ARMAX Modeling and Impedance Analysis of Voltammetric E-Tongue for Evaluation of Infused Tea

Bhuyan, Abhishruti; Tudu, Bipan; Bandyopadhyay, Rajib; Ghosh, Arunangshu; Kumar, Sanjeev

doi:10.1109/jsen.2019.2898226

Cited by 13 publications

(1 citation statement)

References 27 publications

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“…Randles equivalent circuit is enhanced further for a technique of system recognition using e-tongue signals [78]. In other experiment [79] for impedance analysis of taste sensors, the auto-regressive moving average (ARMA) model has utilized. The voltammetric e-tongue development is so far based on ideal circuit consideration.…”

Section: Figure10graphic User Interface (Gui) For E-tonguementioning

confidence: 99%

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: Figure10graphic User Interface (Gui) For E-tonguementioning

confidence: 99%