-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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Quality of cocoons determined by its silk content which is directly related with the market value. Silk is broadly used in textiles and it is costly too. Farmers are selling the cocoons in a bulk with an average market value to the yarn producers. Presently, the quality of cocoons is verified by visualizing the color, size, shape and feeling its solidity on pressing by fingers of our hand. These manual inspections sometimes create dissatisfaction among the buyers and sellers. Sometimes, the sellers (farmers) are duped by the clever buyers. In other method, the silk content is estimated by taking the average raw cocoon shell weight after cutting the cocoon and removing the pupa from it. This approach is destructive, time consuming, expensive and laborious also. In this paper, X-ray imaging technique has been explored to estimate the silk content and determine the quality of cocoons. Firstly the images of the cocoons are captured using standard X-Ray imaging setup. Then the images are enhanced using digital image processing techniques. Finally, different dimensional features are extracted using image analysis techniques. A new method for estimation of the silk content has been proposed using the GRNN (General Regression Neural Network). Quality of the cocoons has been evaluated using unsupervised artificial neural network technique known as SOM (Self Organizing Map) which produces the different classes of quality grades of cocoons. In this experiment, we have considered five classes -good, medium, bad, dead pupa and un-identified quality. Total 49 no of cocoons have been used for the experimentation. The result shows that using GRNN the estimation of silk content is quite helpful with a fair level of accuracy. Using SOM technique, quality of cocoons has been determined and the result is validated with the manual inspection method. Both this approach of estimating the silk content and determining the quality of cocoons opens new possibilities in the field of automatic, non-destructive technique for price appraisal of cocoons.

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