Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA.
Applied Materials, Sunnyvale, CA 94085, USA.
Sensors (Basel). 2021 Nov 16;21(22):7620. doi: 10.3390/s21227620.
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
机器学习方法使电子鼻（E-Nose）能够进行精确的气味识别，具有定性和定量分析功能。先进的机器学习方法对于 E-Nose 获得高性能和增强其在许多应用中的能力至关重要，包括机器人技术、食品工程、环境监测和医学诊断。最近，已经研究、开发和集成了许多机器学习技术，用于特征提取、建模和气体传感器漂移补偿。特征提取的目的是在原始信号中保留稳健的模式信息，同时去除冗余和噪声。利用提取的特征，适当的建模方法可以有效地利用信息进行预测。此外，漂移补偿用于缓解由于气体传感器漂移导致的模型精度降低。这些最新进展极大地提高了 E-Nose 的预测精度和稳定性。本文综述了先进的机器学习方法在 E-Nose 技术中的最新进展，并深入探讨了特征提取、建模和传感器漂移补偿方面的新研究方向。
