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首页> 外文学位 >Calibration transfer in Fourier transform infrared remote sensing and in the near-infrared spectroscopic determination of clinical analytes.
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Calibration transfer in Fourier transform infrared remote sensing and in the near-infrared spectroscopic determination of clinical analytes.

机译：傅里叶变换红外遥感和临床分析物的近红外光谱测定中的标定传递。

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Of the many instrumental technologies provided by analytical chemistry, the chemical selectivity, sensitivity, and flexibility provided by Fourier transform infrared (FT-IR) spectrometry makes it a common choice in broad classes of applications requiring a fast, non-destructive sensing technique. Arising as a practical technology after the arrival of computers, the analysis of the complex digitized chemical information provided by FT-IR spectrometry remains intimately intertwined with computer-based techniques, beginning with the Fast Fourier Transform itself. Like many other modern analytical methods, FT-IR spectrometry is capable of producing tremendous quantities of data that often require complex analysis techniques. These techniques attempt to leverage data into information that can be used to make practical decisions. Techniques discussed in this work in the computer-based analysis of FT-IR data include digital signal processing, digital filtering, pattern recognition, multivariate calibration, and optimization. Whether in the automated qualitative determination of air pollutants in industrial stack monitoring, or a highly sensitive quantitative determination of clinical analytes, when applied to FT-IR data, these data analysis techniques seek the suppression of interfering signals and the extraction of analyte information, allowing these important applications to be realized.; Techniques applied to FT-IR data are becoming more powerful in extracting the maximum amount of chemical information from the instrumental signal. Often, however, signals not related to the analyte such as background features, temperature information, and spectrometer-specific information become included in the multivariate models used in qualitative or quantitative prediction. These interferences can represent orders of magnitude more variation in the FT-IR data than the changes in the signal arising from the analytes of interest. If the model includes any of these kinds of information, the resulting prediction can be of poor quality if any aspect of the analytical measurement environment has changed. The large amount of work required in collecting and analyzing training data used to build these models then needs to be repeated for the different experimental conditions, environment, or different spectrometer.; The work presented in this dissertation focuses on exploring these calibration transfer issues while overcoming difficult challenges posed by the analysis of FT-IR data from two different applications. First, qualitative passive FT-IR remote sensing of several analytes is performed with field and laboratory data collected with different spectrometers. The role of digital filtering is examined in removing instrument-specific signals and allowing data from either instrument to be predicted with a single model. Second, quantitative near-infrared analysis of clinical glucose and urea samples is performed while examining the performance of models with data collected over two years apart, as well as with data collected with two different spectrometers. Methodologies from enhanced digital filtering to the creation of more robust models are described for improving the calibration transfer results in each of these important projects.
机译：在分析化学提供的众多仪器技术中，傅立叶变换红外（FT-IR）光谱技术提供的化学选择性，灵敏度和灵活性使其成为需要快速，无损传感技术的广泛应用的通用选择。在计算机出现之后，作为一种实用技术出现了，从快速傅里叶变换本身开始，对由FT-IR光谱仪提供的复杂数字化化学信息的分析仍然与基于计算机的技术紧密地交织在一起。像许多其他现代分析方法一样，FT-IR光谱仪能够产生大量数据，这些数据通常需要复杂的分析技术。这些技术试图将数据转化为可用于做出实际决策的信息。本工作中讨论的基于FT-IR数据的计算机分析技术包括数字信号处理，数字滤波，模式识别，多元校准和优化。无论是在工业烟囱监测中对空气污染物进行自动定性测定，还是对临床分析物进行高度灵敏的定量测定，将这些数据分析技术应用于FT-IR数据时，都可寻求抑制干扰信号和提取分析物信息的功能，这些重要的应用有待实现。应用于FT-IR数据的技术在从仪器信号中提取最大数量的化学信息方面变得越来越强大。但是，常常将与分析物无关的信号（例如背景特征，温度信息和光谱仪特定的信息）包括在定性或定量预测所用的多元模型中。这些干扰可以表示FT-IR数据中的变化要比由目标分析物引起的信号变化大几个数量级。如果模型包含这些信息中的任何一种，则如果分析测量环境的任何方面发生了变化，则结果预测的质量可能会很差。然后，需要针对不同的实验条件，环境或不同的光谱仪，重复收集和分析用于构建这些模型的训练数据所需的大量工作。本文提出的工作着眼于探索这些校准传递问题，同时克服了来自两个不同应用的FT-IR数据分析所带来的困难挑战。首先，使用不同光谱仪收集的现场和实验室数据对几种分析物进行定性被动FT-IR遥感。在消除特定于仪器的信号并允许使用单个模型预测来自任一仪器的数据时，将检查数字滤波的作用。其次，对临床葡萄糖和尿素样品进行定量近红外分析，同时检查模型的性能，该模型具有间隔两年以上收集的数据以及两个不同光谱仪收集的数据。描述了从增强的数字滤波到创建更健壮的模型的方法，这些方法可改善每个重要项目中的校准传递结果。

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