Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Non-linear Machine Learning with Active Sampling for MOX Drift Compensation

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI’s HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution. Index Terms—Neural Networks, Extreme Gradient Boosting, XGBoost, Support Vector Machines, Non-Linear Learning Methods, Machine Learnin

A low cost gas phase analysis system for the diagnosis of bacterial infection

Drug resistance is becoming a major concern in both the western world and in developing countries. The over use of common anti-bacterial drugs has resulted in a plethora of multi-drug resistant diseases and an ever reducing number of effective treatments - and is now of major concern to the UK government. One of the major reasons behind this is the difficulty in identifying bacterial infections from viral infections, especially in primary care where patients have an expectation of receiving medication. For most viral conditions, there is no effective treatment and the body fights off the disease, thus prescribing anti-bacterial drugs simply results in the proliferation of drugs within the community - increasing the rate of drug resistance. Increasing drug resistance contributed to the rise of superbugs (drug resistant bacteria) which are expected to kill an about 10 million people a year worldwide by the year 2050 and could result to an economic loss of $63 trillion. Increasing drug resistance contributed to the rise of superbugs (drug resistant bacteria) which are expected to kill an about 10 million people a year worldwide by the year 2050 and could result to an economic loss of$63 trillion. Therefore, there is a strong medical and economic need to develop tools that can diagnose bacterial diseases from viral infections, focused towards primary care. One means of achieving this is through the detection of gas-phase biomarkers IX of disease. It is well known that the metabolic activity of bacteria is significantly different from its host. Many studies have shown that it is possible to detect a bacterial infection, identify the strain and its current life-cycle stage simply by measuring bacterial metabolic emissions. In addition, the human body's response to a bacterial infection is significantly different from a viral infection the human body's response to a bacterial infection is significantly different from a viral infection, allowing human stress markers to also be used for differentiating these conditions. Thus, there is evidence that these bio-markers exist and could be detected. However, a major limiting factor inhibiting the wide-spread deployment of this concept is the unit cost of the analytical instrumentation required for gas analysis. Currently, the main preferred methods are GCMS (gas chromatography/mass spectrometry), TOF-MS (time of flight - MS) and SIFT-MS (selective ion flow tube - MS). Though excellent at undertaking this role, the typical unit cost of these instruments is in excess of $100k, making them out of reach of current GP budgets. Therefore, what is required is a low-cost, portable instrument that can detect bacterial infections from viral infections and be applicable to primary care

Sensores: De los biosensores a la nariz electrónica

The recent advances in sensor devices have allowed the developing of new applications in many technological fields. This review describes the current state-of-the-art of this sensor technology, placing special emphasis on the food applications. The design, technology and sensing mechanism of each type of sensor are analysed. A description of the main characteristics of the electronic nose and electronic tongue (taste sensors) is also given. Finally, the applications of some statistical procedures in sensor systems are described briefly.Los recientes avances en los sistemas de sensores han permitido el desarrollo de nuevas aplicaciones en muchos campos tecnológicos. Este artículo de revisión describe el estado actual de esta nueva tecnología, con especial énfasis en las aplicaciones alimentarias. El diseño, la tecnología y el mecanismo sensorial de cada tipo de sensor son analizados en el artículo. También se describen las principales características de la nariz y la lengua electrónica (sensores de sabor). Finalmente, se describe brevemente el uso de algunos procedimientos estadísticos en sistemas de sensores.Peer reviewe

Sensores: De los biosensores a la nariz electrónica

The recent advances in sensor devices have allowed the developing of new applications in many technological fields. This review describes the current state-of-the-art of this sensor technology, placing special emphasis on the food applications. The design, technology and sensing mechanism of each type of sensor are analysed. A description of the main characteristics of the electronic nose and electronic tongue (taste sensors) is also given. Finally, the applications of some statistical procedures in sensor systems are described briefly.Los recientes avances en los sistemas de sensores han permitido el desarrollo de nuevas aplicaciones en muchos campos tecnológicos. Este artículo de revisión describe el estado actual de esta nueva tecnología, con especial énfasis en las aplicaciones alimentarias. El diseño, la tecnología y el mecanismo sensorial de cada tipo de sensor son analizados en el artículo. También se describen las principales características de la nariz y la lengua electrónica (sensores de sabor). Finalmente, se describe brevemente el uso de algunos procedimientos estadísticos en sistemas de sensores

