Gas sensing beyond classification: Analysis of gas mixtures using multisensor array based on Al-doped zinc oxide

Abstract

Understanding the composition of gas mixtures is still a primary prerogative of complex analytical units or biological olfaction systems. Attempts to mimic the olfactory processes by using a multisensor array combined with machine learning algorithms led mainly to solving a problem of a selective classification of odors or regression over the range of concentrations of the same odor. The identification of individual analytes in a mixture remains a difficult task. In this study, we test the identification of individual chemicals in the composition of the gas mixture with a feature extraction algorithm using a multisensor array based on aluminum-doped zinc oxide. Our approach is based on matching the selected parameters of the response curves which share considerable similarity if a volatile compound is common in any two mixture combinations. We demonstrate the efficiency of the method by analyzing five analytes such as acetone, benzene, methanol, ethanol, and isopropanol, and their mixtures. As a result, we were able to efficiently classify all 31 odors with an accuracy of about 99%. We have achieved the mean values of scores of 0.52, 0.63, and 0.59 reaching up to 0.80–0.86 for the prediction of every individual analyte in 2-, 3- and 4-component gas mixtures, respectively. While using just raw signals at steady state, we found that the results become rather biased as the number of analytes increases in a mixture. Thus, our approach enables an improved, more accurate, and thorough examination of the gas mixtures, expanding the scope of application of multisensor systems beyond the common “classification” tasks.