Machine olfaction

An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals.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:[1] quality control in food processing, detection and diagnosis in medicine,[2] detection of drugs, explosives and other dangerous or illegal substances,[3] 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.[4] Another challenge in current research on machine olfaction is the need to predict or estimate the sensor response to aroma mixtures.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.[7] Electronic noses are able to discriminate between odors and volatiles from a wide range of sources.The list below shows just some of the typical applications for electronic nose technology – many are backed by research studies and published technical papers.It is vitally important for all living beings for both finding sustenance and avoiding danger.Various sensors have been developed and a variety of algorithms have been proposed for diverse environments and conditions.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.[12] In this process, the odor sensor simply compares concentration information from different locations.However, when the current state condition is worse than the previous one, the robot will backtrack then move in another random direction.This method is simple and efficient, however, the length of the path is highly variable and missteps increase with proximity to the source.[further explanation needed] Another method based on the diffusion model is the hex-path algorithm, developed by R. Andrew Russel[12] for underground chemical odor localization with a buried probe controlled by a robotic manipulator.[12][13] The probe moves at a certain depth along the edges of a closely packed hexagonal grid.At each state junction n, there are two paths (left and right) for choosing, and the robot will take the path that leads to higher concentration of the odor based on the previous two junction states odor concentration information n−1, n−2.The least square method (LSM) is a slightly complicated algorithm for odor localization.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.Another method based on plume modeling is maximum likelihood estimation (MLE).With these matrices, the plume-based odor detection model can be expressed with the following equation:Then the MLE can be applied to the modeling and form the probability density functionIn 2007, a strategy called infotaxis was proposed in which a mental model is created utilizing previously collected information about where a smell's source is likely to be.It has been implemented as a partially observable Markov decision process[16] with a stationary target in a two-dimensional grid.