Abstract
Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.
Highlights
Gas sensing is an emerging field in medicine, for the development of non-invasive diagnostic tools [1,2,3,4]
The hybrid gelatin gels described in this work add an extra component to ionogels as they consist of gelatin, the IL [BMIM][DCA], and the LC 5CB in the presence of water (~10% [w/w]) [18]
The initial radial configuration is recovered when exposed to clean air, which is clearly observed by polarized optical microscopy (POM) (Fig. 1c)
Summary
Gas sensing is an emerging field in medicine, for the development of non-invasive diagnostic tools [1,2,3,4]. The signals are further processed and analyzed using pattern recognition tools, which allow the identification of characteristic VOC profiles associated to a particular sample. E-noses conceptually mimic the sense of olfaction, in a perfect combination between chemical sensing and artificial intelligence, mirroring the biological orchestra of olfactory proteins and the intricate brain computing processes used in odor recognition [5]. The most conventional e-nose sensing materials are metal oxide semiconductors and synthetic conducting polymers. These are associated with low selectivity, sensor drift, and high operating temperatures [6], which has been triggering the search for alternative gas sensors, either through the incorporation of biological components from natural olfaction or through the use of stimuli-responsive biomaterials, including gelatin [7]
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