Artificial olfaction is an emerging application field for machine learning practitioners. In this work, we propose a holistic approach to pattern classification in electronic noses applications. Specifically, we show how classification results based on a complete measurement cycle can be combined with an assessment provided by real time classifiers acting on the single instantaneous measurement sample. A running classification confidence measure allows for obtaining fast and reliable outcomes. A safety critical scenario has been selected for the testing of the proposed pattern analysis strategy involving the identification and discrimination of surface contaminants on composite panels in pre-bonding Non Destructive Tests (NDT) during lightweight aircraft assembly. A reject option has been introduced to refuse low classification confidence panels improving both FP and FN rates. Results show how this strategy can efficiently exploit two different views of the electronic nose olfactive fingerprinting process that are currently seen as alternatives.