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[γ射线处理对油菜籽光谱特性的影响研究]

[Study on influence of gamma-ray treatment on spectral characteristic of rapeseed].

作者信息

Huang Min, Wang Zun-Yi, He Yong

机构信息

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Nov;28(11):2540-4.

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 PIS 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.

摘要

经伽马射线处理后,油菜籽的光谱特征会发生变化。基于这一原理,提出了一种利用可见近红外光谱的快速无损方法来鉴别不同剂量伽马射线处理的油菜籽(甘蓝型油菜)。采用偏最小二乘法(PLS)和BP神经网络(BPNN)建立鉴别模型,并比较和讨论了原始光谱数据的不同预处理方法、PLS主成分的数据变换方法以及BP神经网络模型隐藏层节点数的选择对预测精度的影响。实验中,184个样本用5种不同剂量(50、100、150、200 Gy以及未进行伽马射线处理的样本)的伽马射线进行处理。然后使用分光光度计(325 - 1075 nm)对184个样本进行光谱测试。随机选取135个样本进行模型校准,其余49个样本用于预测。结果,建立了最优模型,模型参数如下。原始光谱数据经平滑中值滤波、多元散射校正和Savitzky - Golay导数预处理,然后用偏最小二乘法选取6个PLS主成分。经自然对数变换方法变换后,将6个PLS主成分作为输入层因子建立BP神经网络模型,隐藏层节点数选为4或9。最优模型区分未处理样本和伽马射线处理样本的预测精度为100%。预测所有样本伽马射线处理剂量的精度达到85.71%。可以得出结论,所提出的评估不同伽马射线剂量对处理后油菜籽光谱特征影响的方法是可行的。

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