Gas Recognition in E-nose System

Dear Colleagues,

"Electronic noses" refer to instruments that utilize a mechanism for detecting volatile chemicals and incorporate pattern recognition and machine learning. Since the early 1980s, they have undergone significant advancements in terms of sensor technology, machine learning tools, and an expanding range of potential applications. While gas sensors have traditionally served as the sensing mechanism for electronic noses, there is a growing trend to broaden the concept, including instruments, such as ultra-fast chromatography and ion mobility spectrometry, among others. This broader definition enhances gas recognition capabilities, expanding possibilities, but also increases the need for signal processing. Gas recognition algorithms and workflows play a crucial role, and their ability to extract valuable information is correlated with the correct implementation of preprocessing workflows (denoising, baseline correction, peak alignment, outlier detection, etc.) and processing tools (Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares, k-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, etc.). However, on the other hand, numerous challenging issues arise when dealing with gas recognition in new electronic noses, including the high dimensionality of raw data, the balance between simplicity and performance of algorithms, managing short- and long-term drifts, facing nonlinear responses, multi-gas recognition in noisy environments, and more.

The topics covered in this Special Issue will include both recent advances in gas recognition and improvements in the practical application of electronic noses. Original research articles are welcomed from a broad diversity of disciplines, such as engineering, computer science, machine learning, medicine, analytical science, environmental science, sensors technologies, and chemometrics, to highlight the latest developments in the topic of gas recognition with electronic noses.

This Special Issue will cover, but is not limited to, the following topics:

• Gas recognition for electronic noses;
• Chemometrics, pattern recognition, and machine learning for e-nose instruments;
• Electronic nose application solutions;
• Tools and workflows for preprocessing e-nose raw data.

Dr. Antonio Pardo Martínez
Prof. Dr. Luis Fernandez Romero
Guest Editors

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• gas recognition
• electronic noses
• machine olfaction
• chemical sensing
• chemometrics and signal processing
• pattern recognition and machine learning