By example, we laid out a methodology to computationally predict the performance of an electronic nose for anomaly detection. The example constituted (i, hardware) a two-sensor array using nanoporous materials ZIF-8 and ZIF-71 as gravimetric recognition elements paired with a (ii, software) the SVDD algorithm for detecting anomalous concentrations of C 2 H 4 and CO 2 in a fruit ripening room with varying humidity. Our methodology is: (1) specify the probability distribution of normal and anomalous gas compositions, (2) construct a model governing the response of each sensing element to any of these gas compositions, (3) use the model to generate synthetic training and testing data sets, (4) train an anomaly detector and evaluate its ability to detect anomalies in the test data set. Looping over proposed sensor array designs and anomaly detection algorithms, we may cheaply apply this methodology to rank the designs-especially if the response model of the sensing elements comes from molecular simulation .