Beer Taste Detection Based on Electronic Tongue

Abstract

We attempted to detect the five tastes in four different commercially available brands of beer using an electronic tongue and conducted a statistical analysis on their alcohol contents, original wort concentrations, and pH values. Statistical methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and a backpropagation (BP) neural network, were used to identify and classify the four beer brands. According to PCA, in the five taste indicators of the four brands of beer, the contribution rates of the first and second principal components were 56.73 and 34.46% respectively; the beer was oxidized to a certain extent with increasing detection time. The results of LDA confirmed the high sensitivity of the electronic tongue sensors to beer tastes as the four brands were effectively identified by distinguishing the taste differences among them. The results of the BP neural network suggested that its predictive accuracy for the five tastes in the four brands can achieve 100% subject to the conformity between the measured and predicted values. The stepwise regression model established in our study could be effective for accurately predicting the original wort concentration of beer. The determination coefficients of the original wort concentration modeling set and the validation set were 0.99 and 0.96, and the root-mean-square errors were 0.06 and 0.41, respectively. As demonstrated by its high sensitivity in analyzing the tastes of four different beer brands, the electronic tongue can effectively distinguish the taste differences among different beers.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

“…115 Furthermore, a prediction accuracy of 98.81% was reached through an ANN model predicting safranal concentration in saffron. 117 Additionally, BPNN achieved a 100% classication accuracy of ve beer tastes 131 where SVM showed a superior performance as well when compared to LDA in classifying Mexican coffees according to their growth conditions and geographical origin. 119 In a word, this is to conclude that non-linear processing data methods gave promising results when coupled to e-tongues and should be further adopted in data processing strategies.…”

“…Above and beyond, most of the achieved studies were not limited only to the use of PCA alone but on its usage besides supervised methods for regression and discrimination purposes. Linear discriminant models were achieved in many studies relying mostly on LDA for assessing differences between sensory descriptors, 131 discrimination between geographical origins, 29 and spotting adulteration occurrences. 104 LDA also was found to be dominating in establishing discriminant models with e-nose data.…”

Visualization of Changes in Taste of Food during Chewing Process

“…(6)(7)(8)(9)16,17) As in human taste sensation, the voltage output of the taste sensor is proportional to the logarithm of the concentration of chemical substances including amino acids. (3,7,9,17) Namely, it follows the Weber-Fechner law. (18,19) Therefore, the sensor output can be converted by a linear transformation into the strength of the taste perceived by humans.…”

“…Its use has become widespread in the last 20 years, and it is now used by food and pharmaceutical companies. (1)(2)(3)(4)(5) A taste sensor can quantify astringency in addition to the five primary taste qualities, namely, sweetness, bitterness, saltiness, sourness, and umami. It can also measure aftertastes typified by koku (a Japanese word meaning "rich taste"), which is the aftertaste of umami.…”

Electrical Properties of Two Types of Membrane Component Used in Taste Sensors

“…Different from the ion-selective sensors, the taste sensor can distinguish and quantify five basic tastes, such as sourness, sweetness, saltiness, bitterness, and umami. Each basic taste is measured by one kind of taste sensor where the membrane is specifically constructed to measure the taste intensity based on a large quantity of possible substances producing tastes [10][11][12]15,16].…”