Next, we assess the performance of the SVDD under varying levels of measurement noise and background humidity. For various pairings of the variance of (a) measurement noise contaminating the sensor response σ m and (b) background concentrations of humidity σ H 2 O , we 100 times (i) generated a training set {m i , normal)} 100 i=1 , (ii) trained and hyperparameter-tuned an SVDD with this data, then (iii) evaluated the performance of the SVDD on generated test data. The heatmap in Fig. increase. The reasons are: (i) noise can push response vectors, that would otherwise be correctly classified, across the decision boundary onto the wrong side and (ii) variance in the background humidity levels contribute changes in the sensor response vectors that can (a) be mis-attributed to anomalies in C 2 H 4 and CO 2 concentrations, causing false positives, or (b) mask (counter-act) changes due to C 2 H 4 and CO 2 concentrations, causing false negatives.