## 2 Materials and Methods
### Identification Methods
#### 2.3.3 Feature Classification
From the experimental results, it was found that XGBoost has the capability of avoiding the overfitting problem and was superior to RF, SAMME, GBDT, NN, and SVM in terms of accuracy mean and variance based on our EN system. A managerial implication of this paper is that comparing PI methods in advance quantitatively is important to improve time efficiency and to save financial costs for EN-based PI. Some future directions for our work include the following:

1. Comparing other feature extraction methods using the same framework as described here. In the present research, the feature classification subprocess was emphasized and thoroughly studied, while only PCA was tested for feature extraction. 2. Comparing the time-efficiency of the EN-based PI methods. Because the quantity of raw measurements used was not too large, all the tested methods ran fluently and accomplished the PI tasks in similarly short time spans. 3. Simultaneously identifying more types of perfumes with our EN. The identification of more perfume types can be achieved by identifying them in batches; only four types of perfumes were identified in this investigation. 4. Designing a new EN with more compact structure. For ease of use in field stations, the EN should be compact. 5. Identifying other types of VOCs. The gas sensors in our EN were also sensitive to other types of VOCs. It is anticipated that our EN can also be used for identifying other VOCs. Conceptualization, methodology, software, investigation, funding acquisition, writing--original draft preparation, writing--review and editing, M.C. ; data curation, software, X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant no. 61801287). Not applicable. The experimental result data has been deposited in the Github repository ([[http://github.com/aoxdrank/EnoseData.git](http://github.com/aoxdrank/EnoseData.git)]([http://github.com/aoxdrank/EnoseData.git](http://github.com/aoxdrank/EnoseData.git))). The authors would like to thank the anonymous reviewers for their helpful comments that have significantly improved this paper. The authors declare no conflict of interest. ## References

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