Improving the Robustness of Artificial Olfaction Systems by Multivariate Signal Processing

Marta Padilla
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Information about the environment is essential for the survival of every living being, since it determines the way to react with respect to external inputs. In the case of humans, this information is collected through the five senses; sight, hearing, touch, smell and taste. Sight, hearing and smell senses are considered specially interesting because of their ability to get external information without direct interaction with the sources. Sight is probably the most developed sense in humans, followed closely by hearing. On the contrary, smell has always been considered the most primitive and less important of those three senses, and was an almost total mystery until a few years ago. Recently, in 2004, Dr. Richard Axel and Dr. Linda Buck shared the Nobel Prize in medicine for their discoveries of odorant receptors and the organization of the olfactory system, since then, studies on the smell and human olfactory system have received a new interest. With the aim of extending our senses abilities of obtaining information about our surroundings, some devices have been developed which are inspired by the biological mechanisms of the senses. Examples of such devices are well known and of very extended use, like video cameras used for security issues in which image recognition can be implemented (sight), or sound recording with speech recognition (hearing). Relating smell, an instrument named electronic nose was proposed in 1982 by Persaud and Dodd [1] to differentiate odours. Electronic noses were very promising for many qualitative and quantitative applications, since they were expected to provide characteristics such as being of small size, low cost, fast and easy to use. These features are specially interesting for on-field applications, compared to other wellestablished instruments for gas/volatiles analysis which are big, heavy, expensive and difficult to use, though they provide better chemical resolution. Despite the many potential advantages of the use of electronic noses, nowadays, more than 25 years after the first device, this instrument is not massively present on the market. The main reason lies in the sensing area of the instrument, which exhibits poor selectivity and bad stability. The chemical gas sensors used in electronic noses present problems like cross sensitivities, time instability, dependence on previous gas exposures, etc. Therefore, instruments based on these sensors are not robust and do not give enough reproducible results. The nature of the problems that influence chemical gas sensors is mainly technological, but affect sensors of all state of the art technologies, though to different degrees. These deficiencies can be mostly overcome as more research is made on improving fabrication process or developing new technologies. However, while gas sensors technologies are being improved, statistical signal processing can help to mathematically compensate, or at least to reduce, the effect those mentioned issues have on the sensors responses before pattern recognition is carried out. The aim of this thesis is to explore the robustness of some sensor operation methods, and to propose the use of statistical signal processing techniques to correct or compensate sensors responses affected by specific problems, such as sensor drift and failure of one or more sensors in the array. This document is organized in five chapters. In the first one, the concepts of machine olfaction and electronic noses are revised, as well as the main problems that the instrument presents. Specially, evidence of specific issues studied in this thesis and state of the art on solutions by signal processing techniques are presented in this chapter. Concepts and definitions of robustness are given in chapter two. In the third chapter, the aim of this dissertation is detailed. Then, chapter fourth explains the work made and presents the papers which shows many, but not all, of the results obtained during these years of work. Finally, conclusions are given in chapter five.
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