Biomimetic set up for chemosensor-based machine olfaction

The thesis falls into the field of machine olfaction and accompanying experimental set up for chemical gas sensing. Perhaps more than any other sensory modality, chemical sensing faces with major technical and conceptual challenges: low specificity, slow response time, long term instability, power consumption, portability, coding capacity and robustness. There is an important trend of the last decade pushing artificial olfaction to mimic the biological olfaction system of insects and mammalians. The designers of machine olfaction devices take inspiration from the biological olfactory system, because animals effortlessly accomplish some of the unsolved problems in machine olfaction. In a remarkable example of an olfactory guided behavior, male moths navigate over large distances in order to locate calling females by detecting pheromone signals both rapidly and robustly. The biomimetic chemical sensing aims to identify the key blocks in the olfactory pathways at all levels from the olfactory receptors to the central nervous system, and simulate to some extent the operation of these blocks, that would allow to approach the sensing performance known in biological olfactory system of animals. New technical requirements arise to the hardware and software equipment used in such machine olfaction experiments. This work explores the bioinspired approach to machine olfaction in depth on the technological side. At the hardware level, the embedded computer is assembled, being the core part of the experimental set up. The embedded computer is interfaced with two main biomimetic modules designed by the collaborators: a large-scale sensor array for emulation of the population of the olfactory receptors, and a mobile robotic platform for autonomous experiments for guiding olfactory behaviour. At the software level, the software development kit is designed to host the neuromorphic models of the collaborators for processing the sensory inputs as in the olfactory pathway. Virtualization of the set up was one of the key engineering solutions in the development. Being a device, the set up is transformed to a virtual system for running data simulations, where the software environment is essentially the same, and the real sensors are replaced by the virtual sensors coming from especially designed data simulation tool. The proposed abstraction of the set up results in an ecosystem containing both the models of the olfactory system and the virtual array. This ecosystem can loaded from the developed system image on any personal computer. In addition to the engineering products released in the course of thesis, the scientific results have been published in three journal articles, two book chapters and conference proceedings. The main results on validation of the set up under the scenario of robotic odour localization are reported in the book chapters. The series of three journal articles covers the work on the data simulation tool for machine olfaction: the novel model of drift, the models to simulate the sensor array data based on the reference data set, and the parametrized simulated data and benchmarks proposed for the first time in machine olfaction. This thesis ends up with a solid foundation for the research in biomimetic simulations and algorithms on machine olfaction. The results achieved in the thesis are expected to give rise to new bioinspired applications in machine olfaction, which could have a significant impact in the biomedical engineering research area.Esta tesis se enmarca en el campo de bioingeneria, mas particularmente en la configuración de un sistema experimental de sensores de gases químicos. Quizás más que en cualquier otra modalidad de sensores, los sensores químicos representan un conjunto de retos técnicos y conceptuales ya que deben lidiar con problemas como su baja especificidad, su respuesta temporal lenta, su inestabilidad a largo plazo, su alto consumo enérgético, su portabilidad, así como la necesidad de un sistema de datos y código robusto. En la última década, se ha observado una clara tendencia por parte de los sistemas de machine olfaction hacia la imitación del sistema de olfato biológico de insectos y mamíferos. Los diseñadores de estos sistemas se inspiran del sistema olfativo biológico, ya que los animales cumplen, sin apenas esfuerzo, algunos de los escenarios no resueltos en machine olfaction. Por ejemplo, las polillas machos recorren largas distancias para localizar las polillas hembra, detectando sus feromonas de forma rápida y robusta. La detección biomimética de gases químicos tiene como objetivo identificar los elementos fundamentales de la vía olfativa a todos los niveles, desde los receptores olfativos hasta el sistema nervioso central, y simular, en cierta medida, el funcionamiento de estos bloques, lo que permitiría acercar el rendimiento de la detección al rendimiento de los sistemas olfativos conociodos de los animales. Esto conlleva nuevos requisitos técnicos a nivel de equipamiento tanto hardware como software utilizado en este tipo de experimentos de machine olfaction. Este trabajo propone un enfoque bioinspirado para la ¿machine olfaction¿, explorando a fondo la parte tecnológica. A nivel hardware, un ordenador embedido se ha ensamblado, siendo ésta la parte más importante de la configuración experimental. Este ordenador integrado está interconectado con dos módulos principales biomiméticos diseñados por los colaboradores: una matriz de sensores a gran escala y una plataforma móvil robotizada para experimentos autónomos. A nivel software, el kit de desarrollo software se ha diseñado para recoger los modelos neuromórficos de los colaboradores para el procesamiento de las entradas sensoriales como en la vía olfativa biológica. La virtualización del sistema fue una de las soluciones ingenieriles clave de su desarrollo. Al ser un dispositivo, el sistema se ha transformado en un sistema virtual para la realización de simulaciones de datos, donde el entorno de software es esencialmente el mismo, y donde los sensores reales se sustituyen por sensores virtuales procedentes de una herramienta de simulación de datos especialmente diseñada. La propuesta de abstracción del sistema resulta en un ecosistema que contiene tanto los modelos del sistema olfativo como la matriz virtual . Este ecosistema se puede cargar en cualquier ordenador personal como una imagen del sistema desarrollado. Además de los productos de ingeniería entregados en esta tesis, los resultados científicos se han publicado en tres artículos en revistas, dos capítulos de libros y los proceedings de dos conferencias internacionales. Los principales resultados en la validación del sistema en el escenario de la localización robótica de olores se presentan en los capítulos del libro. Los tres artículos de revistas abarcan el trabajo en la herramienta de simulación de datos para machine olfaction: el novedoso modelo de drift, los modelos para simular la matriz de sensores basado en el conjunto de datos de referencia, y la parametrización de los datos simulados y los benchmarks propuestos por primera vez en machine olfaction. Esta tesis ofrece una base sólida para la investigación en simulaciones biomiméticas y en algoritmos en machine olfaction. Los resultados obtenidos en la tesis pretenden dar lugar a nuevas aplicaciones bioinspiradas en machine olfaction, lo que podría tener un significativo impacto en el área de investigación en ingeniería biomédic

