高等学校化学学报 ›› 2018, Vol. 39 ›› Issue (7): 1434.doi: 10.7503/cjcu20170830

• 分析化学 • 上一篇    下一篇

基于X射线吸收谱的不同生物组织的辨识

王倩, 杨正, 方正()   

  1. 厦门大学仪器与电气系, 厦门 361005
  • 收稿日期:2017-12-18 出版日期:2018-07-10 发布日期:2018-06-21
  • 作者简介:联系人简介: 方 正, 男, 博士, 教授, 主要从事生物医学和高端科学仪器开发方面的研究. E-mail: fangzheng@xmu.edu.cn
  • 基金资助:
    国家自然科学基金(批准号: 61571381)资助.

Biological Tissue Recognition Based on X-Ray Absorption Spectral Detection

WANG Qian, YANG Zheng, FANG Zheng*()   

  1. Department of Instrumental and Electrical Engineering, Xiamen University,Xiamen 361005, China
  • Received:2017-12-18 Online:2018-07-10 Published:2018-06-21
  • Contact: FANG Zheng E-mail:fangzheng@xmu.edu.cn
  • Supported by:
    † Supported by the National Natural Science Foundation of China(No.61571381).

摘要:

为了验证X射线吸收光谱法对生物组织的辨识能力, 选取猪的心脏、 肝、 肾、 胃、 瘦肉以及肥肉作为实验样本, 以55 kV的电压激发X射线管, 利用X射线探测器获取这6类生物组织样本的X射线吸收光谱. 将采集到的光谱数据分为测试集与训练集, 利用主成分分析法提取光谱主成分, 以训练集为输入建立径向基函数(RBF)神经网络分类预测模型, 对测试集样本进行分类预测. 通过交叉验证法对所有样本进行辨识的识别正确率达到90.22%. 实验结果表明, X射线光谱技术结合主成分分析法和RBF神经网络能够很好地用于猪的组织分类, 对将X射线光谱技术应用于生物组织辨识具有重要的指导意义.

关键词: X射线吸收光谱, 生物组织, 分类识别, 径向基函数神经网络

Abstract:

In order to verify the ability of X-ray absorption spectroscopy to recognize biological tissues, porcine heart, liver, kidney, stomach, lean meat and fat are selected as specimens. The X-ray absorption spectra of these specimens are obtained by X-ray detector at the excitation voltage of 55 kV. The collected spectra are divided into training set and test set, and principal component analysis is used to extract spectral principal component. Using the training set as input, a radial basis function(RBF) neural network model is established to predict the samples of the test set. The recognition rate of all samples through cross-validation method reached 90.22%. The experimental results show that X-ray spectral technique coupled with statistical analysis methods can be used for the classification of pig tissue, which is of great significance to the application of X-ray spectral technique in organism identification.

Key words: X-Ray absorption spectrum, Biological tissue, Material recognition, Radial basis function(RBF) neural network

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