Abstract
This article introduces a novel approach to improve electronic nose classification accuracy by optimizing sensor arrays and aligning features. This involves selecting the best sensor combinations and reducing redundant information for better odor recognition. We employ a feature alignment algorithm to address the discrepancies that impede model sharing between electronic nose devices. Our research focuses on overcoming challenges associated with material selection and the constraints of transferring classification models across different electronic nose devices for drug classification. We fabricated six SnO(2)-based MEMS gas sensors using physical vapor deposition. The ReliefF algorithm was employed to rank and score each sensor's contribution to drug classification, identifying the optimal sensor array. We then applied feature alignment from transfer learning to enhance model sharing among three inconsistent devices. This study resolves the issue of electronic noses being hard to use on the same database due to hardware inconsistencies in batch production, laying the groundwork for future mass production.