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作 者:曹纪磊 高沛鑫 李鑫宇 肖文静 李振宇 CAO Jilei;GAO Peixin;LI Xinyu;XIAO Wenjing;LI Zhenyu(Henan Transportation Technician College,Zhumadian,Henan 463000,China;Henan Agricultural University,Zhengzhou,Henan 450000,China;National Center for Flour and Product Quality Supervision and Inspection,Shangqiu,Henan 476000,China;Jeonbuk National University,Jeonju 54896,Korea)
机构地区:[1]河南交通技师学院,河南驻马店463000 [2]河南农业大学,河南郑州450000 [3]国家面粉及制品质量监督检验中心,河南商丘476000 [4]韩国全北国立大学,韩国全州54896
出 处:《食品与机械》2023年第12期55-59,116,共6页Food and Machinery
基 金:河南省教育教学改革研究青年教师项目(编号:ZJC16080);河南省重点研发与推广专项(编号:2221023200616);河南省教育教学改革研究与实践项目(编号:豫教[2023]02838)。
摘 要:目的:解决现有牛奶蛋白质检测方法存在的精度低、效率低和人工依赖性强等问题。方法:基于高光谱成像系统,提出一种将改进的鲸鱼算法与Elman神经网络相结合用于牛奶蛋白质含量快速无损检测。通过混沌映射、自适应收敛因子、自适应权重优化鲸鱼算法,提高搜索精度,优化后对Elman神经网络参数(权重和阈值)进行寻优。通过试验分析所提无损检测方法的性能。结果:与常规检测方法相比,试验方法在牛奶蛋白质无损检测的多个性能指标方面均为最优,决定系数为0.997 3,均方根误差为0.000 3,检测时间为1.56 s。结论:试验方法具有较高的检测精度和检测效率。Objective:To solve the problems of low accuracy,low efficiency,and strong manual dependence in existing milk protein detection methods.Methods:Based on hyperspectral imaging systems,proposed a combination of improved whale algorithm and Elman neural network for rapid and non-destructive detection of milk protein content.Optimized the whale algorithm through three aspects(chaotic mapping,adaptive convergence factor,and adaptive weight)to improve search accuracy,and optimized the Elman neural network parameters(weights and thresholds)after optimization.Analyzed the performance of the proposed non-destructive testing method through experimental analysis.Results:Compared with conventional detection methods,proposed method was optimal for multiple performance indicators in non-destructive testing of milk protein.The experimental method was optimal in multiple performance indicators for non-destructive testing of milk protein,with determination coefficient of 0.9973,the root mean square error of 0.0003,and the detection time of 1.56 seconds.Conclusion:The experimental method has high detection accuracy and efficiency.
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