Novel Chemometric Strategies for Multivariate Measurements in Chemistry and the Omics Sciences

化学和组学科学中多变量测量的新型化学计量策略

• 批准号: RGPIN-2018-05242
• 负责人: Wentzell, Peter
• 金额: $ 4.23万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748152
• 关键词: Novel; Chemometric; Strategies; Multivariate; Measurements

Chemical measurements lead scientific discovery and modern measurements are dominated by vectors or matrices of signals, referred to as multivariate data. These data sets can consist of optical spectra (visible, infrared, Raman, etc.), mass spectra, NMR spectra, chromatographic data, sensor array signals, and combinations of these, as well as compositional data reflecting the state of a system (e.g. ecosystem, cell, chemical process) or its origins (e.g. foods, drugs). Applications employing such data are pervasive, encompassing areas such as medical diagnostics, drug discovery, metabolomics, imaging at microscopic/macroscopic levels, forensics, threat detection, food quality and security, pharmaceutical authentication, environmental monitoring, industrial process control and many more. Many analytical instrument companies, as well as well-known IT leaders, are engaged in research to develop smart hand-held sensors and tools for personalized medicine that exploit miniaturized multivariate sensing technologies already in place.The extraction of relevant chemical information from multivariate data typically requires advanced tools due to the complexity of the systems under study. The expansion of these tools is the primary goal of this proposal, which is organized along two general lines. The first is the development of better data visualization methods, which are key to virtually all multivariate analysis strategies in chemistry, especially in the “omics” fields. A common approach in the analysis of multidimensional data is to project it into lower dimensions while preserving the important information so that the relationships among samples (e.g. healthy vs diseased patients, geographic origin of foods) can be more easily visualized. This work will develop new projection methods which use more effective criteria to preserve the information in the signals. The second line of research seeks a better understanding of the errors associated with the chemical measurements themselves, with the goal of using that knowledge to improve the quality of information extracted. All data analysis methods attempt to separate meaningful chemical signals from the “noise”, and this is more effective when the latter is better understood. The proposed research will develop tools to characterize the structure of the errors (variance and covariance) for particular techniques and refine methods that exploit this knowledge to obtain more accurate and relevant results.This work relies on contextual knowledge of the chemical systems and measurements, as well as expertise in the application of multivariate statistics. Students will receive interdisciplinary skills in analytical chemistry and advanced data science that will prepare them for entry into a knowledge-based economy with an increased emphasis on big data and machine learning, specifically as it relates to chemical systems.

化学测量导致科学发现和现代测量值以信号的向量或成熟度为主，称为多元数据。这些数据集可以由光谱（可见，红外，拉曼等），质谱，NMR光谱，色谱数据，传感器阵列信号以及这些组合组合以及反映系统状态（例如生态系统，细胞，化学过程）或其起源（例如食物）的组成数据。采用此类数据的应用程序普遍存在，包括医学诊断，药物发现，代谢组学，微观/宏观水平的成像，取证，威胁检测，食品质量和安全性，药物身份验证，环境监测，工业过程控制等。许多分析仪器公司以及众所周知的IT领导者都从事研究，以开发智能手持传感器和个性化医学工具，以利用已经已经存在的小型化学化学信息来利用微型化学信息的微型多元感应技术。从多变量数据中提取相关的化学信息，通常需要由于研究中系统的复杂性而需要先进的工具。这些工具的扩展是该提案的主要目标，该目标沿着两条一般线条组织。首先是开发更好的数据可视化方法，这是化学中几乎所有多元分析策略的关键，尤其是在“ OMICS”领域。分析多维数据的一种常见方法是将其投影到较低的维度，同时保留重要信息，以便可以更容易地将样本之间的关系（例如，健康的患者与犹豫不决的患者，食物的地理起源）更容易地可视化。这项工作将开发新的项目方法，这些方法使用更有效的标准来保留信号中的信息。第二条研究人员寻求更好地了解与化学测量本身相关的错误，目的是利用该知识来提高提取的信息的质量。所有数据分析方法都试图将有意义的化学信号与“噪声”分开，并且当更好地理解后者时，这更有效。拟议的研究将开发工具来表征特定技术和完善方法的错误（方差和协方差）的结构，以利用这些知识以获得更准确和相关的结果。这项工作依赖于化学系统和测量的上下文知识，以及在多变量统计的应用中的专家。学生将获得分析化学和高级数据科学方面的跨学科技能，这将使他们准备进入基于知识的经济，并越来越重视大数据和机器学习，特别是与化学系统有关。