Microfluidics in gas sensing and artificial olfaction

SCENT-ERC-2014-STG-639123 (2015-2020) UIDB/04378/2020 PTDC/BII-BIO/28878/2017Rapid, real-time, and non-invasive identification of volatile organic compounds (VOCs) and gases is an increasingly relevant field, with applications in areas such as healthcare, agriculture, or industry. Ideal characteristics of VOC and gas sensing devices used for artificial olfaction include portability and affordability, low power consumption, fast response, high selectivity, and sensitivity. Microfluidics meets all these requirements and allows for in situ operation and small sample amounts, providing many advantages compared to conventional methods using sophisticated apparatus such as gas chromatography and mass spectrometry. This review covers the work accomplished so far regarding microfluidic devices for gas sensing and artificial olfaction. Systems utilizing electrical and optical transduction, as well as several system designs engineered throughout the years are summarized, and future perspectives in the field are discussed.publishersversionpublishe

Odour Detection Methods: Olfactometry and Chemical Sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective “analytical instrument” for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing

Towards the development of an electronic nose.

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.Electronic noses are targeted at determining odour character in a fashion that emulates conscious odour perception in mammals. The intention of this study was to develop an organisational framework for electronic noses and deploy a sample cheese odour discriminator within this framework. Biological olfactory systems are reviewed with the purpose of extracting the organisational principles that result in successful olfaction. Principles of gas handling, chemoreception, and neural processing are considered in the formulation of an organisational framework. An electronic nose is then developed in accordance with the biologically inspired framework. Gas sensing is implemented by an array of six commercially available (tin oxide) semiconductor sensors. These popular gas sensors are known to lack stability thus necessitating hardware and signal processing measures to limit or compensate for instability. An odorant auto-sampler was developed to deliver measured amounts of odorant to the sensors in a synthetic air medium. Each measurement event encodes a simulated sniff, and is captured across six sensor channels over a period of 256 seconds at a sampling rate of 1Hz. The simulated sniff captures sensor base references and responses to odorant introduction and removal. A technique is presented for representation and processing of sensor-array data as a two-dimensional (2D) image where one dimension encodes time, and the other encodes multi-channel sensory outputs. The near optimal, computationally efficient 2D Discrete Cosine Transform (DCT) is used to represent the 2D signal in a decorrelated frequency domain. Several coefficient selection strategies are proposed and tested. A heuristic technique is developed for the selection of transform domain coefficients as inputs to a non-linear neural network based classifier. The benefits of using the selection heuristic as compared to standard variance-based selection are evident in the results. Benefits include: significant dimensionality reduction with concomitant reduction in classifier size and training time, improved generalisation by the neural network and improved classification performance. The electronic nose produced a 99.1% classification rate across a set of seven different cheeses

Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

Using linear support vector machines, we investigated the feature selection problem for the application of all-against-all classification of a set of 20 chemicals using two types of sensors, classical doped tin oxide and zeolite-coated chromium titanium oxide sensors. We defined a simple set of possible features, namely the identity of the sensors and the sampling times and tested all possible combinations of such features in a wrapper approach. We confirmed that performance is improved, relative to previous results using this data set, by exhaustive comparison of these feature sets. Using the maximal number of different sensors and all available data points for each sensor does not necessarily yield the best results, even for the large number of classes in this problem. We contrast this analysis, using exhaustive screening of simple feature sets, with a number of more complex feature choices and find that subsampled sets of simple features can perform better. Analysis of potential predictors of classification performance revealed some relevance of clustering properties of the data and of correlations among sensor responses but failed to identify a single measure to predict classification success, reinforcing the relevance of the wrapper approach used. Comparison of the two sensor technologies showed that, in isolation, the doped tin oxide sensors performed better than the zeolite-coated chromium titanium oxide sensors but that mixed arrays, combining both technologies, performed best

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

Colorimetric sensor arrays for the detection of aqueous and gaseous analytes

The past decade has seen great interest concerning the development of artificial sensing devices; most notably optoelectronic tongues and noses. Utilizing previous research on how the mammalian gustatory and olfactory systems operate, significant progress in mimicking these systems has been realized. The turning point in this field of research has been the discovery that the mammalian senses of smell and taste are not based on specific receptors for each stimulant, but rather an array of semi-specific receptors that function simultaneously to produce a pattern. This pattern is interpreted in the brain, and classified either as a known stimulant or a new analyte similar to a known family of tastes or odors. As a predominantly visual species, we are programmed to acknowledge visible reports to chemical reactions over alternative reporting methods. Thus, colorimetric sensing can be more advantageous than other techniques and can allow for a greater number of chemical reactions to be probed. One colorimetric approach to sensing involves the immobilization of cross-responsive chemosensors capable of showing a color change upon reaction with analytes or mixtures of analytes. The employment of porous glasses as an immobilization technique has allowed for facile detection of analytes, both aqueous and gaseous, by allowing dye-analyte interactions to occur while preventing the sensor dye from escaping from the matrix. In this manner, colorimetric sensor arrays have been fashioned that are capable of discriminating among structurally similar compounds such as sugars, while retaining the ability to detect a wide range of analytes including toxic industrial chemicals. For aqueous detection, the newly developed porous glasses successfully immobilized otherwise soluble dyes that could detect changes in solution pH, caused by boronic acid-diol interactions. This allowed for rapid and sensitive detection and identification of natural and artificial sugars and sweeteners. Further experiments showed the array’s ability to differentiate between a selection of common table-top sweeteners such as Equal®, Sweet’N’Low®, Splenda®, and natural sugars. Gas sensing applications were made possible by slight modifications to the liquid sensing array. Hydrophobic silica precursors were added to limit the effect of changing humidity on the array, and printing onto flat, non-porous polymer surfaces gave fast and easy accessibility of incoming analytes to the immobilized indicators. Stable and sensitive colorimetric arrays for the detection and semi-quantification of a large number of toxic industrial chemicals was made possible by the inclusion of additional indicators capable of colorimetrically reporting changes in polarity, metal ligation, and redox reactions. The performances of these sensing arrays showed extremely low limits of detection, and were capable of identifying toxic gases within a large range of concentrations; ppb up to concentration immediately dangerous to life and health. In order to improve upon the detection limits for weakly responding gaseous analytes, alternative methods were developed. It was found that the immobilization of simple and stable color-changing dyes within chemically-reactive matrices could allow for facile and sensitive detection and quantification of formaldehyde. Optimization studies were carried out to assess the proper doping level of hydrophilic polymers with amine-appended polyethylene glycol

