# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 4 Conclusions  
In this study, we provide insight into the electronic nose in the face of drift factors and how these factors affect its feature optimization module. We first performed a categorical analysis of datasets collected under different temperature and humidity conditions, comparing the sensor scores under these conditions. This step was intended to clarify the range of compensation and to reveal the specific impact of drift factors on the feature optimization process. The results of our analysis show that the classification effectiveness of the e-nose, the extent of the impact of drift, and the sensor scores exhibit significant differences across experimental conditions. These findings suggest that compensation for a specific range of environments as well as specialized treatment of the feature optimization module are essential. Furthermore, we observed a positive correlation between the concentration of the sensor scores and the classification effectiveness of the electronic nose, a phenomenon verified in the comparison of the sensor scores before and after compensation. To cope with the impact of drift on the feature optimization module, we propose a compensation method for secondary feature optimization. This method builds on the existing compensation strategy and aims to further improve the robustness and anti-drift performance of the electronic nose. After being processed by this method, the classification accuracy of our data training set is improved to 100%, and the accuracy of the test set reaches 96%, both showing excellent classification performance. Given the relatively few current studies on the effect of drift factor on feature optimization modules, our work not only fills this gap, but also provides a new research direction for performance improvement and drift resistance enhancement of electronic nose technology in real-world detection. The ultimate goal of this study is to provide more stable and reliable technical support for the application of electronic nose in complex environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental** & \begin{tabular}{c} **Training Set** \\ **Classification** \\ **Correctness** \\ \end{tabular}  & \begin{tabular}{c} **Validation Set** \\ **Classification** \\ **Correctness** \\ \end{tabular}  & \begin{tabular}{c} **Compensation** \\ **Status** \\ \end{tabular}  & \ \begin{tabular}{c} **Number of** \\ **Feature** \\ **Optimizations** \\ \end{tabular}  & \begin{tabular}{c} **Weighted State** \\ **Sensor** \\ **Arrays** \\ \end{tabular}  \\ \hline
35\% RH & 92\% & 76\% & \ \begin{tabular}{c} Before \\ compensation \\ Before \\ compensation \\ Before \\ compensation \\ Before \\ compensation \\ Before \\ compensation \\ Before \\ compensation \\ Before \\ compensation \\ \end{tabular}  &
\begin{tabular}{c} **–**  
**Author Contributions:** Conceptualization, M.C. and S.X. ; methodology, M.C. ; software, M.C. ; validation, M.C. ; formal analysis, M.C. ; investigation, M.C. ; resources, S.X., H.L., X.Z. and X.L. ; data curation, S.X. and X.L. ; writing--original draft preparation, M.C. ; writing--review and editing, S.X., H.L., X.Z. and X.L. ; visualization, M.C. ; supervision, S.X., H.L., X.Z. and X.L. ; project administration, S.X., H.L. and X.Z. ; funding acquisition, S.X., H.L. and X.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper received funding from the Special Fund for Rural Revitalization of Guangdong Province (no. 2024 TS-1-2), the National Key Research and Development Program of China (no. 2022 YFD2002203), the International Science and Technology Cooperation Project of Guangdong Province (no. 2023A0505050129), the Innovation Fund Industry Special Project of Guangdong Academy of Agricultural Science (grant no. 202306), and the Special Training Project for Science and Technology Innovation Strategy of Guangdong Academy of Agricultural Sciences (Construction of Main Agricultural Research Force) (R2023 PY-QN002).