National Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, Japan.
Department of Information Science and Technology, Aichi Prefectural University, 1522-3 Ibaragabasama, Nagakute 480-1198, Aichi, Japan.
Sensors (Basel). 2020 May 8;20(9):2687. doi: 10.3390/s20092687.
We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, -decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.
我们研究了目标挥发性有机化合物（VOC）的选择性检测，这些 VOC 是与年龄相关的体臭（即 2-壬烯醛、壬酸和双乙酰）和真菌气味（即乙酸），同时存在来自汽车内饰的干扰 VOC（即 -癸烷和乙酸丁酯）。我们使用了八个半导体气体传感器作为传感器阵列；使用机器学习、主成分分析（PCA）和线性判别分析（LDA）作为降维方法来分析它们的信号；k-最近邻（kNN）分类来评估目标气体确定的准确性；随机森林和 ReliefF 特征选择来从我们的传感器阵列中选择合适的传感器。传感器对每种目标气体和污染物气体的响应的 PCA 和 LDA 得分通常在每个目标气体的区域内；因此；几乎可以实现对每种目标气体的区分。随机森林和 ReliefF 有效地减少了所需传感器的数量，kNN 通过每个传感器集验证了目标气体区分的质量。
