Improvement of capability for classifying odors in dynamically changing concentration using QCM sensor array and short-time Fourier transform
Improvement of capability for classifying odors in dynamically changing concentration using QCM sensor array and short-time Fourier transform
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DOI：
10.1016/j.snb.2007.05.009
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发表时间：
2007-11-15
影响因子：
8.4
通讯作者：
Nakamoto, T.
中科院分区：
文献类型：
--
作者：
Nimsuk, N.;Nakamoto, T.
本文针对环境空气中常见的浓度动态变化情况，提出了一种提高气味分类能力的方法。本研究采用石英晶体微天平（QCM）传感器测量传感器响应。我们提出的方法采用了短时傅立叶变换（STFT）算法和逐步判别分析的特征提取和降维。短时傅立叶变换不仅简单易懂，而且给出了实时应用所需的具有时间信息的频率特性。由于逐步方法的变量选择也降低了导致稳定的分类结果的模式向量的维数。最后，使用学习矢量量化（LVQ）的方法来评估分类性能，我们成功地实现了高的分类率，即使气味浓度变化到各种条件下，而分类率是不够的情况下，只使用传感器响应的幅度。这表明该方法对气味浓度动态变化的鲁棒性。(c)2007年由Elsevier B.V.出版。
In this paper, we propose a method for improving the capability of odor classification in dynamical change of concentration often encountered in the ambient air. Quartz crystal microbalance (QCM) sensors are used to measure the sensor responses in this research. Our proposed method employs a short-time Fourier transform (STFT) algorithm and a stepwise discriminant analysis for feature extraction and dimensional reduction. The STFT is not only simple and easy to understand, but gives frequency characteristics with temporal information required for a real-time application. The variable selection due to the stepwise method also reduces the dimensions of pattern vectors that lead stable classification results. Finally, using a learning vector quantization (LVQ) method to evaluate the classification performance, we successfully achieved a high classification rate even if the odor concentration changed into various conditions whereas, the classification rate was insufficient in the case of using only magnitudes of sensor responses. This shows the robustness of the method against dynamical change of odor concentration. (c) 2007 Published by Elsevier B.V.