A Virtual Electronic Nose for the Efficient Classification and Quantification of Volatile Organic Compounds

Abstract

Although many chemical gas sensors report high sensitivity towards volatile organic compounds (VOCs), finding selective gas sensing technologies that can classify different VOCs is an ongoing and highly important challenge. By exploiting the synergy between virtual electronic noses and machine learning techniques, we demonstrate the possibility of efficiently discriminating, classifying, and quantifying short-chain oxygenated VOCs in the parts-per-billion concentration range. Several experimental results show a reproducible correlation between the predicted and measured values. A 10-fold cross-validated quadratic support vector machine classifier reports a validation accuracy of 91% for the different gases and concentrations studied. Additionally, a 10-fold cross-validated partial least square regression quantifier can predict their concentrations with coefficients of determination, R2, up to 0.99. Our methodology and analysis provide an alternative approach to overcoming the issue of gas sensors’ selectivity, and have the potential to be applied across various areas of science and engineering where it is important to measure gases with high accuracy.

Advanced Field-Effect Sensors

“…Particular attention is given to photo-activated gas detectors with a high selectivity for nitric oxide, nitric dioxide, formaldehyde, and ammonia gases. Domènech-Gil and coworkers deal with the development of a technology for selective gas sensing [23]. Virtual electronic noses are combined with machine learning techniques to discriminate between oxygenated volatile organic compounds in the parts-per-billion concentration range.…”

Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen–Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling

“…A portable and low-cost electronic nose (Figure 1) developed by the Digital Agriculture Food and Wine Group from the University of Melbourne (DAFW-UoM) was used to assess the production of volatile organic compounds [20][21][22][23][24] from processing tomato plants with different treatments and a control. This e-nose consists of an array of nine sensors that are sensitive to different volatile compounds: (i) MQ3 (alcohol), (ii) MQ4 (methane: CH4), (iii) MQ7 (carbon monoxide: CO), (iv) MQ8 (hydrogen: H2), (v) MQ135 (ammonia/alcohol/benzene), (vi) MQ136 (hydrogen sulfide: H 2 S), (vii) MQ137 (ammonia: NH 3 ), (viii) MQ138 (benzene/alcohol/ammonia), and (ix) MG811 (carbon dioxide: CO 2 ), as well as a humidity and temperature sensor to measure the ambient conditions (Henan Hanwei Electronics Co., Ltd., Henan, China).…”

Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions

“…Thus, innovative techniques to obtain the benefits of e-noses while overcoming their flaws must be investigated. In this work, we approach this challenge using the so-called virtual sensor array [3,4] as a simple, flexible, and cost-effective alternative for gas-sensing applications. We demonstrate that data treatment can overcome the influence of relative humidity in the signal of an underperforming sensor and discriminate among similar volatile organic compounds (VOCs).…”