ESTIMATION AND ERROR ANALYSIS IN MULTIMODAL SPECTROSCOPY

多模态光谱的估计和误差分析

• 批准号: 7357933
• 负责人: OBRAD SCEPANOVIC
• 金额: $ 2.03万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7357933
• 关键词: measurement; model; noise

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 errors resulting from chemometric analysis are 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, particularly ordinary least-squares (OLS). 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. To test this formula, we performed OLS analysis upon simulated spectra and upon experimental Raman spectra of dissolved biological analytes in water. In each instance, the root-mean-squared error of prediction was compared to the estimate from the formula. We observe excellent agreement between the formula and data for these simple systems. The ability to make predictions concerning the concentration error is valuable to the process of developing and refining analytical measurements.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。拉曼光谱提供了关于样品组成的定量光谱信息。噪声和多个重叠组分的存在使使用拉曼峰强度进行精确定量分析的尝试复杂化。因此，经常实施多变量分析以从拉曼光谱提取定量信息。由化学计量分析产生的浓度误差通常通过与参考测量值进行比较来评估。参比测量的缺点是引入了包含在参比浓度中的额外误差源。此外，在许多生物样品中，例如组织，获得参考浓度并不简单。我们已经推导出一个分析公式，估计线性多元校正，特别是普通最小二乘法（OLS）预测的浓度的不确定性。该表达式是信噪比和模型光谱重叠的函数。第一项是目标化学品的信噪比。它由单位浓度下感兴趣组分的积分信号（范数）和数据集中的噪声（标准差）确定。第二项描述了模型基础光谱中重叠的影响。光谱重叠受模型中组分的相似性、光谱数据的分辨率和数据的光谱范围的影响。的分析公式表示在易于量化的实验参数，是简单的评估。为了测试这个公式，我们进行了OLS分析后，模拟光谱和实验拉曼光谱的溶解在水中的生物分析物。在每种情况下，将预测的均方根误差与公式的估计值进行比较。我们观察到这些简单系统的公式和数据之间非常一致。预测浓度误差的能力对于开发和改进分析测量的过程是有价值的。