Liu Ying-Jie, Zeng Ming, Meng Qing-Hao
Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Rev Sci Instrum. 2019 Feb;90(2):025001. doi: 10.1063/1.5064540.
The simplification of data processing is the frontier domain for electronic nose (e-nose) applications, whereas there are a lot of manual operations in a traditional processing procedure. To solve this problem, we propose a novel data processing method using the bio-inspired neural network modeled on the mammalian olfactory system. Through a neural coding scheme with multiple squared cosine receptive fields, continuous sensor data are simplified as the spike pattern in virtual receptor units. The biologically plausible olfactory bulb, which mimics the structure and function of main olfactory pathways, is designed to refine the olfactory information embedded in the encoded spikes. As a simplified presentation of cortical function, the bionic olfactory cortex is established to further analyze olfactory bulb's outputs and perform classification. The proposed method can automatically learn features without tedious steps such as denoising, feature extraction and reduction, which significantly simplifies the processing procedure for e-noses. To validate algorithm performance, comparison studies were performed for seven kinds of Chinese liquors using the proposed method and traditional data processing methods. The experimental results show that squared cosine receptive fields and the olfactory bulb model are crucial for improving classification performance, and the proposed method has higher classification rates than traditional methods when the sensor quantity and type are changed.
数据处理的简化是电子鼻应用的前沿领域，而传统处理过程中有许多手动操作。为了解决这个问题，我们提出了一种新颖的数据处理方法，该方法使用基于哺乳动物嗅觉系统建模的生物启发神经网络。通过具有多个平方余弦感受野的神经编码方案，连续的传感器数据被简化为虚拟受体单元中的尖峰模式。模仿主要嗅觉通路的结构和功能的具有生物学合理性的嗅球，旨在提炼编码尖峰中嵌入的嗅觉信息。作为皮质功能的简化表示，建立了仿生嗅觉皮层以进一步分析嗅球的输出并进行分类。所提出的方法可以自动学习特征，而无需进行诸如去噪、特征提取和降维等繁琐步骤，这显著简化了电子鼻的处理过程。为了验证算法性能，使用所提出的方法和传统数据处理方法对七种中国白酒进行了比较研究。实验结果表明，平方余弦感受野和嗅球模型对于提高分类性能至关重要，并且当传感器数量和类型改变时，所提出的方法比传统方法具有更高的分类率。
