中国农机化学报2026，Vol.47Issue(1)：325-330,345,7.DOI:10.13733/j.jcam.issn.2095-5553.2026.01.044
电子鼻茶叶无损分类的传感器温度漂移噪声补偿
Sensor temperature drift noise compensation for non-destructive classification of electronic nose tea leave
摘要
Abstract
In response to the phenomenon of gas data drift of electronic nose under the influence of ambient temperature,we believe that the sensors may receive the influence of drift factors in the process of feature optimization,etc.Therefore,this paper proposes a partially compensated de-drift compensation method,which reduces the feature complexity of the compensation model while retaining the original sensor dataset that is less affected by the drift factors to participate in the classification together.By constructing three different compensation mathematical models to compare the difference between the results of the conventional compensation process and the partial compensation process,it is demonstrated that the partial compensation process can effectively improve the anti-drift capability of the electronic nose in the deep learning model,and the optimal compensation model is filtered out to further illustrate the optimal compensation approach.The best combination is the partial compensation combination of random forest,and the R2 results of the training set and test set reach 0.94 and 0.89 respectively,while the RMSE is 0.14 and 0.20 respectively,and the resolution of the tea species is improved to 98%and 96%.关键词
电子鼻/温度补偿/茶叶分类/神经网络/随机森林Key words
e-nose/temperature compensation/tea classification/neural network/random forest分类
信息技术与安全科学引用本文复制引用
Cai Minhao,Xu Sai,Lu Huazhong,Zhou Xingxing..电子鼻茶叶无损分类的传感器温度漂移噪声补偿[J].中国农机化学报,2026,47(1):325-330,345,7.基金项目
国家重点领域研发计划项目(2022YFD2002203) （2022YFD2002203）
广东省乡村振兴专项资金(2024TS—1-2) （2024TS—1-2）
广东省国际科技合作项目(2023A0505050129) （2023A0505050129）
广东省农业科学院科技创新战略专项培训项目(农业科研主力军建设)(R2023PY—QN002) （农业科研主力军建设）
广东省科协青年科技人才培育项目(SKXR2025519) （SKXR2025519）
