# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion
### Electronic Nose Compensation
#### 3.3.1 Determining the Scope of Compensation  
Not all environmental conditions need to be compensated for the electronic nose; in order to avoid overfitting compensation leading to a decrease in the detection efficiency of the electronic nose, we used the random forest model to classify the classification results under each experimental condition separately and obtained the results of the classification correctness rate of each gradient (Tables 2 and 3).  
Under different humidity gradients, the classification under 80% and 95% humidity conditions were more satisfactory, reaching 96% and 100%, respectively, so no compensation was needed. As the humidity gradually decreased, the correct classification rate showed a decreasing trend, down to 76%. The results suggest that compensation is necessary for e-nose data at 35%, 50%, and 65% humidity gradients.  
The correct rate of tea classification under temperature conditions, on the other hand, showed a decreasing trend with decreasing temperature, starting to decline at 35 \({}^{\circ}\)C, but 96% of the results were still able to classify the tea types well. However, the temperature condition of 45 \({}^{\circ}\)C has dropped to 80%, showing poor classification performance, so this temperature is used as the compensation range of the electronic nose.  
The specific division of temperature and humidity performance also indicates that the environmental conditions that act on the electronic nose to make its working state  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Humidity Level**} & **Training Set Classification** & **Validation Set Classification** \\  & **Correctness** & **Correctness** \\ \hline
35\% & 92\% & 76\% \\
50\% & 96\% & 76\% \\
65\% & 89.3\% & 88\% \\
80\% & 98.7\% & 96\% \\
95\% & 97.3\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification correctness results for each humidity gradient after feature optimization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Temperature Level**} & **Training Set Classification** & **Validation Set Classification** \\  & **Correctness** & **Correctness** \\ \hline
5 \({}^{\circ}\)C & 100\% & 100\% \\
15 \({}^{\circ}\)C & 98.7\% & 100\% \\
25 \({}^{\circ}\)C & 98.7\% & 100\% \\
35 \({}^{\circ}\)C & 98.7\% & 96\% \\
45 \({}^{\circ}\)C & 97.3\% & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification correctness results for each temperature gradient after feature optimization.  
change will also have a certain impact on the detection of targets in the environment. This suggests that the distinction between electronic noses needs to be analyzed in terms of specific targets and detection environments, that the optimal operating environment for an electronic nose is not necessarily the optimal detection environment for detecting a target, and that trade-offs need to be analyzed. In this experiment, based on the results of tea classification, we chose humidity 35%, 50%, 65% and temperature 45 \({}^{\circ}\)C as the data intervals for compensation and observation, because the results under this condition presented a poor classification correctness.  
In addition, we found that the higher the concentration of sensor scores, the corresponding detection environment showed better classification results (humidity 80%, temperature 5 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, and 25 \({}^{\circ}\)C all demonstrated the results of score centralization), so in the subsequent comparison of the results, we will further validate the classification by the effect of sensor scores.