Electronic nose for wine discrimination

García, María L.; Aleixandre, M.; Gutiérrez, J.; Horrillo, M.C.

doi:10.1016/j.snb.2005.03.078

Cited by 146 publications

(63 citation statements)

References 15 publications

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“…Nowadays, E-nose has already been used in various field, such as: pork (O'Sullivan et al, 2003), mandarin (Gomez et al, 2007), peach (Benedetti et al, 2008;Di Natale et al, 2001), apple (Li and Heinemann, 2007), pear (Oshita et al, 2000), wine (Garcia et al, 2006) and grain (Balasubramanian et al, 2007;Evans et al, 2000). Moreover, E-nose and GC-MS have also been used for detection of spoilage of grain and mycotoxins, ergosterol, and odour volatiles in durum wheat (Abramson et al, 2005;Presicce et al, 2006).…”

Section: Bo Zhoumentioning

confidence: 99%

Identification of different wheat seeds by electronic nose

Zhang¹,

Wang²,

2012

International Agrophysics

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A b s t r a c t. The potential of electronic nose to distinguish of wheat seeds was studied. The reproducibility and practicability of electronic nose data was evaluated by repeating the analysis of samples with a time difference of two months. The principle components analysis and linear discriminant analysis were applied to the generated patterns to distinguish the varieties of wheat seeds. The results showed that they could distinguish the wheat varieties properly. The stepwise discriminant analysis and a three-layer backpropagation neural network were developed for pattern prediction models. The results showed that both models could identify the wheat varieties, the back-propagation neural network presented the higher percent of correct classifications in comparison to stepwise discriminant analysis. Moreover, gas chromatography mass spectrometry analysis of the headspaces of same samples confirmed that electronic nose as a powerful tool is able to identify the wheat seeds.K e y w o r d s: electronic nose, wheat seeds, identification, gas chromatography mass spectrometry INTRODUCTIONThe usual methods for identification of wheat seeds are seed protein electrophoresis, DNA molecular markers techniques, morphological identification and field evaluation (Li et al., 2006). In most cases, these methods are expensive and time-consuming, have low reproducibility, both in their commercial as well as in their technological implications. Several attempts have been made recently to classify wheat varieties using nondestructive methods such as machine vision (Douik and Abdellaoui, 2008;, near infrared spectrometer (Li et al., 2008) and thermal imaging (Manickavasagan et al., 2010). Most of them used kernel morphological features of a single grain for variety identification. It would be highly desirable to have an alternative method for classification of wheat varieties, which may use some characteristics of the kernels other than morphological features. A simple variety identification system with less complexity for bulk sample testing (not single grain analysis) would be most desirable in the grain-handling facilities.Electronic nose (E-nose) is instrument which mimics the sense of smell. These devices are typically array of sensors used to detect and distinguish odours precisely in complex samples and at low cost (Peris and EscuderGilabert, 2009). In contrast to the well known analytical gas chromatography mass spectrometry (GC-MS) and sensory techniques that have been used for the analysis of flavour compounds, the E-nose does not give any information about the compounds causing the investigated aroma; neither about the identity of the compounds nor their sensorial properties. Using E-nose the aroma is judged by the so-called 'aroma pattern', which should be characteristic to the investigated substrate (O'Sullivan et al., 2003). With the use of appropriate mathematical methods, E-nose should be capable of recognizing the aroma pattern or of distinguishing it from aroma patterns of other samples (Peris and EscuderGilab...

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Section: Bo Zhoumentioning

confidence: 99%

Identification of different wheat seeds by electronic nose

Zhang¹,

Wang²,

2012

International Agrophysics

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“…To this end, wines [12][13][14][15][16][17][18][19][20][21], tequila, whiskey, vodkas [22,23] and Chinese spirit [24] were analyzed. Until now, the application of electronic nose was based on sensors, e.g., conductometric, amperometric and piezoelectric sensors [25].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the suitability of electronic nose based on fast GC for distinguishing between the plum spirits of different geographical origins

Śliwińska

Wiśniewska

Dymerski

et al. 2016

Eur Food Res Technol

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“…García et al (2004) have discriminated among several types of wine using an electronic nose and Di Natale et al (2004) analysed wine quality using gas and liquid sensors arrays.…”

Section: Introductionmentioning

confidence: 99%

Optimization of an aroma sensor for assessing grape quality for wine making

Gutierrez

Burgos

Garcerá

et al. 2007

Span. j. agric. res.

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Control of the raw material has considerable influence on final wine quality. The objective of this work was to develop a sensor capable of assessing grape juice quality in the cellar. Sensor response was compared with conventional chemical analysis using multivariate data techniques. The aroma sensor array is composed of 14 metal oxide sensors (MOS) gas transducers plus a temperature and a relative humidity sensor. This work showed that the sensor could detect rotten flavours in grape juice.

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Electronic nose for wine discrimination

Cited by 146 publications

References 15 publications

Identification of different wheat seeds by electronic nose

Identification of different wheat seeds by electronic nose

Evaluation of the suitability of electronic nose based on fast GC for distinguishing between the plum spirits of different geographical origins

Optimization of an aroma sensor for assessing grape quality for wine making

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