Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (8): 16-21.doi: 10.16180/j.cnki.issn1007-7820.2019.08.004

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Near-infrared Scanning Polysaccharide Content Classification of Auricularia Auricular Based on SVM

SUN Liping,ZHANG Ximeng,HE Rui,LI Jiaqi   

  1. School of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China
  • Received:2018-08-15 Online:2019-08-15 Published:2019-08-12
  • Supported by:
    The State Bureau of Forestry 948 Project(2015-4-52)


As a kind of colloidal edible fungus, auricularia auricular was rich in a variety of nutrients, among which polysaccharide was one of the most important functional ingredients. According to the classification of polysaccharides from Auricularia auricula, it was different from the traditional chemical detection method, and the detection speed was slow. In this paper, infrared spectrometry was used to carry out nondestructive detection of polysaccharides in northeast auricularia auricular samples, and the algorithm is modeled by SVM simulation. The accuracy of the recognition result of the auricularia auricular polysaccharide classification model was 85.7% and the recall rate was 87.3% , F1-score was 0.864, which achieved the expected goal. The experiment proved that compared with the conventional black fungus polysaccharide detection method, the high-precision auricularia auricular near-infrared spectrum polysaccharide classification model established by the support vector machine algorithm is feasible.

Key words: computer simulation, support vector machine, near infrared spectroscopy, auricularia auricular, polysaccharide content, classification model

CLC Number: 

  • TP391.9