实的12个理化品质指标随贮藏时间变化的取值;分别采用2种特征选择方法对品质指标进行排序,依次累加排序为1～12的品质指标结合贮藏温度作为BP-ANN的输入层变量。通过GAN扩大BP-ANN的训练集样本数量,建立"富士"苹果货架期的GAN-BP-ANN和BP-ANN预测模型。试验结果表明,经过GAN可生成与真实数据分布范围一致的数据集,以真实和生成数据集共同作为训练集构建的GAN-BP-ANN模型其验证集准确度总体高于BP-ANN模型;以稀疏主成分分析(SPCA)选取得到的前1、2、6个品质指标,结合贮藏温度分别作为GAN-BP-ANN模型的输入层对货架期进行预测,其平均相对误差均在0.070以内,决定系数均在0.988以上。更多还原

【Abstract】 Accurately predict the shelf-life of apple is urgently needed in practice.A feasible and nonequipment-depended data collection and model construction method was explored for shelf-life prediction of apple based on quality attributes observations and storage temperature.’Fuji’ apples were stored at four different temperatures of 0℃,5℃,15℃ and 25℃,respectively.The firmness,soluble solids content,titratable acid,SSC-TA rate,reducing ascorbic acid,starch content,weight loss and color values(L,a,b,ΔE,C) were measured periodically to obtain data set of 12 quality features at each storage stage and temperature.Feature selection method of SPCA and ReliefF was used to rank the quality attributes,respectively.Generative adversarial networks(GAN)-back propagation artificial neural network(BP-ANN),and BP-ANN were used to construct regression models between quality feature,storage temperature and shelf-life.Ratio of training set to test set was 3∶ 1.Totally 12 quality attributes were ranked in different orders by different feature selection methods.Using each accumulative combination of 1 ～ 12 th quality attributes and storage temperature as input variables of GAN-BP-ANN and BP-ANN respectively,error rate of the validation set as evaluation criterion of prediction model.The accuracy of the models constructed by feature selection methods of SPCA were higher than that of ReliefF.The accuracy of the models established by GAN-BP-ANN were generally higher than that of the BP-ANN.It showed that GAN can effectively reduce the overfitting of BP-ANN model.Using three selected feature combinations as input variables,respectively,BP-ANN reached an accuracy above 0.930.GAN added BP-ANN can be a novel approach for accurately predict the shelf-life of postharvest “Fuji”apples by using the selected quality attributes and temperature.更多还原