食品科学2024，Vol.45Issue(10)：9-18,10.DOI:10.7506/spkx1002-6630-20240129-262
基于残差网络模型的速溶全脂奶粉分散性与堆积密度检测方法
Detection of Dispersibility and Bulk Density of Instant Whole Milk Powder Based on Residual Network
摘要
Abstract
To address the problems of the traditional international standard methods for milk powder quality detection such as subjectivity and lag,this study proposed a rapid method for the detection of the dispersibility and bulk density of milk powder based on residual network(ResNet).The dataset used in this study included 499 particle distribution images taken for 10 groups of instant whole milk powder samples under a 10 × optical microscope.Initially,these sample groups were tested for dispersibility and bulk density using the international standard methods,and classified into different levels of dispersibility and bulk density based on the test results.Subsequently,these microscopic images were used to train the ResNet to facilitate effective classification of different samples.Ultimately,the classification results were used to predict the dispersibility,loose density,and tapped density of instant whole milk powder.Additionally,this study compared the predictive performance of different deep learning models,including ResNet,EfficientNetV2,and Swin Transformer.The results indicated that the deep learning model based on ResNet 152 exhibited the best performance in predicting the dispersibility,loose density,and tapped density of instant whole milk powder,with accuracy rates of 97.50％,98.75％,and 95.00％,respectively for the test set.The exceptional performance of these deep learning models in milk powder quality detection not only proves that this method can predict the dispersibility and bulk density of milk powder in real time and accurately,but also provides a new technological approach for online quality detection of milk powder.关键词
速溶全脂奶粉/分散性/堆积密度/深度学习/残差网络Key words
instant whole milk powder/dispersibility/bulk density/deep learning/residual network分类
轻工业引用本文复制引用
丁浩晗,沈嵩,谢祯奇,崔晓晖,王震宇..基于残差网络模型的速溶全脂奶粉分散性与堆积密度检测方法[J].食品科学,2024,45(10):9-18,10.基金项目
"十四五"国家重点研发计划重点专项(2022YFF1101100) （2022YFF1101100）
中央高校基本科研业务费专项资金资助项目(JUSRP123053) （JUSRP123053）
跨境网络空间安全教育部工程研究中心2023年度开放课题(KJAQ202304007) （KJAQ202304007）