Bacteria classification with an electronic nose employing artificial neural networks

This PhD thesis describes research for a medical application of electronic nose technology. There is a need at present for early detection of bacterial infection in order to improve treatment. At present, the clinical methods used to detect and classify bacteria types (usually using samples of infected matter taken from patients) can take up to two or three days. Many experienced medical staff, who treat bacterial infections, are able to recognise some types of bacteria from their odours. Identification of pathogens (i.e. bacteria responsible for disease) from their odours using an electronic nose could provide a rapid measurement and therefore early treatment. This research project used existing sensor technology in the form of an electronic nose in conjunction with data pre-processing and classification methods to classify up to four bacteria types from their odours. Research was performed mostly in the area of signal conditioning, data pre-processing and classification. A major area of interest was the use of artificial neural networks classifiers. There were three main objectives. First, to classify successfully a small range of bacteria types. Second, to identify issues relating to bacteria odour that affect the ability of an artificially intelligent system to classify bacteria from odour alone. And third, to establish optimal signal conditioning, data pre-processing and classification methods. The Electronic Nose consisted of a gas sensor array with temperature and humidity sensors, signal conditioning circuits, and gas flow apparatus. The bacteria odour was analysed using an automated sampling system, which used computer software to direct gas flow through one of several vessels (which were used to contain the odour samples, into the Electronic Nose. The electrical resistance of the odour sensors were monitored and output as electronic signals to a computer. The purpose of the automated sampling system was to improve repeatability and reduce human error. Further improvement of the Electronic Nose were implemented as a temperature control system which controlled the ambient gas temperature, and a new gas sensor chamber which incorporated improved gas flow. The odour data were collected and stored as numerical values within data files in the computer system. Once the data were stored in a non-volatile manner various classification experiments were performed. Comparisons were made and conclusions were drawn from the performance of various data pre-processing and classification methods. Classification methods employed included artificial neural networks, discriminant function analysis and multi-variate linear regression. For classifying one from four types, the best accuracy achieved was 92.78%. This was achieved using a growth phase compensated multiple layer perceptron. For identifying a single bacteria type from a mixture of two different types, the best accuracy was 96.30%. This was achieved using a standard multiple layer perceptron. Classification of bacteria odours is a typical `real world' application of the kind that electronic noses will have to be applied to if this technology is to be successful. The methods and principles researched here are one step towards the goal of introducing artificially intelligent sensor systems into everyday use. The results are promising and showed that it is feasible to used Electronic Nose technology in this application and that with further development useful products could be developed. The conclusion from this thesis is that an electronic nose can detect and classify different types of bacteria