Robust Odor Detection in Electronic Nose Using Transfer-Learning Powered Scentformer Model

ACS Sensors, Год журнала: 2025, Номер unknown

Опубликована: Май 15, 2025

Mimicking the olfactory system of humans, use electronic noses (E-noses) for detection odors in nature has become a hot research topic. This study presents novel E-nose based on deep learning architecture called Scentformer, which addresses limitations current like narrow range and limited generalizability across different scenarios. Armed with self-adaptive data down-sampling method, is capable detecting 55 natural classification accuracy 99.94%, model embedded analyzed using Shapley Additive exPlanations analysis, providing quantitative interpretation performance. Furthermore, leveraging Scentformer's transfer ability, efficiently adapts to new gases. Rather than retraining all layers odor set, only fully connected need be trained pretrained model. Using 1‰ retrained model, model-based can also achieve accuracies 99.14% various gas concentrations. provides robust approach diverse direct signals real-world applications.

Язык: Английский

MoS2@PANI composite for highly sensitive ammonia gas sensing at room temperature

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 114087 - 114087

Опубликована: Май 1, 2025

Язык: Английский