Coding and learning of chemosensor array patterns in a neurodynamic model of the olfactory system

Arrays of broadly-selective chemical sensors, also known as electronic noses, have been developed during the past two decades as a low-cost and high-throughput alternative to analytical instruments for the measurement of odorant chemicals. Signal processing in these gas-sensor arrays has been traditionally performed by means of statistical and neural pattern recognition techniques. The objective of this dissertation is to develop new computational models to process gas sensor array signals inspired by coding and learning mechanisms of the biological olfactory system. We have used a neurodynamic model of the olfactory system, the KIII, to develop and demonstrate four odor processing computational functions: robust recovery of overlapping patterns, contrast enhancement, background suppression, and novelty detection. First, a coding mechanism based on the synchrony of neural oscillations is used to extract information from the associative memory of the KIII model. This temporal code allows the KIII to recall overlapping patterns in a robust manner. Second, a new learning rule that combines Hebbian and anti-Hebbian terms is proposed. This learning rule is shown to achieve contrast enhancement on gas-sensor array patterns. Third, a new local learning mechanism based on habituation is proposed to perform odor background suppression. Combining the Hebbian/anti-Hebbian rule and the local habituation mechanism, the KIII is able to suppress the response to continuously presented odors, facilitating the detection of the new ones. Finally, a new learning mechanism based on anti-Hebbian learning is proposed to perform novelty detection. This learning mechanism allows the KIII to detect the introduction of new odors even in the presence of strong backgrounds. The four computational models are characterized with synthetic data and validated on gas sensor array patterns obtained from an e-nose prototype developed for this purpose

Cooperative strategies for the detection and localization of odorants with robots and artificial noses

En este trabajo de investigación se aborda el diseño de una plataforma robótica orientada a la implementación de estrategias de búsqueda cooperativa bioinspiradas. En particular, tanto el proceso de diseño de la parte electrónica como hardware se han enfocado hacia la validación en entornos reales de algoritmos capaces de afrontar problemas de búsqueda con incertidumbre, como lo es la búsqueda de fuentes de olor que presentan variación espacial y temporal. Este tipo de problemas pueden ser resueltos de forma más eficiente con el empleo de enjambres con una cantidad razonable de robots, y por tanto la plataforma ha sido desarrollada utilizando componentes de bajo coste. Esto ha sido posible por la combinación de elementos estandarizados -como la placa controladora Arduino y otros sensores integrados- con piezas que pueden ser fabricadas mediante una impresora 3D atendiendo a la filosofía del hardware libre (open-source). Entre los requisitos de diseño se encuentran además la eficiencia energética -para maximizar el tiempo de funcionamiento de los robots-, su capacidad de posicionamiento en el entorno de búsqueda, y la integración multisensorial -con la inclusión de una nariz electrónica, sensores de luminosidad, distancia, humedad y temperatura, así como una brújula digital-. También se aborda el uso de una estrategia de comunicación adecuada basada en ZigBee. El sistema desarrollado, denominado GNBot, se ha validado tanto en los aspectos de eficiencia energética como en sus capacidades combinadas de posicionamiento espacial y de detección de fuentes de olor basadas en disoluciones de etanol. La plataforma presentada -formada por el GNBot, su placa electrónica GNBoard y la capa de abstracción software realizada en Python- simplificará por tanto el proceso de implementación y evaluación de diversas estrategias de detección, búsqueda y monitorización de odorantes, con la estandarización de enjambres de robots provistos de narices artificiales y otros sensores multimodales.This research work addresses the design of a robotic platform oriented towards the implementation of bio-inspired cooperative search strategies. In particular, the design processes of both the electronics and hardware have been focused towards the real-world validation of algorithms that are capable of tackling search problems that have uncertainty, such as the search of odor sources that have spatio-temporal variability. These kind of problems can be solved more efficiently with the use of swarms formed by a considerable amount of robots, and thus the proposed platform makes use of low cost components. This has been possible with the combination of standardized elements -as the Arduino controller board and other integrated sensors- with custom parts that can be manufactured with a 3D printer attending to the open-source hardware philosophy. Among the design requirements is the energy efficiency -in order to maximize the working range of the robots-, their positioning capability within the search environment, and multiple sensor integration -with the incorporation of an artificial nose, luminosity, distance, humidity and temperature sensors, as well as an electronic compass-. Another subject that is tackled is the use of an efficient wireless communication strategy based on ZigBee. The developed system, named GNBot, has also been validated in the aspects of energy efficiency and for its combined capabilities for autonomous spatial positioning and detection of ethanol-based odor sources. The presented platform -formed by the GNBot, the GNBoard electronics and the abstraction layer built in Python- will thus simplify the processes of implementation and evaluation of various strategies for the detection, search and monitoring of odorants with conveniently standardized robot swarms provided with artificial noses and other multimodal sensors