辐照处理对油菜籽光谱特性影响的研究

doi:10.3964/j.issn.1000-0593(2008)11-2540-05

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摘要：针对油菜籽经过核辐照处理后其光谱反射特性会发生改变的特点，提出了应用可见／近红外光谱技术进行油菜籽的快速无损鉴别。利用偏最小二乘法和BP神经网络建立鉴别模型，并比较了不同光谱预处理方法、主成分数据变换方式及隐含层节点数对模型预测结果的影响。实验采用五种剂量辐照(50, 100, 150, 200Gy和不经核辐照处理)的油菜籽共135个样本进行建模，49个进行预测。结果显示，最优模型是原始光谱数据先经过中值滤波平滑法、附加散射校正及一阶求导法预处理。经PLS方法提取6个主成分经自然对数变换后，选取神经网络隐含层结点数为4个或9个。最优模型对是否经过核辐照处理的样本识别率达100%，对核辐照剂量预测精度为85.71%, 说明提出的方法可以用于评估核辐照处理对油菜籽光谱特性产生的明显影响。

关键词：可见-近红外光谱;甘蓝型油菜;核辐照;偏最小二乘;人工神经网络

Abstract：After being treated by gamma-ray, the spectral characteristic of rapeseed would be changed. Based on the principle, a rapid and nondestructive method by using visible and near infrared spectroscopy was proposed to discriminate rapeseeds (Brassica nupus) treated by different dosages of gamma-ray. Partial least square (PLS) method and BP neural network (BPNN) were applied to establish the discrimination model, and the influences of different pretreatment methods of original spectra data, data transformation methods of PLS principal components and the selection of node number of hidden layers of BP neural network model on prediction precision were compared and discussed. In the experiment, 184 samples were treated by gamma-ray with 5 different dosages (50, 100, 150, 200 Gy, and the samples without gamma-ray treatment). Then spectra tests were performed on the 184 samples using a spectrophotometer (325-1 075 nm). One hundred thiry five samples were selected randomly for model calibration and the left 49 samples were used for prediction. As a result, the optimal model was established and the parameters of the model were shown as follows. The original spectra data were pretreated by smoothing media filter, multiplicative scatter correction and Savitzky-Golay derivatives, then 6 PLS principal components were selected by using partial least square method. After being transformed by using natural logarithm transformation method, the 6 PLS principal components were used as the input layer factors to establish the BP neural network model and the node number of hidden layers was selected as 4 or 9. The prediction precision of the optimal model to distinguish the untreated samples from gamma-ray treated samples was 100%. The precision of predicting the dosages of gamma-ray treatment of all samples achieved 85.71%. It can be concluded that the proposed method for estimating the influence of different gamma-ray dosages on the spectral characteristic of treated rapeseeds was feasible.

Key words：Vis/NIR spectroscopy;Brassica napus;Gamma-ray;Partial least square;BP artificial neural network

收稿日期: 2007-05-26 修订日期: 2007-09-06

中图分类号:	S56
	TL99

通讯作者: 何勇 E-mail: yhe@zju.edu.cn

引用本文:

黄敏¹,王遵义²,何勇^1*. 辐照处理对油菜籽光谱特性影响的研究[J]. 光谱学与光谱分析, 2008, 28(11): 2540-2544.
HUANG Min¹,WANG Zun-yi²,HE Yong^1*. Study on Influence of Gamma-Ray Treatment on Spectral Characteristic of Rapeseed. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(11): 2540-2544.

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