文章摘要
引用本文:王建明,王 武,李祥辉,李玉榕.基于傅里叶近红外特征光谱的血流感染致病菌鉴别研究[J].福州大学学报(自然科学版),2017,45(5):713~718
基于傅里叶近红外特征光谱的血流感染致病菌鉴别研究
Research on identification of bacteria in bloodstream infections based on FT-NIR characteristic spectrum selection
  
DOI:10.7631/issn.1000-2243.2017.05.713
中文关键词: 血流感染  傅里叶变换近红外光谱  偏最小二乘判别分析  最小二乘-支持向量机  竞争性自适应重加权算法  病原菌鉴别
英文关键词: bloodstream infections  FT-NIR  PLS-DA  LS-SVM  CARS  pathogen bacteria discriminate
基金项目:
作者单位
王建明 福州大学电气工程与自动化学院福建 福州 350116 福建省医疗器械和医药技术重点实验室福建 福州 350002 
王 武 福州大学电气工程与自动化学院福建 福州 350116 福建省医疗器械和医药技术重点实验室福建 福州 350002 
李祥辉 福建医科大学医学技术与工程学院福建 福州 350004 
李玉榕 福州大学电气工程与自动化学院福建 福州 350116 福建省医疗器械和医药技术重点实验室福建 福州 350002 
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中文摘要:
      利用傅里叶变换近红外光谱(FT-NIR)收集1000~1852nm范围内3种常见病原菌大肠杆菌(ATCC 25922)、 金黄色葡萄球菌(ATCC 29213)、 铜绿假单胞菌(ATCC 27853)的近红外透射光谱,采用竞争性自适应重加权算法(CARS)对波长变量进行筛选,并分别结合偏最小二乘判别分析(PLS-DA)、 最小二乘-支持向量机(LS-SVM)建立鉴别模型. 比较两种鉴别模型在进行波长变量优选前后的性能发现,采用全波段建模的PLS-DA与LS-SVM两种模型的预测性能较低;利用CARS对波长变量进行筛选后,对优选的24个特征波长分别建立两种鉴别模型,模型预测性能明显提高,其中以LS-SVM模型最优,3种病原菌准确率分别为85.0%,100%和100%. 研究结果表明,利用CARS能够有效去除光谱无用信息,减少模型复杂度,增强模型预测性能,结合LS-SVM可为临床利用近红外快速检测血流感染病原菌提供一种新的方法.
英文摘要:
      Fourier transform near-infrared spectroscopy (FT-NIR) was used to collect the range from 1000 to 1852nm near-infrared spectra of the three common pathogen bacteria which included Escherichia coli,Pseudomonas aeruginosa,and Staphylococcus aureus. Competitive adaptive reweighted sampling (CARS) algorithm to be used to select the characteristic wavelength variables which were used to build the PLS-DA model and LS-SVM model. Moreover,performance of PLS-DA and LS-SVM model built by the characteristic wavelength variables were also compared with PLS-DA and LS-SVM model built by full spectra. Studies showed that full spectra model had poor prediction performance due to some wavelength variables contains irrelevant information. The prediction performance of PLS-DA model and LS-SVM had improved significantly by using 24 characteristic wavelengths selected by CARS. Meanwhile,LS-SVM model achieved the optimal performance which the correct rate of three pathogen bacteria was 85.0%,100%,100% respectively. The results showed that CARS can remove useless information,reducing model complexity,enhanced model prediction performance and combined with LS-SVM can accurately identify clinical pathogens.
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