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首页> 外文学位 >Development of Radial Basis Function Cascade Correlation Networks and Applications of Chemometric Techniques for Hyphenated Chromatography- Mass Spectrometry Analysis.
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Development of Radial Basis Function Cascade Correlation Networks and Applications of Chemometric Techniques for Hyphenated Chromatography- Mass Spectrometry Analysis.

机译：径向基函数级联相关网络的发展和化学计量技术在联用色谱-质谱分析中的应用。

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摘要

A cascade correlation learning architecture has been devised for radial basis function neural networks. Cascade correlation furnishes incremental learning networks. The proposed algorithm was applied to three different datasets: a synthetic dataset and two chemical datasets. The synthetic dataset was used to test the novelty detection ability of the proposed network. In the chemical datasets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate the proposed neural network is useful for pattern recognition.;A discriminant based charge deconvolution analysis pipeline is proposed. The molecular weight determination (MoWeD) charge deconvolution method was applied directly to the discrimination rules obtained by the fuzzy rule-building expert system (FuRES) pattern classifier. This approach was demonstrated with synthetic electrospray ionization mass spectra. Identification of the tentative protein biomarkers by bacterial cell extracts of Salmonella enterica serovar typhimurium strains A1 and A19 by liquid chromatography--electrospray ionization-mass spectrometry (LC--ESI-MS) was also demonstrated. The data analysis time was reduced by applying this approach. Furthermore, this method was less affected by noise and baseline drift.;The gasoline and kerosene collected from different locations in the United States were identified by gas chromatography/mass spectrometry (GC/MS) followed by chemometric analysis. Classifications based on twoway profile and target component ratio were compared. The projected difference resolution (PDR) mapping was applied to measure the differences among the ignitable liquid (IL) samples by their GC/MS profiles quantitatively. FuRESs were applied to classify individual ILs. The FuRES models yielded correct classification rates greater than 90% for discriminating between samples. PDR mapping, a new method for characterizing complex data sets, was consistent with the FuRES classification result.
机译：已经为径向基函数神经网络设计了一种级联相关学习架构。级联相关提供了增量学习网络。该算法被应用于三个不同的数据集：一个合成数据集和两个化学数据集。综合数据集用于测试所提出网络的新颖性检测能力。在化学数据集中，意大利橄榄油的生长区域通过其脂肪酸谱进行了鉴定；聚氯联苯化合物的质谱按氯数分类。自举拉丁分区的预测结果表明所提出的神经网络对模式识别很有用。;提出了一种基于判别式的电荷反卷积分析流水线。分子量确定（MoWeD）电荷反褶积方法直接应用于由模糊规则建立专家系统（FuRES）模式分类器获得的判别规则。合成电喷雾电离质谱证明了这种方法。还证明了通过液相色谱-电喷雾电离质谱（LC-ESI-MS）沙门氏菌血清鼠伤寒沙门氏菌菌株A1和A19的细菌细胞提取物鉴定暂定蛋白生物标志物。通过应用这种方法，可以减少数据分析时间。此外，该方法受噪声和基线漂移的影响较小。;通过气相色谱/质谱法（GC / MS），然后进行化学计量学分析，从美国不同位置收集汽油和煤油。比较了基于双向分布和目标成分比率的分类。投影差异分辨率（PDR）映射用于通过可燃液体（IL）样品的GC / MS曲线定量测量其差异。应用FuRESs对单个IL进行分类。 FuRES模型产生的正确分类率大于90％，可以区分样本。 PDR映射是一种表征复杂数据集的新方法，与FuRES分类结果一致。

著录项

  • 作者

    Lu, Weiying.;

  • 作者单位

    Ohio University.;

  • 授予单位 Ohio University.;
  • 学科 Chemistry Analytical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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