Temporal-Spatial and Signal Descriptor Feature Extraction for Electronic Nose Signal Processing

Electronic noses (E-Noses) offer a rapid and nondestructive way to identify substances. However, accurately classifying complex sensor data from E-Noses remains a challenge. While effective at handling this data, deep learning (DL) suffers from high computational demands, hindering its use in portable devices for resource-constrained environments. Furthermore, there is an opportunity to better exploit the interplay among temporal, spatial, and spectral information within E-Nose data. This study addresses these limitations by proposing a novel feature extraction technique called temporal-spatial and signal descriptor (TSSD). TSSD leverages both time-domain and frequency-domain data to capture key odor signatures. It builds upon existing advancements to extract a more comprehensive feature space for accurate odor classification using machine-learning (ML) algorithms. We validate TSSD’s effectiveness against existing approaches using a public E-Nose dataset with threefold, fivefold, and tenfold cross validation. In addition, we compare its performance to seven datasets with its state-of-the-art methods. Our approach demonstrated that it has the potential to improve E-Nose classification accuracy and outperform the benchmark while enabling computational costs around 13–14.4 ms.