Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

Saha, Pradip Kumar; Ghorai, Santanu; Tudu, Bipan; Bandyopadhyay, Rajib; Bhattacharyya, Nabarun

doi:10.1109/jsen.2016.2544979

Cited by 23 publications

(10 citation statements)

References 27 publications

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“…Traditional tea quality assessment methods are based on different analytical instruments, such as high performance liquid chromatography [1], gas chromatography [2] and plasma atomic emission spectrometry [3]. However, these methods require a lot of technical personnel, material and financial support, which lead to low efficiency and larger overhead [4]. With the development of sensor technology, the advantages of methods based on sensor technology are more distinguished.…”

Section: Introductionmentioning

confidence: 99%

“…Many scholars have contributed to tea pattern recognition. For instance, principal component analysis (PCA) [10], artificial neural network (ANN) [11], support vector machine (SVM) [9,12], k-nearest neighbor algorithm (k-NN) [13], random forest (RF) [14] and autoregressive (AR) model [4] have been put forward for classification analysis in the e-tongue system. Features extraction is another critical step of the e-tongue as the quality of features selection will directly affect the quality of pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

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A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are "hand-crafted", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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“…Em Saha et al (2016) uma LEé usada na caracterização de amostras de chá de diferentes qualidades, fazendo uso de SVM e MLP. O trabalho de Sharma and Ugale (2015) descreve uma maneira eficiente de identificar líquidos, como cerveja, chá, café, mel, leite e outros.…”

Section: Estado Da Arteunclassified

Análise de Dados de Língua Eletrônica baseada em redes SOM 1D

Sousa

Ferreira

Fukushima

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The design of devices that resemble biological recognition systems, inspired by the functioning of human organs, have made important advances in recent decades. As a result, interest in the concept of electronic languages has increased considerably in recent years. This study uses this type of device in order to identify different samples of flavor enhancers, such as Glutamate, Guanylate and Inosinate. The applied methodology involves self-organizing maps, an unusual tool in this area. The best results reached 95,4% accuracy. Resumo: O projeto de dispositivos que se assemelham a sistemas de reconhecimento biológico, inspirados no funcionamento deórgãos humanos, tiveram avanços importantes nasúltimas décadas. Em consequência disso, o interesse no conceito de línguas eletrônicas aumentou consideravelmente nosúltimos anos. Este estudo utiliza esse tipo de dispositivo com o intuito de identificar diferentes amostras de realçadores de sabor, como Glutamato, Guanilato e Inosinato. A metodologia aplicada envolve redes do tipo mapas auto-organizáveis, ferramenta pouco usual nessaárea. Os melhores resultados atingiram 95,4% de precisão.

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“…An electronic tongue sensor can produce different electrical signals in response to different adsorbed molecules, thereby simulating the biological taste system, enabling sample detection and analysis. An electronic tongue is mainly employed for liquid detection and has been applied to olive oil, (7,8) tea, (9)(10)(11) coffee, (12,13) and other products. Several studies have investigated the applications of an electronic tongue in beer detection.…”

Section: Introductionmentioning

confidence: 99%

Beer Taste Detection Based on Electronic Tongue

Zhang¹,

Zou²,

Liu³

et al. 2020

Sensors and Materials

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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.

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Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

Cited by 23 publications

References 27 publications

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Análise de Dados de Língua Eletrônica baseada em redes SOM 1D

Beer Taste Detection Based on Electronic Tongue

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