## 3 Results
Since the training and validation spectra are simulated and do not contain noise, we can conclude that the misclassifications for the validation spectra arise from similar transitions present in very complex spectra for more than one molecule, and are not from overfitting. We conclude that VOC-Net is not overfitting, since similar training and validation accuracies were achieved and the model was trained with only four epochs. The smaller number of epochs essentially implements an early stopping strategy that prevents overfitting. Ensuring the model is not overfitting does not preclude misclassifications; however, it makes the model capable of generalization. Thirty-six experimental spectra measured as described above were used to demonstrate and test the model. The softmax scores produced by the VOC-Net for these experimental spectra and their subsequent classifications are given in Figures 12-14. Against the experimental dataset, the model yields one misclassification. The misclassified spectra  
Figure 11: Sampled validation spectra and their corresponding VOC-Net softmax scores. is for methanol at 1 Torr. The details of the measurement conditions of the experimental spectra are given in Table 3. We observe that all of the spectra contain some amount of noise (noise floor is on average 0.001 in absorbance space) and other experiment-to-experiment variations and anomalies. For example, some measurements exhibit an subtle upward drift in measured absorbance at higher frequencies that is a purely experimental artifact resulting from intensity baseline drift in the radiation source. Despite the noise and other aberrations not found in the training datasets, VOC-Net performs very well against the experimental dataset, yielding a 97.2% classification accuracy. The strong performance against experiments illustrates that the VOC-Net has managed to generalize and learn to distinguish the salient spectral features belonging to each molecule. However, when the noise floor is large relative to the signal, the softmax scores illustrate that the model has lower confidence and offers probabilities that the spectra under consideration may belong to multiple molecules. For the single misclassification (spectra no. 17), we observe that the model marginally misses the correct classification (methanol), confusing the methanol spectra with that of formic acid and carbonyl sulfide. Note, similar uncertainty was shown by the model with respect to methanol, carbonyl sulfide, and formic acid for simulated validation spectra. Due to this particular misclassification, in the ROC curves shown in Figure 15, the AUC scores for formic acid and methanol are reduced. The overall AUC score for the six molecule classes that make up the experimental spectra is 0.983, which is very similar to the training and validation AUC scores of 0.996 and 0.998, respectively. The overall classification accuracy for the VOC-Net against experimental data (97.2%) is somewhat better than the performance of a support vector machine (SVM) classifier we have previously reported, that under similar conditions produced an accuracy of 93.5% against experiments [17]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Exp. Spectrum No.** & **Molecule** & **Pressure (Tor)** & **Exp. Spectrum No.** & **Molecule** & **Pressure (Tor)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 3: Experiment conditions. The experimental spectra are sequentially plotted from left to right in Figures 12–14. Figure 12: VOC-Net classification of experimental spectra (1–12). The corresponding softmax scores for each classification are given with each spectra. Figure 13: VOC-Net classification of experimental spectra (13–24). The corresponding softmax scores for each classification are given with each spectra.