Beer discrimination using a portable electronic tongue based on screen-printed electrodes

Blanco, Carlos A.; Fuente, Rocío De la; Caballero, Isabel; Rodrı́guez-Méndez, M.L.

doi:10.1016/j.jfoodeng.2015.02.018

Cited by 57 publications

(29 citation statements)

References 33 publications

(29 reference statements)

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“…Considering the retention of the sample gas in the chamber, the chamber was composed of cardboard covered by polytetrafluoroethylene (PTFE). PTFE is a thermoplastic polymer, its melting point is 327 °C, its density is 2.2 g/cm 3 and its friction coefficient is 0.05-0.10. (23) The resistance of PTFE to Van der Waals forces means that the adsorption of gas to PTFE is low.…”

Section: Electronic Nosementioning

confidence: 99%

“…These components affect the quality of beer in a coordinated and comprehensive way. (1)(2)(3) At present, testing and evaluation methods for beer quality include the traditional physical and chemical index detection methods, the sensory evaluation method, electronic nose detection, and electronic tongue detection. (4)(5)(6)(7) The traditional physical and chemical index detection methods are widely used in production.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Evaluation of Sensory Information for Beer at a Fuzzy Level Using Electronic Tongue and Electronic Nose

2016

Sensors and Materials

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Keywords: electronic tongue, electronic nose, sensory evaluation of beer, cloud model, fuzzy neural networkThe flavor of beer is an important means of evaluating its quality. Beer flavor is the integrated embodiment of beer smell and taste information. In this work, the automatic evaluation of beer aroma, taste, and overall flavor sensory information was realized by a smell and taste sensor coupling array. First, a cloud model was used to realize the conversion between the descriptive language and the corresponding quantitative numbers in the process of beer sensory evaluation. Next, an electronic nose and an electronic tongue were used to test the quality of beer in terms of smell and taste. Finally, a fuzzy neural network was trained with the characteristic information collected by the sensor coupling array as the input, with a characteristic value generated from the conversion of the cloud model of sensory evaluation as the output. The results from this system are excellent, as the error rate in the overall fuzzy information evaluation of flavor was between 0.0048 and 0.0394.

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Section: Electronic Nosementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Sensory Information for Beer at a Fuzzy Level Using Electronic Tongue and Electronic Nose

2016

Sensors and Materials

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“…(González-Calabuig et al, 2016;Huynh y Kutner 2015;Blanco et al, 2015;MariLuz 2016). Los primeros prototipos de lengua electrónica voltamétricas se basaron en la utilización de sensores metálicos de oro, iridio, platino, plata, cobre, níquel, cobalto, entre otros.…”

Section: Introductionunclassified

Capacidad de Discriminación de Dos Redes de Sensores como Unidades Sensibles de una Mini-Lengua Electrónica sobre Muestras de Leche

2017

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ResumenEn este artículo se presentan los resultados de discriminación de dos redes de sensores. Una de estas redes de sensores se conformó por electrodos comerciales serigrafiados con materiales electroactivos y la otra red de sensores se elaboró a partir de electrodos modificados con polipirrol y diferentes dopantes. Se analizó la capacidad de discriminación y clasificación de diferentes tipos de muestras de leche comercial. Los resultados muestran que el uso de sensores modificados con polipirrol permite obtener respuestas más completas en información, clasificando correctamente las muestras de leche con propiedades fisicoquímicas y organolépticas diferentes mediante el uso de dos componentes principales que reprodujo el 67,6% de la información. Por otra parte, la red de sensores comerciales se evaluó sobre el mismo grupo de muestras e iguales condiciones, consiguiéndose el 56,5% de la variabilidad de información, mostrando así menos capacidad de discriminación de las muestras analizadas.Palabras clave: polipirrol; electrodos serigrafiados; sensores electroquímicos; leche; lengua electrónica. Ability of Discrimination of Two Sensor Networks as Sensitive Units of a Mini-Electronic Tongue on Milk Samples AbstractThis paper presents the development of two sensor networks as initial stage for the design of a prototype electronic tongue. One of these sensor networks was joined by commercial screen printed electrodes and the other sensor network was assembled from modified electrodes with pyrrole and different dopants. The capacity of discrimination and classification of different types of samples of commercial milk to two sensor networks was analyzed. The results show that the use of modified sensors with conductive polymers allows obtaining more complete information, classifying correctly samples of milk with different organoleptic and physico-chemical properties by the use of two main components that reproduced the 67,6% of information. On the contrary, the commercial sensor network evaluated on the same group of samples and equal conditions, obtaining 56,5% of the variability of information, showing in this way lower classifying capacity of the samples analyzed.

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“…Potentiometric and voltammetric electronic tongues have been used to analyze a variety of foods and beverages [9,10,12,13], including drinks with low alcoholic content such as wines [14,15,16,17] or beers [18]. Regarding beverages prepared from apples, recently an e-tongue based on glassy carbon electrodes has been successfully used to compare taste profiles of ciders produced with different yeasts or to discriminate Polish commercial ciders [19].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

Śliwińska

García-Hernández

Kościński

et al. 2016

Sensors

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The capability of a phthalocyanine-based voltammetric electronic tongue to analyze strong alcoholic beverages has been evaluated and compared with the performance of spectroscopic techniques coupled to chemometrics. Nalewka Polish liqueurs prepared from five apple varieties have been used as a model of strong liqueurs. Principal Component Analysis has demonstrated that the best discrimination between liqueurs prepared from different apple varieties is achieved using the e-tongue and UV-Vis spectroscopy. Raman spectra coupled to chemometrics have not been efficient in discriminating liqueurs. The calculated Euclidean distances and the k-Nearest Neighbors algorithm (kNN) confirmed these results. The main advantage of the e-tongue is that, using PLS-1, good correlations have been found simultaneously with the phenolic content measured by the Folin–Ciocalteu method (R2 of 0.97 in calibration and R2 of 0.93 in validation) and also with the density, a marker of the alcoholic content method (R2 of 0.93 in calibration and R2 of 0.88 in validation). UV-Vis coupled with chemometrics has shown good correlations only with the phenolic content (R2 of 0.99 in calibration and R2 of 0.99 in validation) but correlations with the alcoholic content were low. Raman coupled with chemometrics has shown good correlations only with density (R2 of 0.96 in calibration and R2 of 0.85 in validation). In summary, from the three holistic methods evaluated to analyze strong alcoholic liqueurs, the voltammetric electronic tongue using phthalocyanines as sensing elements is superior to Raman or UV-Vis techniques because it shows an excellent discrimination capability and remarkable correlations with both antioxidant capacity and alcoholic content—the most important parameters to be measured in this type of liqueurs.

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Beer discrimination using a portable electronic tongue based on screen-printed electrodes

Cited by 57 publications

References 33 publications

Automatic Evaluation of Sensory Information for Beer at a Fuzzy Level Using Electronic Tongue and Electronic Nose

Automatic Evaluation of Sensory Information for Beer at a Fuzzy Level Using Electronic Tongue and Electronic Nose

Capacidad de Discriminación de Dos Redes de Sensores como Unidades Sensibles de una Mini-Lengua Electrónica sobre Muestras de Leche

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

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