## 2 Materials and Methods
### Identification Methods
#### 2.3.3 Feature Classification
The generation process of mean accuracies on the test sets was closer to real EN-based PI applications. Thus, the mean accuracies on the training sets were not listed and compared. Although the value of \(d^{\prime}\) was relatively high when XGBoost achieved its highest \(\mu\) value of 97.5%, when \(d^{\prime}\) equaled 15, the mean accuracy obtained by XGBoost was 96.67%, which was close to its highest \(\mu\) value and higher than the highest \(\mu\) values of the other tested methods. In our experiments, when \(d^{\prime}\) equaled 15, the input data for the classification methods was a 120 \(\times\) 15 matrix, which can be readily processed by a typical commercial notebook computer. The identification accuracy variances \(\delta\) obtained in our experiments are shown in Figure 6. As mentioned, RF, SAMME, and GBDT were faced with the overfitting problem, which explains why the three methods obtained generally higher \(\delta\) values on the test sets than those obtained on the training sets. When the feature dimension \(d^{\prime}\) exceeded seven, the \(\delta\) values of NN and SVM almost converged at 0.34% and 0.48%, respectively. When the feature dimension \(d^{\prime}\) exceeded nine, the \(\delta\) values of XGBoost became smaller and more stable. Moreover, with respect to the highest mean identification accuracies, the \(\delta\) value of NN, SVM, and XGBoost were 0.34%, 0.48%, and 0.15%, respectively. The experimental results shown in Figures 5 and 6 demonstrate that it is feasible to realize PI by successively using PCA and XGBoost to reduce the feature dimension to about 15 and the feature classification in PI based on our self-designed EN, respectively. ### Advantages and Limitations

As mentioned in Section 1, all methods tested in this paper are based on a portable EN device, and, thus, have the inherent advantages of low-cost, high time-efficiency, and portability. However, the limitation of these methods is that they cannot discriminate different constituents within a chemical compound, which can be achieved using GC and MS with enough investment, time, and training. Nevertheless, various real applications can be modeled as the problem of identifying the type of different separated volatile organic compounds (VOCs), which can be solved with our EN using the comparison methods. The advantage and limitations of the methods can be summarized as follows: The advantage of PCA is that it can achieve good performance on feature dimension reduction. However, PCA has the limitation that it cannot solely realize feature classification. In contrast, the tested classification methods have the advantage that they can obtain good

Figure 6: The variances of identification accuracies \(\delta\) obtained by the six tested classification methods when the output vector dimension of PCA is set as different numbers. Bars with and without shading pattern indicate the accuracy variances obtained with the test and training sets, respectively. classification performance; however, they also have the limitation that they cannot be used alone for feature extraction or dimension reduction. Moreover, according to our experimental results, XGBoost, NN, and SVM have the advantage that they can overcome the overfitting problem, while RF, SAMME, and GBDT have the limitation that they are influenced by the overfitting problem. ## 4 Conclusions and Future Work

Combined with PCA-based feature extraction, four well-known tree ensemble learning methods (RF, SAMME, GBDT, and XGBoost) were successfully implemented for PI using a portable self-designed EN. By considering samples of perfumes in the market as the test sets in our study, the model trained with genuine perfume samples was used to predict whether the tested perfume was genuine or fake. RF, SAMME, GBDT, and XGBoost obtained different PI accuracies in our experiments, due to their different ways of constructing the base decision tree learners and of combining the base learner prediction results.