This is a bachelor thesis about investigating the viability of using a one-class support vector machine (OCSVM) to detect a target substance in a gas regardless of inference, using partially selective metal-oxide (MOX) gas sensors.Many applications of an electronic nose (e-nose) require the sensor to be used in complex scenarios, where environmental conditions are uncontrolled and unknown non-target substances might be present. The latter hinders the use of multiclass classifiers but leaves one-class classifiers as a possible option. If using one class classification to interpret response patterns from an array of metal-oxide gas sensors for gas discrimination tasks prove viable, this can potentially broaden the applicability of low-cost MOX sensors-based e-noses in real-world scenarios.Throughout this investigation, several OCSVM-models have been trained and tested using data collected from practical sampling of several gas classes comprised of different mixes of the same two analytes.Four different training sets will be utilized for model training. One of these sets will contain data generated exclusively from samples taken on pure target analyte with no inference present, while the others are comprised of data collected from a mixture of target analyte and inference analyte.The OCSVM models trained on these different training sets are tuned in terms of functional parameters, and then the tuned models are used to perform gas classification on test sets that include pure analytes or gas mixtures. The model implemented relies on the scikit-learn library, version 0.23.2.Based on the gas classification results, OCSVM models investigated in this work are validated for the use of one-class classification for gas discrimination tasks.