食品工业科技2025，Vol.46Issue(13)：314-326,13.DOI:10.13386/j.issn1002-0306.2024080191
不同温湿度贮藏对澳洲坚果鲜果品质的影响及BP神经网络预测模型构建
Effect of Different Temperature and Humidity Storage Conditions on the Quality of Fresh Macadamia Nuts and the Construction of A Backpropagation Neural Network Prediction Model
摘要
Abstract
The present study investigated the quality changes in fresh macadamia nuts during short-term storage by examining the effects of various storage temperatures and humidity levels(30 ℃-RH80％,35 ℃-RH80％,40℃-RH80％,30 ℃-RH90％,35 ℃-RH90％,and 40℃-RH90％)on the moisture content of the pericarps,nut-in-shells,and kernels.In addition,the cracking and mold rates of pericarps,acid value,peroxide value,iodine value,total phenol content,and total sugar content were evaluated.A quality prediction model for the short-term storage of fresh macadamia nuts was developed using a backpropagation(BP)neural network,and its predictive capability was evaluated using a test set.The results showed that water loss was the fastest in the 35 ℃-RH80％storage group.The cracking rate of the pericarps stored at 35 ℃ increased the most and was significantly higher than that of the nuts treated at 30 and 40 ℃(P＜0.05).The mold rate of the pericarps stored at 30 ℃ increased the most and was significantly higher than that of nuts treated at 35 and 40 ℃(P＜0.05).Both the acid and peroxide values increased during storage;at the end of storage,the highest acid value was 15.57 mg/100 g in the 35 ℃-RH90％group,and the highest peroxide value was 36.44 μg/g in the 30 ℃-RH80％ group.During storage,the iodine value and total phenol contents first increased and then decreased.The maximum increase in the iodine value was 119.26 mg/g in the 35 ℃-RH90％ storage group.At the end of storage,the iodine value in the 40 ℃-RH80％ storage group was 675.72 mg/g,which was lower than that under the other storage conditions.The total phenol content at the end of storage in the 35 ℃-RH80％ and 40 ℃-RH90％ groups was 0.88 mg/g,which was significantly lower than that under the other storage conditions(P＜0.05).The total sugar contents of the samples decreased gradually,that of 35 ℃-RH80％ storage group at the end of storage was significantly lower than the other storage conditions(P＜0.05).The correlation analysis showed that the input and output layers of the prediction model exhibited good correlation.The quality prediction model of fresh macadamia nuts featured seven hidden layer nodes,with correlation coefficients for acid value,peroxide value,iodine value,total phenol content,and total sugar content of 0.97952,0.98815,0.94869,0.94882,and 0.97109 in the training set,respectively,indicating good prediction accuracy.Therefore,this neural network prediction model can be used to predict the quality changes of fresh macadamia nuts during postharvest transportation and storage.This study lays a foundation for the application of the neural network prediction model in macadamia nut quality prediction.关键词
澳洲坚果/鲜果/贮藏品质/预测模型/反向传播(BP)神经网络Key words
macadamia nut/fresh fruit/storage quality/prediction model/BP neural network分类
轻工纺织引用本文复制引用
付镓榕,马尚玄,杨悦雪,徐文婷,兰秀华,魏元苗,黄克昌,贺熙勇,郭刚军..不同温湿度贮藏对澳洲坚果鲜果品质的影响及BP神经网络预测模型构建[J].食品工业科技,2025,46(13):314-326,13.基金项目
云南省重大科技专项计划(202202AE090006) （202202AE090006）
云南省创新引导与科技型企业培育计划(202404BP090014) （202404BP090014）
云南省热带作物科技创新体系建设专项(RF2025) （RF2025）
云南省科技人才和平台计划项目(202405AD350060) （202405AD350060）
"兴滇英才支持计划"项目(2022-0068) （2022-0068）
西双版纳州"雨林英才支持计划" （）
科技人才与平台计划"云南省科技副总项目" （）
云南省郭刚军专家基层科研工作站项目资助. （）
