We find that the EE outperforms the SVDD in both computational cost and F1-score for anomaly detection on test data (average [over σ H 2 O and σ m values and runs] F1-score improvement of 0.13; see Fig. ). This result is unsurprising because our normal sensor response vectors closely resemble a Gaussian distribution, owing to the underlying Gaussian and uniform distributions of gas compositions and linear gas adsorption model. In practice, the Gaussian assumption limits the practical application of EE for anomaly detection in favor of the more flexible SVDD that is capable of drawing non-elliptical decision boundaries that may even enclose disjoint regions.