Detection of Optimum Fermentation Time of Black CTC Tea Using a Voltammetric Electronic Tongue

Ghosh, Arunangshu; Sharma, Prolay; Tudu, Bipan; Sabhapondit, Santanu; Baruah, Binoti Devi; Tamuly, Pradip; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib

doi:10.1109/tim.2015.2415113

Cited by 31 publications

(9 citation statements)

References 28 publications

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“…Table III is about Electrode ratings per tea sample for fractional impedance, and the root means square error. Voltammetric e-tongue is used to obtain the optimum fermentation time of curl black tea [80]. The principal component analysis (PCA) correlates the e-tongue response with the biochemical changes during tea's fermentation process.…”

Section: System Hardware and Functioningmentioning

confidence: 99%

“…All other attributes tied by applying sophisticated data processing methods such as neural networks may be used to detain the temporal patterns of difference among the attributes. It allows precise and vigorous exposure of optimum fermentation duration for many samples [80].In another paper [81], the taste detection analysis had done for the bitterness and astringency of green tea. The three types of green tea samples have analyzed by using e-nose and e-tongue.…”

Section: System Hardware and Functioningmentioning

confidence: 99%

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Artificial Perception of the Beverages: An In-Depth Review of the Tea Sample

2021

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India is the second-largest tea producer and consumer in the world after China. In 2017, the Indian tea market size accounted for 130 billion Indian rupees. An estimated global tea market size was at USD 13.31 billion in 2019, and the expected compound annual growth rate is 5.5% up to the year 2025. India can grab worth tea market size globally by making market strategies with AI and ML-based demonstrations for the unique identity of tea flavor. Conventional instruments available are not handy, time-consuming and require a skilled person to operate. The tea attributes should be digitally recognizable before purchase from the consumer's perspective, significantly enlarging the tea market circle. In the paper, the comprehensive review about an artificial perception of tea has been briefly discussed. Three major attributes of the tea sample, its taste, smell, and color, are under consideration. With the help of various sensors, the attributes of liquefied tea samples had got converted into their digital signature. By analyzing the correlation of them with the pattern recognition, their classification had done. The electronic feature fusion of tea liquor attributes may cause handling issues with the formation of redundant data. So this paper explains the method and guidelines of an application of specific filters which remove the redundant data. The constructive sample data can establish the decision matrix for correlation. With the established decision matrix, précised test prediction can be achieved for the tea sample based on correlation and regression. The limitations and glitches of the conventional instruments for an artificial perception have been discussed in-depth for possible improvement. The paper ends with a bibliometric analysis of the topic "artificial taste perception of tea," which had derived from the standard repository of Web of Science. The bibliometric analysis is very useful to showcase the current research trends in the artificial taste perception of tea.

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Section: System Hardware and Functioningmentioning

confidence: 99%

Section: System Hardware and Functioningmentioning

confidence: 99%

Artificial Perception of the Beverages: An In-Depth Review of the Tea Sample

2021

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“…The features do not contain frequency information of the response data. Ghosh et al, 2015b Area values under response curve…”

Section: Referencementioning

confidence: 99%

Stationary wavelet singular entropy based electronic tongue for classification of milk

Kaushal

Mudhalwadkar

2020

Transactions of the Institute of Measurement and Control

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Electronic tongue mimics human gustatory sensation and is used to characterize and discriminate beverages and foods. Feature extraction plays a key role in improving the classification accuracy by preserving the distinct characteristics while reducing high dimensionality of data generated from electronic tongue. This paper presents a new feature extraction method based on stationary wavelet singular entropy for a developed electronic tongue system to classify pasteurized cow milk. The electronic tongue consists of an array of five working electrodes along with a reference and a counter electrode to characterize milk sample. The feature extraction of acquired data is done by computing stationary wavelet transform to obtain detail and approximate coefficients at different level of decomposition. These coefficients are processed using singular value decomposition followed by calculation of entropy to obtain stationary wavelet singular entropy values. These values form the feature set and feed to two classifiers, k-nearest neighbor and back propagation artificial neural network, and their classification accuracy is evaluated with variation in their model parameters. The proposed method is compared with other wavelet transform-entropy methods in terms of classification accuracy, which indicates that the proposed method is more effective in discriminating milk samples.

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“…As already pointed out, voltammetric electronic tongues can provide valuable qualitative information about food samples when combined with a chemometric method of pattern recognition. Table 1 summarizes some relevant applications found in references [14,15,16,17] and [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71] focused on the characterization, classification and authentication of food products.…”

Section: Characterization Classification and Authentication Of Fomentioning

confidence: 99%