The traditional method of identifying the quality of star anise is subjective and complicated.To realize accurate and rapid identification of star anise, fumigated star anise with sulfur and its counterfeit, a novel detection method based on image feature analysis and pattern recognition was studied to identify the quality of star anise.The RGB images of star anise, fumigated star anise with sulfur, sikimi, and wild star anise were captured respectively, then the image was preprocessed by median filtering, image binarization, morphological open and close operation, and image segmentation afterwards, a total of 58 feature parameters in 3 categories of shape, color, and texture were extracted.Furthermore, the feature parameters were screened, and dimensionality was reduced by stepwise discriminant and principal component analyses, respectively.Finally, a linear parameter classifier and back propagation neural network model were established to identify star anise and its counterfeits and sulfur-fumigated star anise.Results showed that the accuracy of the stepwise discriminant analysis-linear parameter classifier was 87.50%-100% for the star anise, fumigated star anise with sulfur, sikimi, and wild star anise, and the average recognition rate was 96.75%.The accuracy of the principal component analysis-BP neural network model was 85.00%-100% for the star anise, fumigated star anise with sulfur, sikimi, and wild star anise, and the average recognition rate was 91.88%.The results indicated that image processing and analysis could effectively detect and identify star anise and its counterfeit products, and make an effective judgment for sulfur-fumigated star anise.
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