Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure induced by unknown gas interference. Existing methods predominantly rely on Euclidean distance or conventional classifiers, failing to account for anisotropic feature distributions and dynamic signal intensity variations. To address these issues, this study proposes the Spherical Normalization coupled Mahalanobis (SNM) module, a universal post-processing module for open-set gas recognition. First, it achieves geometric decoupling through cascaded batch and L2 normalization, projecting features onto a unit hypersphere to eliminate signal intensity fluctuations. Second, it utilizes Mahalanobis distance to construct adaptive ellipsoidal decision boundaries that conform to the anisotropic feature geometry. The architecture-agnostic SNM-Module seamlessly integrates with mainstream backbones including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer. Experiments on the public Vergara dataset demonstrate that the Transformer+SNM configuration achieves near-theoretical-limit performance in discriminating among multiple target gases, with an AUROC of 0.9977 and an unknown gas detection rate of 99.57% at 5% false positive rate, significantly outperforming state-of-the-art methods with a 3.0% AUROC improvement and 91.0% standard deviation reduction compared to Class Anchor Clustering (CAC). The module maintains exceptional robustness across five sensor positions, with standard deviations below 0.0028. This work effectively addresses the critical challenge of simultaneously achieving high accuracy and high stability in open-set gas recognition, providing solid support for industrial E-nose deployment.

翻译：电子鼻系统在开放集气体识别中面临两个相互关联的挑战：由信号漂移引起的特征分布偏移，以及由未知气体干扰导致的决策边界失效。现有方法主要依赖欧氏距离或传统分类器，未能考虑各向异性特征分布和动态信号强度变化。为解决这些问题，本研究提出了球形归一化耦合马氏距离模块，这是一个用于开放集气体识别的通用后处理模块。首先，它通过级联的批归一化和L2归一化实现几何解耦，将特征投影到单位超球面上以消除信号强度波动。其次，它利用马氏距离构建自适应椭球决策边界，以符合各向异性特征几何结构。该架构无关的SNM模块可与包括卷积神经网络、循环神经网络和Transformer在内的主流骨干网络无缝集成。在公开的Vergara数据集上的实验表明，Transformer+SNM配置在区分多种目标气体方面达到了接近理论极限的性能，其AUROC为0.9977，在5%误报率下未知气体检测率达到99.57%，显著优于现有最先进方法，与类锚点聚类方法相比，AUROC提升了3.0%，标准差降低了91.0%。该模块在五个传感器位置上均保持卓越的鲁棒性，标准差低于0.0028。这项工作有效解决了在开放集气体识别中同时实现高精度和高稳定性的关键挑战，为工业电子鼻的部署提供了坚实支持。