Tasting of beverages using an electronic tongue

Legin, Andrey; Rudnitskaya, Alisa; Vlasov, Yuri; Natale, Corrado Di; Davide, Fabrizio; D’Amico, A.

doi:10.1016/s0925-4005(97)00167-6

Cited by 197 publications

(111 citation statements)

References 8 publications

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“…k is the output nodes (k: 1-7). j is the hidden nodes (j: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]: Because the output function is a Tansig, the output has a value in the range [-1,1] so they must be made to fit among [0,1] using Eq. (2).…”

Section: Implementation Of the Multi-layer Feed-forward Neural Networkmentioning

confidence: 99%

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A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

García‐Breijo

Atkinson

Gil-Sánchez

et al. 2011

Sensors and Actuators A: Physical

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a b s t r a c tA portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 microcontroller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.

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Section: Implementation Of the Multi-layer Feed-forward Neural Networkmentioning

confidence: 99%

“…Various electrochemical techniques have been used in electronic tongues, such as potentiometry [2], voltammetry [3] or impedance spectroscopy [4]. These have been used in several applications, including waste water control [5] and food analysis [6].…”

Section: Introductionmentioning

confidence: 99%

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

García‐Breijo

Atkinson

Gil-Sánchez

et al. 2011

Sensors and Actuators A: Physical

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“…So it is an urgent problem to find a rapid, simple, credible way to determine the sweetness differences among samples [8]. To overcome the above problem Electronic tongue (E-tongue) used, it is a mimic of human taste device and have been fruitfully applied to discriminating different beers, milk [9,10], fruit juices [9][10][11][12][13]. Keeping significance of these sweeteners in mind, this present work was done to check the texture and overall acceptability of products with blend of sweeteners in Gulab jamun.…”

Section: Introductionmentioning

confidence: 99%

"Optimization study of sweeteners for preparation of syrup for sugar free Traditional sweet: Gulab jamun"

2017

IJCRR

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Aim: Study was conducted with artificial sweetener based syrups to be used in the preparation of Gulab jamun with an objective to get low calorie foods. Methods:The sweeteners used in the study were Stevia, Maltitol and Sucralose. For sugar free product the syrup concentration was optimized using response surface methodology. A rotatable central composite design (CCRD) was applied on three controlled variables viz. Maltitol, Stevia and Sucralose. Sweetness, lingering sweetness, color, softness, and textural properties were evaluated by trained sensory panelists using combined methods Quantitative Descriptive Analysis. Texture and taste profile was done by Texture Profile Analyzer and Electronic Tongue respectively. Results:Threshold values for Stevia, Maltitol and Sucralose were 0.0027, 1.78, and 0.005% respectively. The hardness of fresh Gulab jamun samples ranged from 1.8-3.86N. E-tounge graph shows the variance of 2DFA (DF1 and DF2) in which DF1 ranges from 89.61 -96.54% and DF2 ranged between 3.05-8.83%. Conclusion:All three responses were significantly (p <0.05) affected by the combinations of independent variable. The numerical optimization suggested a proportion (% w/w) of maltitol: stevia: sucralose in 5.92:0.0325:0.0054 with high overall acceptability.

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“…Along with discrete sensors, quite useful for certain applications (Meyerhoff et al 1986 ix ), there is another approach widely called now "electronic tongue" (ET). ET is an analytical instrument comprising an array of cross-sensitive sensors, an appropriate data acquisition system for sensors response recording and multivariate data processing engine (Legin et al 1997 The most interesting applications of multisensor systems are related to the evaluation of parameters having complex integral nature that are not directly correlated with the content of certain chemical substances in the sample. In the present work we suggest using an artificial sensory system for estimation of the tea retail price.…”

Section: Introductionmentioning

confidence: 99%

A Tool for General Quality Assessment of Black Tea—Retail Price Prediction by an Electronic Tongue

et al. 2014

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Retail price of food products is a complex interplay between multiple factors. Overall product quality has got one of the most serious impacts on the price in many situations. In the present study an artificial sensory system (potentiometric electronic tongue) was employed for the analysis of black tea samples purchased in the retail stores in Spain and Russia. It was possible to relate the response of a potentiometric sensor system formed by 22 ion-selective electrodes with retail prices of various black tea samples by means of partial least squares regression. PLS regression models allowed for prediction of retail price with mean relative errors of about 15% and 25% for Spain's tea bags and for loose packed tea from Russia, respectively. The suggested approach shows a good promise for the development of an instrumental analytical technique for regulatory authorities to fight with counterfeits, and for commercial purposes to evaluate market space.

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Tasting of beverages using an electronic tongue

Cited by 197 publications

References 8 publications

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

"Optimization study of sweeteners for preparation of syrup for sugar free Traditional sweet: Gulab jamun"

A Tool for General Quality Assessment of Black Tea—Retail Price Prediction by an Electronic Tongue

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