Machine Learning in Gas Sensing: A Paradigm Shift for Enhanced Detection and Selectivity—A Review

The incorporation of Machine Learning (ML) with gas sensing has brought a revolution in the field by greatly improving the sensing performance, detection accuracy and selectivity. Traditional gas sensors are typically limited by issues that include cross-sensitivity, drift, and environmental fluctuations, which affect their performance. This review investigates how these limitations have been countered using data and advanced algorithmic features in ML. The use of ML in gas sensing mainly involves important steps such as feature extraction and selection, as well as a variety of superior models like Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF). These methodologies enable proper categorization of the types of gases involved, as well as qualitative regression for concentration estimation. The E-nose systems, which integrate gas sensors arrays with machine learning algorithms, are particularly noteworthy due to their ability to mimic the human olfactory system. This combination enables the detection and classification of complex gas mixtures with high accuracy, enhanced selectivity, and robustness against environmental variations—capabilities that conventional single-sensor systems often lack. These applications are most well-known in the fields of food quality inspection, disease detection, and environmental monitoring. Despite recent progress, there are gaps regarding systematic insights about mathematical algorithms, optimization of the gas sensors, and real-world applications of ML in the field of gas sensing. This study reveals that, by overcoming the current problems and proposing future research directions, this work greatly promotes the role of ML in improving the gas sensing technologies to build highly selective, sensitive, and robust sensing systems for various complicated, realistic applications.