Utilizing Machine Learning for Rapid Discrimination and Quantification of Volatile Organic Compounds in an Electronic Nose Sensor Array
Abstract
Volatile organic compounds (VOCs) are ubiquitous in the surroundings, originating from both industrial and natural sources. VOCs directly impact the quality of both indoor and outdoor air and play a significant role in processes such as fruit ripening and the body’s metabolism. VOC monitoring has seen significant growth recently, with an emphasis on developing low-cost, portable sensors capable of both vapor discrimination and concentration measurements. VOC sensing remains challenging, mainly because these compounds are nonreactive, appear in low concentrations and share similar chemical structures that results in poor sensor selectivity. Therefore, individual gas sensors struggle to selectively detect target VOCs in the presence of interferences. Electronic noses overcome these limitations by employing machine learning for pattern recognition from arrays of gas sensors. Here, an electronic nose fabricated with four types of functionalized gold nanoparticles demonstrates rapid detection and quantification of eight types of VOCs at four concentration levels. A robust two-step machine learning pipeline is implemented for classification followed by regression analysis for concentration prediction. Random Forest and support vector machine classifiers show excellent results of 100% accuracy for VOC discrimination, independent of measured concentration levels. Each Random Forest regression analysis exhibits high R2 and low RMSE with an average of 0.999 and 0.002, respectively. These results demonstrate the ability of gold nanoparticle gas sensor arrays for rapid detection and quantification.
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