Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose

Szulczyński, Bartosz; Gębicki, Jacek; Namieśnik, Jacek

doi:10.1051/e3sconf/20182801036

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Blurring the boundaries between them gives the opportunity to come up with values between this interval (e.g., almost false , half truth ). The proposed scheme of using fuzzy logic to estimate the odor intensity is presented in Figure 3 and described in previous research [41,42].…”

Section: Methodsmentioning

confidence: 99%

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic

Szulczyński

Gębicki

2019

Sensors

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Measurement and monitoring of air quality in terms of odor nuisance is an important problem. From a practical point of view, it would be most valuable to directly link the odor intensity with the results of analytical air monitoring. Such a solution is offered by electronic noses, which thanks to the possibility of holistic analysis of the gas sample, allow estimation of the odor intensity of the gas mixture. The biggest problem is the occurrence of odor interactions between the mixture components. For this reason, methods that can take into account the interaction between components of the mixture are used to analyze data from the e-nose. In the presented study, the fuzzy logic algorithm was proposed for determination of odor intensity of binary mixtures of eight odorants: n-Hexane, cyclohexane, toluene, o-xylene, trimethylamine, triethylamine, α-pinene, and β-pinene. The proposed algorithm was compared with four theoretical perceptual models: Euclidean additivity, vectorial additivity, U model, and UPL model.

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Section: Methodsmentioning

confidence: 99%

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic

Szulczyński

Gębicki

2019

Sensors

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“…In comparison to these findings, adaptive neuro‐fuzzy inference system and support vector machine models showed their robustness in presenting satisfactory prediction performances for either aerobic or MAP conditions and could have great impact as an important detection methodology in evaluating meat microflora. Szulczyński et al applied fuzzy logic algorithms to assess the odor intensity of gas mixtures of α‐pinene, TMA, and toluene via e‐nose prototype. The e‐nose sensor in conjunction with fuzzy logic pattern recognition system was successfully employed to determine the odor intensity of gas mixtures by comparing the results with the values obtained by sensory analysis, and multiple linear regression model.…”

Section: Sensors Used In Meat Industrymentioning

confidence: 99%

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

et al. 2018

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The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.

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Section: Introductionmentioning

confidence: 99%

“…The problem of classification of "odours" has been successfully treated, in particular with fuzzy logic [2]- [7]. To carry out this task, different descriptors are used for classification, such as the physico-chemical quantities of the molecules [3], [5], the characteristics resulting from the responses of sensory sensors [4], [7], or even sensory analyses carried out by experts [2]. Although they all rely on the formalism of fuzzy logic, the classification methods used are also very varied, ranging from neural networks like Fuzzy ARTMAP [4], to methods of fuzzy rules induction [2], [5] and modeling by experts [3], [7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Classifiers for Chemical Compound Recognition from SAW Sensors Signals

Friedmann

Poli

Hotel

et al. 2020

2020 IEEE 32nd International Conference on Tools With Artificial Intelligence (ICTAI)

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Chemical vapor analysis devices are booming, thanks to a growing need in areas such as security and quality control. These control devices are based on various technologies that are the subject of important researches in an ever-growing community of physicists and electronics. However, the data from these sensors are often processed by conventional algorithms poorly configured for the purpose of automatically recognizing target chemical compounds. These algorithms are often based on statistical models that are not always adapted to a limited number of learning data and demonstrated reproducibility problems for these kind of sensors. In this article, we propose to train fuzzy models and compare their performances with the classical methods of the state of the art, to show how practical they can be for such applications. Three different uses cases will be studied: toxic chemicals recognition, detection of counterfeit coffee capsules and detection of a chemical weapon among everyday products.

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Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose

Cited by 6 publications

References 13 publications

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

Fuzzy Classifiers for Chemical Compound Recognition from SAW Sensors Signals

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