Compact computer vision for black tea quality evaluation based on the black tea particles

Suprijanto,; Rakhmawati, Amalia; Yuliastuti, E.

doi:10.1109/ica.2011.6130135

Cited by 2 publications

(2 citation statements)

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“…[24]. Mohit Sharma et al [25] makes an attempt to study the colour change during fermentation using RGB (red, green, blue) colour model.…”

Section: Introductionmentioning

confidence: 99%

“…Quality analysis of tea grades is proposed by taking image of tea particle using a microscope with 5X magnification and an artificial neural network is employed to differentiate among various grades of tea. [24]. Mohit Sharma et al [25] makes an attempt to study the colour change during fermentation using RGB (red, green, blue) colour model.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Vision System For Estimation Of Theaflavins And Thearubigins In Orthodox Black Tea

Akuli

Pal

Bej

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

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-Orthodox black tea quality depends upon the amount of certain organic compounds present and out of these, theaflavins (TF) and thearubigins (TR) are the most important ones While TF is responsible for attractive golden colour, increased brightness and astringency in tea liquor, TR is reddish brown, reduces the brightness of tea liquor and contribute mostly for the ashy taste of the liquor with minor improvement in astringency. The rapid estimation of TF and TR thus may resolve the problem of certain uncertainty or ambiguity that may arise during quality assessment of tea by the tea tasters. In this paper, a new method for rapid measurement of concentration of TF and TR is described using a machine vision system taking images of tea liquor and employing artificial neural networks (ANN). The results show good correlation of estimated values of TF and TR with the actual concentrations obtained using ultraviolet-visible spectrophotometer (UV-VIS).

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“…[24]. Mohit Sharma et al [25] makes an attempt to study the colour change during fermentation using RGB (red, green, blue) colour model.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%