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

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.*