Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9–4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.

Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.

Artificial olfaction is a fast growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition where volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. As a result, electrical signals are transduced to the brain where pattern recognition is performed. An efficient approach in artificial

Artificial olfaction is a fast growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition where volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. As a result, electrical signals are transduced to the brain where pattern recognition is performed. An efficient approach in artificial

Artificial olfaction is a fast growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition where volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. As a result, electrical signals are transduced to the brain where pattern recognition is performed. An efficient approach in artificial

Artificial olfaction is a fast growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition where volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. As a result, electrical signals are transduced to the brain where pattern recognition is performed. An efficient approach in artificial