During training, the SVDD employs an optimization algorithm to draw the smallest sphere in a mapped feature space that contains most of the normal response vectors within it. The intention is to circumscribe the bulk of the support of the underlying distribution that generated the normal sensor response vectors. During inference, given a new response vector of the sensor array to an unknown gas composition, the SVDD anomaly detector acts as a binary classifier for which the sphere serves as a decision boundary: if the new response, when mapped to the feature space, falls within the sphere, it is labeled as "normal" (negative); if it falls outside the sphere, it is labeled as "anomalous" (positive). The SVDD is a sparse kernel method; thus, (i) via a menu of kernel functions, we can create flexibly-shaped decision boundaries, not just spheres, in the original response space and (ii) it is memory-and computation-efficient.