Electronic noses typically comprise gas sensor arrays, signal-acquisition electronic circuits, and pattern-recognition algorithms. Compared with other similar ones, metal-oxide-semiconductor-based electronic noses offer long lifespans, high accuracy, and competitive cost; they have been widely employed in safety monitoring, food quality monitoring, and so on. High power consumption, complex software, and inconsistent performance in classification and regression currently limit the wider application of this technology. Algorithm optimization is a critical route for enhancing gas analysis performance in electronic noses, as it can reduce extra hardware costs while sustaining high gas recognition performance. This review compares two key algorithm architectures: Artificial Neural Networks and Spiking Neural Networks. A wide range of popular artificial neural networks is covered in detail, including multilayer perceptron, convolutional neural networks, and recurrent neural networks. The article also discusses the pros and cons of these networks, along with the most current findings from their electronic nose applications. Also included are reviews and comparisons of gas classification/regression models that use spiking neural networks, highlighting their architectures and accuracies. Ultimately, we review the current state of gas classification and regression using artificial neural networks and spiking neural networks, and provide the future trends of neural network for electronic nose applications. • Neural networks for electronic nose data processing have been reviewed comprehensively. • The latest progress on attention mechanisms for the electronic nose has been included. • Spiking neural networks for electronic nose data processing have been analyzed.
Guo et al. (Wed,) studied this question.