Grade discrimination of machine-fried Longjing tea based on hyperspectral technology

Because of the same contour and luster for different types of machine-fried Longjing Tea, the intrinsic quality will be the pivotal factor in classification. Therefore, a combined technique with hyper spectral (HS) and support vector machine (SVM) was proposed in this paper to identify the class of intrinsic quality of machine-fried Longjing Tea. By using hyper spectral technology, the spectral feature parameters can be obtained, such as absorption depth, absorption area, red edge, red valley location and normalized difference vegetation index, etc.,within 350-2500 nm wavelength range. The correlation between these spectral feature parameters and tea classes was calculated. Based on the support vector classification theory with a penalty coefficient C, the key kernel functions and classification functions were identified by taking these spectral feature parameters as inputs. The identification model was constructed for classing Longjing Tea’s classes. The model was also used in the classification experiment for different types of machine-fried Longjing Tea. The classification accuracy rate for machine-fried Longjing Tea’s of this method is 98.3%, which proved that this method is feasible in machine-made Longjing Tea’s classification.