[Study of discrimination of corn seed based on near-infrared spectra and artificial neural network model].
A new non-destructive and rapid method was developed to discriminate varieties of corn seeds. The method is based on near-infrared reflectance spectroscopy (NIRS) and artificial neural network (ANN). The corn seeds used for this study involved four varieties: Gaoyou115, Nongda368, Nongda108 and Nongda4967. After collecting the near-infrared reflectance spectrum of each single seed in the range between 1000 and 2632 nm, the principal component analysis (PCA) was used to compress the NIR spectra, which had been preprocessed with Savitky-Golay and multiplicative scatter correction (MSC). The analysis results showed that the cumulate reliabilities of PC1 to PC8 (the first eight principal components) were 99.602%. A three-layer back-propagation neural network (BPNN) was developed for classification, which was trained by the Levenberg-Marquard algorithm to improve the network training speed and efficiency. The LMBP was activated by the sigmoid function, and normalization of targets was used to get the best discrimination result of network. The first eight principal components of the samples were applied as LMBPNN inputs, and the values of the type of corn seeds were applied as the outputs. In this model, 120 kernels were used as the training data set and 40 kernels were used as the test data set. Calculation results showed that the distinguishing rate of the four corn seed varieties was 95%. This model is reliable and practicable. The results demonstrated that this identification method was rapid and non-destructive, and could be used for classification.
Chen
Jian
J
College of Engineering, China Agricultural University, Beijing 100083, China. gxy5@cau.edu.cn
Chen
Xiao
X
Li
Wei
W
Wang
Jia-hua
JH
Han
Dong-hai
DH
D000465:Algorithms; D008956:Models, Chemical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods; D012639:Seeds / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods; D003313:Zea mays / Q000737:chemistry*