Odor Discrimination System Research Articles

Design of odor discrimination system based on insect electroantennogram responses

Design of odor discrimination system based on insect electroantennogram responses

Sensor characterization for multisensor odor-discrimination system

In recent years, with the advent of new and cheaper sensors, the use of olfactory systems in homes, industries, and hospitals has a new start. Multisensor systems can improve the ability to distinguish between complex mixtures of volatile substances. To develop multisensor systems that are accurate and reliable, it is important to take into account the anomalies that may arise because of electronic instabilities, types of sensors, and air flow. In this approach, 32 metal oxide semiconductor sensors of 7 different types and operating at different temperatures have been used to develop a multisensor olfactory system. Each type of sensor has been characterized to select the most suitable temperature combinations. In addition, a prechamber has been designed to ensure a good air flow from the sample to the sensing area. The multisensor system has been tested with good results to perform multidimensional information detection of two fruits, based on obtaining sensor matrix data, extracting three features parameters from each sensor curve and using these parameters as the input to a pattern recognition system.

Artificial Odor Discrimination System Using Electronic Nose and Neural Networks for the Identification of Urinary Tract Infection

Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

Unknown odor recognition using Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm

To deal with unknown odor recognition problem for a developed artificial odor discrimination system, Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm (EF-SONIA) is proposed. Euclidean fuzzy similarity enables a zero similarity calculation between an unknown odor vector and hidden unit vectors, so that the system can recognize the unknown odor. In addition, an elliptical approach for fuzziness determination is proposed. The elliptical approach can approximate an appropriate fuzziness, so that the unknown odor recognition accuracy is improved. Experiments on three datasets of three-mixture vegetal odors show that the recognition accuracy of the proposed method is 20% better than those of the conventional method. The system is very promising to be used for a real development of dog robot that enables localization and identification of dangerous natural gas.

Artificial odor discrimination system using multiple quartz resonator sensors and various neural networks for recognizing fragrance mixtures

An electronic odor discrimination system had been developed by using four quartz resonator-sensitive membranes basic-resonance frequencies at 10 MHz as a sensor and analyzed the measurement data through a back propagation (BP) as the pattern recognition system. The developed system showed high recognition probability to discriminate various single odors to its high generality properties; however, the system had a limitation in recognizing the fragrances mixture. This system also had other disadvantages, such as classifying the unknown category of odor as the known category of odor. In order to improve the performance of the proposed system, development of the sensor and other neural networks (NNs) are being sought. This paper explains the improvement of the capability of that system. In this experiment, the improvement is conducted not only by replacing the last hardware system from four quartz resonator-basic resonance frequencies at 10 MHz with new 16 quartz resonator-basic resonance frequencies at 20 MHz, but also by replacing the pattern classifier from BP NNs with the variance of BP, probabilistic NNs, and fuzzy-neuro learning vector quantization (FNLVQ). Matrix similarity analysis (MSA) is then proposed to increase the accuracy of the FNLVQ, to become FNLVQ-MSA neural systems in determining the best exemplar vector, for speeding up its convergence. The purpose of the recent study is to construct a new artificial odor discrimination system for recognizing the fragrance mixtures, in addition to recognizing the unknown fragrance mixtures. The use of new sensing systems and FNLVQ-MSA has produced higher capability, compared to the previously mentioned system.

A fuzzy-similarity-based self-organized network inspired by immune algorithm for three-mixture-fragrance recognition

A fuzzy-similarity-based self-organized network inspired by immune algorithm (F-SONIA) is proposed in order to develop an artificial odor discrimination system for three-mixture-fragrance recognition. It can deal with an uncertainty in frequency measurements, which is inherent in odor acquisition devices, by employing a fuzzy similarity. Mathematical analysis shows that the use of the fuzzy similarity results on a higher dissimilarity between fragrance classes, therefore, the recognition accuracy is improved and the learning time is reduced. Experiments show that F-SONIA improves recognition accuracy of SONIA by 3%-9% and the previously developed artificial odor discrimination system by 14%-25%. In addition, the learning time of F-SONIA is three times faster than that of SONIA.

Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

Abstract The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.