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首页> 外文学位 >Quantitative infrared spectroscopy in challenging environments: Applications to passive remote sensing and process monitoring.
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Quantitative infrared spectroscopy in challenging environments: Applications to passive remote sensing and process monitoring.

机译：挑战性环境中的定量红外光谱：在无源遥感和过程监控中的应用。

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摘要

Chemometrics is a discipline of chemistry which uses mathematical and statistical tools to help in the extraction of chemical information from measured data. With the assistance of chemometric methods, infrared (IR) spectroscopy has become a widely applied quantitative analysis tool. This dissertation explores two challenging applications of IR spectroscopy facilitated by chemometric methods: (1) passive Fourier transform (FT) remote sensing and (2) process monitoring by near-infrared (NIR) spectroscopy.;Passive FT-IR remote sensing offers a measurement method to detect gaseous species in the outdoor environment. Two major obstacles limit the application of this method in quantitative analysis: (1) the effect of both temperature and concentration on the measured spectral intensities and (2) the difficulty and cost of collecting reference data for use in calibration. To address these problems, a quantitative analysis protocol was designed based on the use of a radiance model to develop synthetic calibration data. The synthetic data served as the input to partial least-squares (PLS) regression in order to construct models for use in estimating ethanol and methanol concentrations. The methodology was tested with both laboratory and field remote sensing data.;Near-infrared spectroscopy has attracted significant interest in process monitoring because of the simplicity in sample preparation and the compatibility with aqueous solutions. For use in process monitoring, the need exists for robust calibrations. A challenge in the NIR region is that weak, broad and highly overlapped spectral bands make it difficult to extract useful chemical information from measured spectra. In this case, signal processing methods can be helpful in removing unwanted signals and thereby uncovering useful information. When applying signal processing as a spectral preprocessing tool and regression analysis for building a quantitative calibration model, optimizing the parameters that specify the details of the methods is crucial. In this research, particle swarm optimization, a population-based optimization method was applied. Digital filtering and wavelet processing methods were evaluated for their utility as spectral preprocessing tools.;Both a pump-controlled flowing system and bioreactor runs involving the yeast, Pichia pastoris, were studied in this work. In investigating the bioreactor runs, insufficient reference data resulted in difficulties in employing the PLS calibration method. Instead, the augmented classical least-squares modeling technique was applied since it requires only pure-component or composite spectra of the analyte and background matrix rather than a large set of mixture samples of known analyte concentration.
机译：化学计量学是一门化学学科，它使用数学和统计工具来帮助从测量数据中提取化学信息。借助化学计量学方法，红外光谱已成为一种广泛应用的定量分析工具。本文探讨了化学计量学方法在红外光谱学中的两个具有挑战性的应用：（1）被动傅里叶变换（FT）遥感和（2）通过近红外（NIR）光谱进行过程监控；;被动FT-IR遥感提供测量室外环境中检测气态物种的方法。两个主要的障碍限制了该方法在定量分析中的应用：（1）温度和浓度对测得的光谱强度的影响；（2）收集用于校准的参考数据的难度和成本。为了解决这些问题，基于辐射模型设计了定量分析方案，以开发合成校准数据。合成数据用作偏最小二乘（PLS）回归的输入，以便构建用于估计乙醇和甲醇浓度的模型。该方法已通过实验室和现场遥感数据进行了测试。由于样品制备的简便性以及与水溶液的兼容性，近红外光谱法引起了人们对过程监控的极大兴趣。为了在过程监控中使用，需要鲁棒的校准。近红外区域的挑战是，弱，宽且高度重叠的光谱带使其难以从测量的光谱中提取有用的化学信息。在这种情况下，信号处理方法有助于去除不想要的信号，从而发现有用的信息。当将信号处理用作频谱预处理工具并进行回归分析以建立定量校准模型时，优化指定方法详细信息的参数至关重要。在这项研究中，应用了基于群体的优化方法粒子群优化。评估了数字滤波和小波处理方法作为光谱预处理工具的实用性。这项工作研究了泵控制的流动系统和涉及酵母毕赤酵母的生物反应器运行。在研究生物反应器运行时，参考数据不足导致采用PLS校准方法有困难。相反，使用了增强的古典最小二乘建模技术，因为它只需要分析物和背景基质的纯组分或复合光谱，而不需要大量已知分析物浓度的混合样品。

著录项

  • 作者

    Guo, Qiaohan.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Chemistry General.;Chemistry Physical.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 259 p.
  • 总页数 259
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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