摘要
目的建立了一种结合图像分析和深度学习的油菜籽中总酚含量的快速预测方法。方法利用VGG19网络进行油菜籽图像籽粒特征的提取,通过多个卷积层来学习油菜籽图像的特征,并建立了回归模型用于预测油菜籽的总酚含量。共收集了100种油菜籽样本,将油菜籽样本按照3:1的比例划分为训练集和测试集,利用均方损失函数(MSELoss)和决定系数(r^(2))评估模型预测准确性。结果测试集MSELoss=0.0085、r^(2)=0.9914,表明该预测模型具有一定的准确性和实用性。结论本研究提出了一种快速、准确的评估油菜籽总酚含量的方法,为油菜籽的总酚测定提供一种快速、准确的智能化检测方法。
Objective To establish a rapid method for predicting total phenol content in rapeseed by combining image analysis and deep learning.Methods The VGG19 network was used to extract features of rapeseed images,multiple convolutional layers were used to learn the features of rapeseed images,and a regression model was established to predict the total phenolic content of rapeseed.A total of 100 rapeseed samples were collected,and the rapeseed samples were divided into training sets and test sets at a ratio of 3:1.The mean square loss function(MSELoss)and coefficient of determination(r^(2))were used to evaluate the model prediction accuracy.Results On the test set,MSELoss was 0.0085,r^(2)was 0.9914,indicating that the prediction model had certain accuracy and practicality.Conclusion This study proposes a rapid and accurate method to evaluate the total phenolic content of rapeseed,which can provide a rapid and accurate intelligent detection method for the determination of total phenol of rapeseed.
作者
黄晓琛
张凯利
肖华明
刘元杰
赵志聪
陈洪
魏芳
HUANG Xiao-Chen;ZHANG Kai-Li;XIAO Hua-Ming;LIU Yuan-Jie;ZHAO Zhi-Cong;CHEN Hong;WEI Fang(Oil Crops Research Institute of Chinese Academy of Agricultural Sciences,Key Laboratory of Oilseeds Processing,Ministry of Agriculture and Rural Affairs,Hubei Key Laboratory of Lipid Chemistry and Nutrition,Oil Crops and Lipids Process Technology National&Local Joint Engineering Laboratory,Wuhan 430062,China;College of Information and Electrical Engineering,China Agricultural University/Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,Beijing 100083,China;National Key Laboratory of Crop Genetic Improvement,Huazhong Agricultural University,Wuhan 430070,China)
出处
《食品安全质量检测学报》
CAS
北大核心
2023年第19期29-36,共8页
Journal of Food Safety and Quality
基金
国家自然科学基金项目(U21A20274)
国家重点研发计划专项(2021YFD1600103)
农业农村部油料作物生物学与遗传育种重点实验室开放课题项目(KF2023008)
中国农业科学院创新工程项目(CAAS-ASTIP-2013-OCRI)。
关键词
油菜籽
图像分析
总酚含量
深度学习
rapeseed
image analysis
total phenolic content
deep learning
