摘要
针对葡萄酒的鉴别问题,通过电子鼻采集7种葡萄酒的气味信息,应用LightGBM算法对葡萄酒的气味特征进行学习,并运用TPE超参数优化算法对LightGBM算法超参数进行自适应寻优,以5折交叉验证为指标评估模型的性能。试验结果表明LightGBM建立的判别模型对葡萄酒样本的判别准确率为96.62%,优于传统的支持向量机、随机森林、神经网络,验证了LightGBM在葡萄酒品种鉴别中的优越性。
Aiming at the problem of wine identification,the odor information of 7 kinds of wine was collected through the electronic nose,the LightGBM algorithm was used to learn the odor characteristics of the wine,and the TPE hyperparameter optimization algorithm is used to adaptively optimize the HyperGB parameter of the LightGBM algorithm.Verification is an indicator to evaluate the performance of the model.The experimental results showed that the discrimination model established by LightGBM had a 96.62%accuracy rate for wine samples,which was superior to traditional support vector machines,random forests,and neural networks.It verifies the superiority of LightGBM in wine variety identification and provides wine identification a fast,reliable and effective analysis method is also suggested,and more excellent algorithms can be introduced into the field of wine smell data mining machines.
作者
乔淼
张磊
母芳林
QIAO Miao;ZHANG Lei;MU Fang-lin(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300130,China)
出处
《食品与机械》
北大核心
2020年第5期76-79,共4页
Food and Machinery
关键词
葡萄酒
电子鼻
LightGBM
TPE
wine
electronic nose
light gradient boosting machine(LightGBM)
tree parzen estimator(TPE)
