DISCRIMINATION OF VARIETIES OF SILKWORM EGG BASED ON VISIBLE-NEAR INFRARED SPECTRA
基于可见-近红外光谱技术的家蚕蚕种鉴别方法的研究

A new method which was based on principal component analysis(PCA) and artificial neural network(ANN) was developed to discriminate the varieties of silkworm eggs nondestructively by visible and near infrared spectroscopy(Vis/NIRS).Principal component analysis(PCA) was used to analyze the clustering of silkworm egg samples,and offered the principal components of silkworm egg samples.The score plots of first and second components show that PCA can provide the reasonable clustering of the varieties of silkworm eggs,and can be used to analyze the silkworm eggs varieties qualitatively.The scores of the first 6 principal components computed by PCA were applied as the inputs of a back propagation neural network with one hidden layer.100 samples from four varieties were selected randomly to build BP-ANN model,and then the model was used to predict the varieties of 20 unknown samples.The discrimination rate of 100% was achieved.It indicates that this model is reliable and practicable.So this model can offer a new approach to the fast discrimination of varieties of silkworm egg.