Abstract：Visible/near infrared spectroscopy (Vis/NIRS) appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. In the present study,a nondestructive method for the classification of honey brands was developed using Vis/NIRS. The honey brands studied in the research were Feng boshi,Tian ranfeng and Guan shengyuan. The sample set comprised 30 of each brand. Independent component analysis (ICA) was put forwarded to select several optimal wavelengths based on loading weights. Two types of preprocessing (Savitzky-Golay combined with multiplicative scatter correction) were used before the spectral data were analyzed with multivariate calibration methods of artificial neural network (ANN). The absorbance values log (1/T) (T=transmission),corresponding to the wavelengths of 408,412,409,1 000,468,462,408,400,997 and 998 nm were chosen as the input data of ANN. The ANN model with three layers was built,and the transfer function of sigmoid was used in each layer. After several trials,the best neural network architecture was obtained with 10 nodes in hidden layers. In the model,the node of input layer,hidden layer,output layer was set to be 9,10,and 3 respectively,and the goal error was set to be 0.000 1,the speed of learning was set to be 0.2,the time of training was set to be 1 500. Seventy five samples (25 with each brand) from three brands were selected randomly as calibration set,and the left 15 samples (5 with each brand) were as perdition set. The discrimination rate of 100% was achieved,and the fitting residual was 8.245 365×10-5. These indicated that the result of honey discrimination was very good based on ICA method,and offer a new approach to the fast discrimination of varieties of honey.
邵咏妮,何勇,鲍一丹*. 基于独立组分分析和BP神经网络的可见/近红外光谱蜂蜜品牌的鉴别[J]. 光谱学与光谱分析, 2008, 28(03): 602-605.
SHAO Yong-ni,HE Yong,BAO Yi-dan*. Application of Visible/Near Infrared Spectroscopy to Discriminating Honey Brands Based on Independent Component Analysis and BP Neural Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(03): 602-605.
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