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05 August 2010

Measuring Quality of Black Tea From Theaflavins Analysis Using Secondary Measurement

International Conference on Instrumentation, Communication and Information Technology (ICICI) 2005 Proc., August 3 -5 , 2005, Bandung, Indonesia

Measuring Quality of Black Tea From Theaflavins Analysis Using Secondary Measurement Melania S. Muntini1), Yul Y. Nazaruddin2), The Houw Liong 3), Lienda Handojo4)
1) Department of Physics, Institut Teknologi Sepuluh Nopember (ITS) Surabaya, Indonesia
2) Department of Engineering Physics, Institut Teknologi Bandung, Indonesia
3) Department of Physics, Institut Teknologi Bandung, Indonesia
4) Department of Chemical Engineering, Institut Teknologi Bandung Jl. Ganesa 10 Bandung 40132, Indonesia
Phone/Fax: +62-22-2508138

Abstract – Theaflavins (Tf) is a key compound that significantly contributes in the quality of black tea. It undergoes a series of chemical changes during the fermentation process. Fermentation is one of the most critical processes in black tea processing. There are many parameters that significantly influenced the process including room temperature, thickness of greendhool, and duration of the process. In general, it is difficult to measure theaflavins directly as it involves some chemical analysis and enzymes for pigment. An alternative approach, theaflavins is measured indirectly and inferred from easily made process measurements or secondary measurements. This inferential method of measurements employs a scheme which is called a virtual sensor, which is realized by integrating artificial neural networks with the Extended Kalman Filter algorithm. Secondary variables are several parameters of fermentation process and results of color analysis of tea liquid, whereas primary variable is Theaflavins. The data for implementing this proposed technique were obtained by conducting several real-time experiments at black tea factory in Indonesian Tea and Cinchona Research Institute (PPTK Gambung), West Java. Results show how the quality of black tea can be infered indirectly using the proposed technique.The mean and variance of error between the obtained output of virtual sensor algorithm and the output chemical analysis of theflavins were 1,81 x 10^-4 and 5,07 x 10^-6 respectively .
Keywords – artificial neural network, black tea, Extended Kalman Filter, indirect measurements, Theaflavins, virtual sensor

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