Prediction of bitterness and alcoholic strength in beer using an electronic tongue

Arrieta, Andrés Adolfo Amell; Rodrı́guez-Méndez, M.L.; Saja, J.A. de; Blanco, Carlos A.; Nimubona, Dieudonné

doi:10.1016/j.foodchem.2010.05.006

Cited by 79 publications

(56 citation statements)

References 27 publications

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“…When the total amount of bittering agents is measured by HPLC, more accurate results can be obtained. Recently, using an electronic tongue based on a polypyrrole sensor array, we have determined bitterness intensity in a single measurement [41]. Theresults agree quite well with the total amount of iso-α-acids obtained by HPLC in 21 samples of beer.…”

Section: Introductionsupporting

confidence: 70%

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Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

Blanco¹,

Nimubona²,

Fernández‐Fernández³

et al. 2014

JFNR

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A total of 40 commercial lager beers (6 dark, 28 pale and 6 alcohol free) were analysed by HighPerformance Liquid Chromatography (HPLC) and evaluated by a descriptive sensorial panel. Discriminant Analysis was applied to 15 sensory descriptors to obtain a classification by types of beer. Cluster Analysis identified 4 clusters grouping the 15 sensory descriptors. The Correlation Matrix confirmed a correlation between them and the subsequent Principal Component Analysis. A stepwise discriminant analysis was used to eliminate the less significant descriptors, leading to 100% of the samples being correctly classified with a reduced number of variables (colour intensity, smell of caramel, smell of toasted, persistence and viscosity). The analysis of the correlations between iso-α-acid concentrations and sensory descriptors shows a good relationship with bitter taste. Thus, HPLC data can be used for predicting lager beer bitterness through the mathematical correlation developed in this paper.

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Section: Introductionsupporting

confidence: 70%

“…It is generally recognised that the main bittering principles of beer are the hop-derived iso-α-acids [38]. Beer bitterness could be estimated by several methods [41]. An approximate measurement is commonly obtained in breweries by spectrophotometry at 275 nm [40].…”

Section: Introductionmentioning

confidence: 99%

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

Blanco¹,

Nimubona²,

Fernández‐Fernández³

et al. 2014

JFNR

Self Cite

View full text Add to dashboard Cite

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“…(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. For example, MALDI-TOF MS was applied to detect the acid and hopshock-induced responses in beer.…”

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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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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“…Smell and taste systems have produced inspiration so that from its functioning principles instruments may be developed to search for a way to emulate its capacity to classify and differentiate complex substances [4]- [6]. These biologically inspired systems are known as electronic noses and tongues.…”

Section: Introductionmentioning

confidence: 99%

Electronic Tongue and Neural Networks, Biologically Inspired Systems Applied to Classifying Coffee Samples

Almario¹,

Cáceres²

2014

AJAC

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In this paper, the possibility to use an electronic tongue based on a polypyrrole sensor array in classifying coffee samples has been studied. Each sensor shows a distinguished electrochemical response when exposed to the studied solutions, providing signals with a high degree of cross-selectivity. The sensor array electrochemical response is related to the interaction of the ionic and non-ionic solution compounds and to the surface of the sensors polymeric matrix. Furthermore, the electronic tongue was used to perform an analysis on coffee samples. In this case, each sensor showed a particular response to each coffee sample. Data obtained from the registered signals were used to perform a discrimination of the samples. The analysis with neural networks of the principal components (NNPC) done on the electronic tongue response to five types of commercial coffee, allows to achieve a clear differentiation of samples.

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Prediction of bitterness and alcoholic strength in beer using an electronic tongue

Cited by 79 publications

References 27 publications

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

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

Electronic Tongue and Neural Networks, Biologically Inspired Systems Applied to Classifying Coffee Samples

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Prediction of bitterness and alcoholic strength in beer using an electronic tongue

Cited by 79 publications

References 27 publications

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-&#945;-Acid Concentrations

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-&#945;-Acid Concentrations

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

Electronic Tongue and Neural Networks, Biologically Inspired Systems Applied to Classifying Coffee Samples

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Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations