[Study on discrimination of tea based on color of multispectral image]

Tea is one of the most popular beverages worldwide. Its categories have a great relationship to its beneficial medicinal properties. The present work attempted to study the feasibility to use multispectral imaging technique as a rapid and non-destructive method to discriminate tea varieties. Two categories of tea discriminated hardly by naked eye were sorted. The images were 1 036 pixels vertically by 1 384 pixels horizontally with 24-bit depth, and were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multispectral digital imager, MS3100 (Duncan Technologies, Inc., CA, USA). The three wavebands of image (Red, Green, NIR) can be composed into one image which contains more information than images recorded by ordinary digital cameras, especially, the NIR image is more sensitive to the color of organic matter than visible spectrum. The three images of one sample can be obtained simultaneously. The color features of tea were calculated using the standard notations: mean and mean square deviation. Then, the two color features of 3CCD and ordinary digital cameras were extracted and calculated by Matlab 7. 3 software respectively, and were contrasted. A total of 60 samples were adopted, and the features of mean and mean square deviation of NIR waveband image were applied as inputs to a back propagation neural network (BP-ANN) with one hidden layer. The forty samples (twenty for each category) were selected randomly to build BP-ANN model, and this model was used to predict the varieties of 20 unknown samples (ten for each category). The two categories of tea can be discriminated by the information of color of images of 3CCD, but can not by the ordinary digital cameras. The result indicted that the discrimination rate of classification set of BP-ANN model was up to 100% within 0.3 of threshold. It concluded that multi-spectral imaging technique has a high potential to identify categories of green tea fast and non-destructively.