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Journal ArticleDOI

Fuzzy Logic based Odour Classification System in Electronic Nose

18 Sep 2013-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 78, Iss: 15, pp 18-21

TL;DR: A Sugeno based fuzzy logic classifier for the classification of gases has been presented and has been designed using the Fuzzy toolbox in MATLAB.

AbstractIn this paper, a Sugeno based fuzzy logic classifier for the classification of gases has been presented. The system employs an array of five gas sensors for sensing different gases. The sample space consists of eight gases. A database has been developed using experimentally collected data from the responses of the sensors. The classifier has been designed using the Fuzzy toolbox in MATLAB.

Topics: Fuzzy logic (64%), Classifier (UML) (53%), Electronic nose (51%)

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Citations
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01 Jan 2010

2,172 citations


Journal ArticleDOI
TL;DR: E-nose is discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.
Abstract: Generally, E-nose mimics human olfactory sense to detect and distinguish an odor or gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to the odorant molecules (aroma). Each sensor will respond to their specific gas respectively. These sensors are a major part of the electronic nose to detect gas or odor contained in a volatile component. Information about the gas detected by sensors will be recorded and transmitted to the signal processing unit to perform the analysis of volatile organic compound (VOC) pattern and stored in the database classification, in order to determine the type of odor. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors in an electric signal form. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.

4 citations


Cites methods from "Fuzzy Logic based Odour Classificat..."

  • ...Fuzzy Inference System (FIS) method was proposed by [58] to evaluate a grading of dates based on water contents since dates can be graded as moist, dry, somewhat dry and solid dry....

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DOI
01 Jan 2020
TL;DR: E-nose, it’s principle of work and classification method and in order to classify food freshness are discussed in this paper.
Abstract: Food safety is an important consideration because the reduced quality of freshness may result in food poisoning that can threaten our health. There are many methods used to test the freshness of food such as visual appearance as well using a variety of devices. One of the devices that can be used to test the freshness of food is the E-Nose. E-nose is an instrument that enables the discrimination of gas and odor in food industry for quality and safety purposes. It is a well-established instrument to detect odor and aroma not only in the food industry, but also in health-diagnosis, defense, and environmental industry. Generally, E-nose mimics human olfactory sense to detect and discriminate gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to molecules from vaporous compound. Each sensor will respond to their specific gas respectively. These sensors are the major component in electronic nose to sense and obtain percentage of gases release by the compound samples. All gases detected by sensors will be recorded, that to be analyzed using classification method. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors using a method of machine learning. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.

2 citations


References
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Journal ArticleDOI
01 Apr 1990
TL;DR: The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined and several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated.
Abstract: For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from laboratory level to industrial process control, are briefly reported. Some unsolved problems are described, and further challenges in this field are discussed. >

5,371 citations


Journal Article
TL;DR: The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy.
Abstract: During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory. Fuzzy control is based on fuzzy logic. The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. A survey of the FLC is presented; a general methodology for constructing an FLC and assessing its performance is described; and problems that need further research are pointed out

4,823 citations


01 Jan 2010

2,172 citations


Journal ArticleDOI
Abstract: ‘Electronic nose’ systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odours. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odour quality evaluation of food packaging material.

415 citations


"Fuzzy Logic based Odour Classificat..." refers background in this paper

  • ...An electronic nose refers to an instrument which consists of an array of gas sensors with partial selectivity and an appropriate pattern-recognition system, capable of recognizing simple or complex odours [5]....

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Book
15 Nov 1996
TL;DR: The author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied and culminates in a chapter which describes fuzzy logic control.
Abstract: Fuzzy logic has become an important tool for a number of different applications ranging from the control of engineering systems to artificial intelligence. In this concise introduction, the author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied. The book culminates in a chapter which describes fuzzy logic control: the design of intelligent control systems using fuzzy if-then rules which make use of human knowledge and experience to behave in a manner similar to a human controller. Throughout, the level of mathematical knowledge required is kept basic and the concepts are illustrated with numerous diagrams to aid in comprehension. As a result, all those curious to know more about fuzzy concepts and their real-world application will find this a good place to start.

365 citations


"Fuzzy Logic based Odour Classificat..." refers background in this paper

  • ...One of the best known industrial fuzzy logic applications is the control system of the Sendai underground railway in Japan, utilized by Hitachi Company [2]....

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