One Receptor Show: Evaluation of Feature Extraction Methods for a Single Gas Sensor Based on α-Tocopherol-Substituted Zinc Phthalocyanine.

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.