Machine olfaction

Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of (potentially numerous) odorant compounds. The technology, though still in the early stages of development, promises many applications, such as: quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring. One type of proposed machine olfaction technology is via gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds. However, a critical element in the development of these instruments is pattern analysis, and the successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation.…

Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of (potentially numerous) odorant compounds. The technology, though still in the early stages of development, promises many applications, such as: quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring. One type of proposed machine olfaction technology is via gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds. However, a critical element in the development of these instruments is pattern analysis, and the successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. Another challenge in current research on machine olfaction is the need to predict or estimate the sensor response to aroma mixtures. Some pattern recognition problems in machine olfaction such as odor classification and odor localization can be solved by using time series kernel methods. ==Detection== There are three basic detection techniques using conductive-polymer odor sensors (polypyrrole), tin-oxide gas sensors, and quartz-crystal micro-balance sensors. They generally comprise (1) an array of sensors of some type, (2) the electronics to interrogate those sensors and produce digital signals, and (3) data processing and user interface software. The entire system is a means of converting complex sensor responses into a qualitative profile of the volatile (or complex mixture of chemical volatiles) that make up a smell, in the form of an output. Conventional electronic noses are not analytical instruments in the classical sense and very few claim to be able to quantify an odor. These instruments are first 'trained' with the target odor and then used to 'recognize' smells so that future samples can be identified as 'good' or 'bad'. Research into alternative pattern recognition methods for chemical sensor arrays has proposed solutions to differentiate between artificial and biological olfaction related to dimensionality. This biologically-inspired approach involves creating unique algorithms for information processing. Electronic noses are able to discriminate between odors and volatiles from a wide range of sources and quality. The list below shows just some of the typical applications for electronic nose technology – many are backed by research studies and published technical papers. \frac{\partial C}{\partial t} =D\frac{\partial^2 C}{\partial d^2} where is the diffusion constant, is distance in the diffusion direction, is chemical concentration and is time. Assuming the chemical odor flow only disperses in one direction with a uniform cross-section profile, the relationship of odor concentration at a certain distance and certain time point between odor source concentrations is modeled as \frac{C}{C_s}=\operatorname{erfc}\frac{d}{\sqrt{4Dt}} where C_s is the odor source concentration. This is the simplest dynamic equation in odor detection modeling, ignoring external wind or other interruptions. Under the diffusion-dominated propagation model, different algorithms were developed by simply tracking chemical concentration gradients to locate an odor source. \frac{\partial C}{\partial t} =D_x\frac{\partial^2 C}{\partial x^2}+D_y\frac{\partial^2 C}{\partial y^2}+\alpha\frac{\partial C}{\partial x}+\beta\frac{\partial C}{\partial y} where and are diffusion constants; \alpha is the linear wind velocity in the direction, \beta is the linear wind velocity in the direction. Additionally assuming that the environment is uniform and the plume source is constant, the equation for odor detection in each robot sensor at each detection time point is R_i=\gamma_i\sum_{k=1}^{K}{\frac{C_k}{\|{\rho_k-r_k}\|^\alpha}}+\omega_i where R_i is the sample of sensor, \gamma_i is gain factor, C_k is source intensity, \rho_k is the location of source, \alpha is plume attenuation parameter, \omega_i is background noise that satisfies N(\mu,\sigma^2) . Under plume modeling, different algorithms can be used to localize the odor source. (x_1-x_s)^2+(y_1-y_s)^2=R_1/(\gamma_1C) (x_2-x_s)^2+(y_2-y_s)^2=R_2/(\gamma_2C) (x_3-x_s)^2+(y_3-y_s)^2=R_3/(\gamma_3C) R_i,_t=\gamma_i{\frac{C}{\|{\rho_k-r_i}\|^2}}+\omega_i=\gamma_i{\frac{C}{d^2}}+\omega_i where d_i is the Euclidean distance between the sensor node and the plume source, given by: d_i=\sqrt{(x_i-x-s)^2+(y_i+y_s)^2} The main difference between the LSM algorithm and the direct triangulation method is the noise. In LSM, noise is considered, and the odor source location is estimated by minimizing the squared error. The nonlinear least square problem is given by: J=\sum_{i=1}^{N}({\frac{C}{\sqrt{(x_i-\widehat{x_s})^2(y_i-\widehat{y_s})^2}}-\overline{z_l})} where (\widehat{x_s},\widehat{y_s}) is the estimated source location and \overline{z_l} is the average of multiple measurements at the sensors, given by: \overline{z_l}=\frac{1}{M}\sum_{i=1}^M{z_i} Z=[\frac{R_1-\mu_1}{\sigma_1}, \frac{R_2-\mu_2}{\sigma_2}, ...\frac{R_N-\mu_N}{\sigma_N}] G=diag[\frac{\gamma_1}{\sigma_1}, \frac{\gamma_2}{\sigma_2}, ...\frac{\gamma_N}{\sigma_N}] D=[\frac{1}{d_1^2}, \frac{1}{d_2^2}, ...\frac{1}{d_N^2}] \zeta=[\zeta_1, \zeta_2,...\zeta_N] \zeta_i=(\omega_i-\mu_i)/\sigma_i\sim N(0,1) \frac{R_i-\mu_i}{\sigma_i}\sim N(\frac{\gamma_i}{\sigma_i}\frac{C}{d_i^2},1) With these matrices, the plume-based odor detection model can be expressed with the following equation: Z=GDC+\zeta Then the MLE can be applied to the modeling and form the probability density function f(Z,\theta)=2\pi^{-(N/2)}e^{-\frac{1}{2}(Z-GDC)^T(Z-GDC)} where \theta is the estimated odor source position, and the log likelihood function is L(\theta)\sim \frac{1}{2}\sum_{i=1}^N{\|Z_i-\gamma_i\frac{c}{d_i^2}\|}=\frac{1}{2}\sum_{i=1}^N{(\frac{Ri-\mu_i}{\sigma_i}-\gamma_i\frac{c}{d_i^2})^2} The maximum likelihood parameter estimation of \theta can be calculated by minimizing I(\theta)=\sum_{i=1}^N{(\frac{Ri-\mu_i}{\sigma_i}-\gamma_i\frac{c}{d_i^2})^2} and the accurate position of the odor source can be estimated by solving: \frac{\partial{I(\theta)}}{\partial(x)}=0, \frac{\partial{I(\theta)}}{\partial(y)}=0 ==See also== ==References== ==External links== Electronic Nose Technologies from Scensive Technologies Ltd, UK

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