Abstract
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 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 been 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.
Highlights
Due to long and fast working hours, people use ready-made food and drink products of various brands
E-nose had established with five MOS sensors from Figaro, and E-tongue had used with voltammetric electrodes [2].Medical health benefits of green tea are well known, so classification based on bitterness [29]–[35] and astringency had to be carried out with E-tongue, E-nose and classifier algorithms same described as previous, only with the difference is in classification algorithms
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
Summary
Due to long and fast working hours, people use ready-made food and drink products of various brands. With the digitization and pattern recognition of main attributes of water, milk and tea or any other beverage, the artificial monitoring and grading can be unified and personalize as per the health history and requirement of human being, which turns into a reduction of their malpractices, economically triggered adulterations and dependency on manual test methods. The system had established to detect impurities in water based on E-tongue 36-37] and E-nose [38], [39] with additive wavelet transform and homomorphism image processing This electronic sensor system could extract the required information from a water sample. E-nose had established with five MOS sensors from Figaro, and E-tongue had used with voltammetric electrodes [2].Medical health benefits of green tea are well known, so classification based on bitterness [29]–[35] and astringency had to be carried out with E-tongue, E-nose and classifier algorithms same described as previous, only with the difference is in classification algorithms. The fermentation process of black tea had automated with the e-nose and LabVIEW simulation software [12]
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