电子鼻茶叶无损分类的传感器温度漂移噪声补偿OA

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%.