Electronic nose and artificial neural network

Haugen, John‐Erik; Kvaal, Knut

doi:10.1016/s0309-1740(98)90054-7

Cited by 108 publications

(44 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The classification of samples with different fatty acid compositions was performed by using a DA, and an ANN, given that they currently seem to be the most useful techniques for classifying samples using sensor data from electronic noses (16). Three variables were chosen by DA (Table 2), with the last variable providing only a small improvement in the model.…”

Section: Resultsmentioning

confidence: 99%

An electronic nose to classify Iberian pig fats with different fatty acid composition

Carrapiso

Ventanas

Jurado

et al. 2001

J Americ Oil Chem Soc

View full text Add to dashboard Cite

Fatty acid analysis is frequently performed in fat and other raw materials to classify them according to their fatty acid composition, but the need to carry out online determinations has generated a growing interest in more rapid options. This research was done to evaluate the ability of a polymer-sensor based electronic nose to classify Iberian pig fat samples with different fatty acid compositions. Significant correlations were found between individual fatty acids and sensor responses, proving that sensor response data were not fortuitously sorted. Significant correlations also appeared between some sensors and water activity, which was considered during the sample classification. Two supervised pattern recognition techniques were attempted to process the sensor responses: 85.5% of the samples were correctly classified by discriminant analysis, but the percentage increased to 97.8% using a one-hidden layer back-propagation artificial neural network. The electronic nose (specifically, sensor responses analyzed by a neural network) achieved success similar to that obtained using the more usual fatty acid analysis by gas chromatography.Paper no. J9704, JAOCS 78, 415-418 (April 2001).

show abstract

Section: Resultsmentioning

confidence: 99%

An electronic nose to classify Iberian pig fats with different fatty acid composition

Carrapiso

Ventanas

Jurado

et al. 2001

J Americ Oil Chem Soc

View full text Add to dashboard Cite

show abstract

“…Two basic approaches, multivariate data analysis and artificial neural networks, are commonly used. Principal component analysis (PCA), discriminant function analysis (DFA), cluster analysis (CA), partial least squares regression (PLSR), canonical correlation analysis (CCA), and fuzzy logic or artificial neural networks (ANN) are most frequently used as pattern recognition techniques [57][58][59][60].…”

Section: Smell-related Qualitymentioning

confidence: 99%

Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

Korel

Balaban

2010

Handbook of Seafood Quality, Safety and Health Applications

View full text Add to dashboard Cite

The increase in demand for seafood products has catalyzed the desire for higher standards regarding safety and quality issues. Since seafoods are perishable, freshness is a major quality parameter to be considered [1,2]. There is no unique freshness or spoilage indicator for seafood, therefore combinations of selected indicators need to be used to evaluate freshness [3,4]. An important and widely used method to determine freshness is sensory evaluation [5]. The Quality Index Method (QIM) uses a demerit point scoring system [6] based on the evaluation of the important sensory attributes (odour, texture, and appearance) of fish and other aquatic foods. The sensory quality is expressed by the sum of the demerit points, and a linear correlation between these points and the storage time is used to predict the freshness of the target seafood [5,7,8]. The QIM has been developed for various seafood species and products, such as Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) [9], gilthead seabream (Sparus aurata)[10], farmed Atlantic salmon (Salmo salar) [11,12], and cod (Gadus morhua) [13], etc. Even though QIM is fast and reliable in determining the freshness of seafood, it still requires experts to evaluate the quality attributes. Alternatively, appearance, odour, and taste can be measured by machine vision system (MVS), electronic nose (e-nose), and electronic tongue (e-tongue), respectively.In this chapter, the measurement of visual, odour, and taste quality of seafood using MVS, e-nose, and e-tongue is discussed. A few literature examples are given for all techniques, some of which are given from research conducted in our own laboratories. Visual qualityVisual quality of seafood includes appearance (size, shape, and colour) attributes. These have a direct influence on the seafood's value and acceptance. One of the methods of measuring them is by using a MVS, which consists of a digital camera to acquire images, an illumination system (e.g. a light box with fluorescent bulbs as lighting source), and computer software to analyze the image [14,15]. This is a rapid, objective, repeatable, and non-destructive method, Handbook of Seafood Q uality, Safety and Health ApplicationsEdited by Cesarettin Alasalvar, Fereidoon Shahidi, Kazuo Miyashita and Udaya Wanasundara

show abstract

“…They cannot be eliminated via simple transformation of the data. Variations among samples are too small to establish valid statistic models, causing those linear methods to lose robustness when employed for information processing in eNoses [17] .…”

Section: Technology Of Information Processing In Artificial Olfactionmentioning

confidence: 99%

Progress in bionic information processing techniques for an electronic nose based on olfactory models

Zheng

2009

Chin. Sci. Bull.

View full text Add to dashboard Cite

As a novel bionic analytical technique, an electronic nose, inspired by the mechanism of the biological olfactory system and integrated with modern sensing technology, electronic technology and pattern recognition technology, has been widely used in many areas. Moreover, recent basic research findings in biological olfaction combined with computational neuroscience promote its development both in methodology and application. In this review, the basic information processing principle of biological olfaction and artificial olfaction are summarized and compared, and four olfactory models and their applications to electronic noses are presented. Finally, a chaotic olfactory neural network is detailed and the utilization of several biologically oriented learning rules and its spatiotemporal dynamic propties for electronic noses are discussed. The integration of various phenomena and their mechanisms for biological olfaction into an electronic nose context for information processing will not only make them more bionic, but also perform better than conventional methods. However, many problems still remain, which should be solved by further cooperation between theorists and engineers.artificial olfaction, olfactory model, pattern recognition, electronic nose, bionics Various sensors derive different types of information from complicated environments, following which computers analyze before actuators perform particular tasks. To a certain degree, such devices have extended human sensory, brain and physical power to enhance the capacity of understanding and transforming nature. However, biological systems derived from evolution are useful models for engineering design, particularly with regard to bionics.Electronic noses (eNose) are a kind of novel bionic analytical instrument for mimicking the principle of biological olfaction. Generally, an electronic nose consists of an array of cross-sensitive gas sensors and an appropriate pattern recognition method, capable of automatically detecting and discriminating simple or complex odors [1] . Only twenty years after Persaud et al.'s pioneer work [2] , eNoses have been widely utilized within the food industry, for environmental monitoring, public security and medical diagnosis [3,4] , as they tend to be relatively fast, easy to use and objective with a modest overall cost.As a multidisciplinary research field, most studies of eNose primarily focus on gas sensor technology [5] and pattern recognition methods [6] . Pattern recognition models remain inadequately investigated such that they are the bottle-neck for commercialization. With more novel sensing techniques are applied to eNoses [3,7] , signal processing and pattern recognition methods have increased in significance. The information processing mechanism in olfaction from the genetic, cellular to systemic levels is more fully understand as a result of cross-

show abstract

Electronic nose and artificial neural network

Cited by 108 publications

References 23 publications

An electronic nose to classify Iberian pig fats with different fatty acid composition

An electronic nose to classify Iberian pig fats with different fatty acid composition

Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

Progress in bionic information processing techniques for an electronic nose based on olfactory models

Contact Info

Product

Resources

About