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Proceedings Paper

Quality indexing by machine vision during fermentation in black tea manufacturing
Author(s): S. Borah; M. Bhuyan
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Paper Abstract

Although the organoleptic method of tea testing has been traditionally used for quality monitoring, an alternative way by machine vision may be advantageous. Although, the three main quality descriptors estimate the overall quality of made-tea, viz., strength, briskness and brightness of tea liquor, the exact colour detection in fermenting process leads to a good quality-monitoring tool. The use of digital image processing technique for this purpose is reported to play an effective role towards the production of good quality tea though it is not the only quality determining parameter. In this paper, it has been tried to compare the contribution of the chemical constituents towards the final product with the visual appearance in the processing stage by imaging. The use of machine intelligence supports the process somewhat invariantly in comparison to the human decision and colorimetric approach. The captured images are processed for colour matching with a standard image database using HSI colour model. The application of colour dissimilarity and perceptron learning for the standard images and the test images is ensured. Moreover, the performance of the system is being tried to correlate with the decision made by the organoleptic panel assigned for the tea testing and chemical test results on the final product. However, it should be noted that the optimized result could be achieved only when the other quality parameters such as withering, flavour (aroma) detection, drying status etc. are properly maintained.

Paper Details

Date Published: 1 May 2003
PDF: 8 pages
Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515153
Show Author Affiliations
S. Borah, Tezpur Univ. (India)
M. Bhuyan, Tezpur Univ. (India)


Published in SPIE Proceedings Vol. 5132:
Sixth International Conference on Quality Control by Artificial Vision
Kenneth W. Tobin; Fabrice Meriaudeau, Editor(s)

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