A consistent part of gas sensor research activities aims to improve sensing performances by synthesizing new sensing materials, improving the selection of elements in arrays, and optimizing the feature extraction and classification algorithms. This paper combines most of these aspects to confer selectivity to a low-selectivity sensor by using feature extraction algorithms applied to the sensor response kinetics. Several algorithms were employed to represent the kinetic behavior of the sensor response during the adsorption and desorption phases. In detail, we utilized fitting functions, dynamic descriptors, and a mirrored fast Fourier transform (FFT) to extrapolate information from the desorption and adsorption phases. As sensitive material, we synthesized as a receptor a novel Zn(II) phthalocyanine derivative bearing long aliphatic chains, namely, 4-[2,5,7,8-tetramethyl-2-(4,8,12-trimethyl-tridecyl)-chroman-6-yloxy] units that allow good solubilization of the complex in most of the solvents tested and confer a large set of sites to potentially interact with volatile compound (VC) analytes by different portions of the molecular skeleton. After deposition onto a quartz crystal microbalance (QMB) transducer, this single sensor has been tested to recognize five VCs as chemical probes of different chemical families. Remarkably, the results disclose that responses at equilibrium are mainly correlated with nonspecific interactions, while the kinetic parameters are more correlated with stronger and more specific interactions. Finally, the desorption phases bear a higher information content about the chemical identities of the compounds. Eventually, features extracted by a modified version of the FFT obtained the highest clustering (principal component analysis) and classification performances (95% classification accuracy with linear discriminant analysis), suggesting the possibility of developing virtual electronic noses based on a single sensor and a minimal set of descriptors.

中文翻译：

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One Receptor Show：基于 α-生育酚取代的锌酞菁的单个气体传感器特征提取方法的评价


气体传感器研究活动的一贯部分旨在通过合成新的传感材料、改进数组中元件的选择以及优化特征提取和分类算法来提高传感性能。本文结合了这些方面的大部分，通过使用应用于传感器响应动力学的特征提取算法，赋予低选择性传感器选择性。采用了几种算法来表示传感器响应在吸附和解吸阶段的动力学行为。详细地说，我们利用了拟合函数、动态描述符和镜像的快速傅里叶变换 （FFT） 来推断解吸和吸附阶段的信息。作为敏感材料，我们合成了一种带有长脂肪链的新型 Zn（II） 酞菁衍生物作为受体，即 4-[2,5,7,8-四甲基-2-（4,8,12-三甲基-十三烷基）-染色烷-6-基氧基] 单元，允许复合物在大多数测试溶剂中具有良好的溶解性，并赋予大量位点，可能通过分子骨架的不同部分与挥发性化合物 （VC） 分析物相互作用。沉积到石英晶体微天平 （QMB） 传感器上后，该单个传感器已经过测试，可识别五个 VC 作为不同化学家族的化学探针。值得注意的是，结果显示，平衡时的响应主要与非特异性相互作用相关，而动力学参数与更强和更特异性的相互作用更相关。最后，解吸相具有有关化合物化学特性的较高信息含量。 最终，通过修改后的 FFT 版本提取的特征获得了最高的聚类（主成分分析）和分类性能（线性判别分析的 95% 分类精度），这表明了基于单个传感器和最少的描述符集开发虚拟电子鼻的可能性。