ESTIMATION AND ERROR ANALYSIS IN MULTIMODAL SPECTROSCOPY

多模态光谱的估计和误差分析

• 批准号: 7600880
• 负责人: OBRAD SCEPANOVIC
• 金额: $ 2.34万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7600880
• 关键词: Affect; Calibration; Chemicals; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Disadvantaged; Error Sources; Fluorescence Spectroscopy; Funding; Grant; Institution; Measurement; Modeling; Multivariate Analysis; Noise; Process; Raman Spectrum Analysis; Range; Research; Research Personnel; Resolution; Resources; Sampling; Signal Transduction; Source; Spectrum Analysis; Standards of Weights and Measures; Tissues; Uncertainty; United States National Institutes of Health; base; interest

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Raman spectroscopy provides quantitative spectral information about sample composition. The presence of noise and multiple overlapping components complicates attempts to use Raman peak intensities for accurate quantitative analysis. Thus, multivariate analysis is often implemented to extract quantitative information from Raman spectra. The concentration uncertainty resulting from chemometric analysis is typically assessed by comparison to a reference measurement. Reference measurements have the disadvantage of introducing an additional source of error, contained in the reference concentration. Further, in many biologic samples, such as tissue, obtaining reference concentrations is not straightforward. We have derived an analytical formula that estimates the uncertainty in concentrations predicted by linear multivariate calibration, applicable to both Raman and fluorescence spectroscopy. The expression is a function of the signal to noise ratio and the model spectral overlap. The first term is the signal to noise ratio of the target chemical. It is determined by the integrated signal (norm) of the component of interest at unit concentration and the noise (standard deviation) in the data set. The second term describes the effect of overlap in model basis spectra. The spectral overlap is affected by the similarity of the components in the model, the resolution of the spectral data, and the spectral range of the data. The analytical formula is expressed in terms of easily quantifiable experimental parameters and is straightforward to evaluate. The ability to make predictions concerning the concentration uncertainty is valuable to the process of developing and refining analytical measurements.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 拉曼光谱法提供了有关样品组成的定量光谱信息。噪声和多个重叠成分的存在使尝试使用拉曼峰强度进行准确的定量分析复杂。因此，通常实施多元分析以从拉曼光谱中提取定量信息。通常通过与参考测量值进行比较评估化学计量分析引起的浓度不确定性。参考测量值的缺点是引入参考浓度中包含的其他错误源。此外，在许多生物样品（例如组织）中，获得参考浓度并不直接。我们得出了一个分析公式，该公式估计了线性多元校准预测的浓度的不确定性，适用于拉曼和荧光光谱。该表达式是信号与噪声比和模型光谱重叠的函数。第一项是目标化学物质的信号与噪声比。它取决于单位浓度和数据集中噪声（标准偏差）在单位浓度和噪声（标准偏差）中的集成信号（规范）确定。第二项描述了模型基谱中重叠的影响。光谱重叠受模型中组件的相似性，光谱数据的分辨率以及数据的光谱范围的影响。分析公式以易于量化的实验参数表示，并且很容易评估。对浓度不确定性进行预测的能力对于开发和完善分析测量的过程很有价值。