CDS&E: Adaptive Learning for Multivariate Calibration with Big Data Attributes

在化学部门化学测量和成像项目的支持下，以及国家司法研究所调查和法医学办公室的部分共同资助下，爱达荷州立大学的John Kalivas教授和他的团队正在开发新的数据分析方法，目标是解决复杂的校准问题。校准是一个多学科的问题。在化学中，它涉及到在仪器测量得到的测量电信号和样品中感兴趣的信息（如植物叶片的氮含量、牛肉中的脂肪百分比或血糖水平）之间形成数学关系。随着生成和共享数据量的增长，需要对校准方法进行范式转换。Kalivas小组正在设计利用历史校准数据的方法，以提高新的化学测量的效用。该方法的一个战略特点是开发新的数学工具，使现场测量适应新的条件和样品类型，而无需复杂的实验室分析，从而减少分析时间和成本。通过参与这项工作，爱达荷州立大学的本科生正在学习最先进的校准方法，并精通科学研究，包括传播。由于该项目的多学科性质，其结果直接使行业和社会受益，有效的算法可以快速准确地分析样本。该项目利用光谱数据库日益增加的可用性来开发全新的计算过程和伴随算法，以满足对改进和简化多变量校准日益增长的需求。固有的化学和物理分子相互作用负责测量信号的基本原理与仪器特定的问题（条件）一起考虑，以创建新的自我和交叉建模数据挖掘工具。与传统的全局和局部建模不同，该方法不需要优化任何调优参数。此外，目标样本条件下不需要参考值；因此，该方法被认为是半监督学习。该项目的一个关键目标是将多变量校准提升到“大数据”水平，开发有效的算法来揭示有用的信息。使用参考数据库，数据挖掘算法识别与新样本（一个或多个）匹配的样本最佳矩阵。该项目的成果将在一系列应用中推进多变量校准，包括制药和化学工业的过程分析技术、环境和农业监测以及医疗诊断。随着该项目的改进，这些油田将能够更好地在现场形成和维持校准，从而消除了复杂的化学分析的需要。所有开发的算法都将发布在Kalivas网站上，允许潜在用户免费访问。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Measurement and Imaging program in the Chemistry Division, and partial co-funding from the Office of Investigative and Forensic Sciences in the National Institute of Justice, Professor John Kalivas and his group at Idaho State University are developing new methods of data analysis with the target of solving complex calibration problems. Calibration is a multidisciplinary problem. In chemistry it involves forming a mathematical relationship between measured electronic signals derived from instrumental measurements and information of interest in a sample, such as the nitrogen content of plant leaves, percent fat in beef, or the blood glucose level. With the growth of the amount of generated and shared data, there is a need for a paradigm shift in calibration methods. The Kalivas group is devising means to exploit historical calibration data to improve the utility of new chemical measurements. A strategic feature of the approach is the development of new mathematical tools enabling adaptation of field-based measurements to new conditions and sample types without complex laboratory analyses, thereby reducing analysis time and costs. Through participation in this work, undergraduates from Idaho State University are learning state-of-the-art calibration methods and becoming proficient at scientific research, including dissemination. Because of the multidisciplinary nature of the project, outcomes directly benefit industry and society with efficient algorithms for rapid and accurate analysis of samples. This project exploits the increasing availability of spectral databases to develop completely new computational processes and accompany algorithms to address the growing need for improved and simplified multivariate calibration. The fundamentals of the inherent chemical and physical molecular interactions responsible for the measured signal are considered along with instrument-specific issues (conditions) to create new self- and cross-modeling data mining tools. Unlike conventional global and local modeling, the method does not require optimization of any tuning parameters. Additionally, reference values are not needed for target sample conditions; the method is therefore considered to be semi-supervised learning. A key goal of the project is to advance multivariate calibration to a "big-data" level, developing efficient algorithms to reveal useful information. Using reference databases, the data-mining algorithms identify samples best matrix-matched to new samples (one or many). Results from this project will advance multivariate calibration in a range of applications, including process analytical technology for the pharmaceutical and chemical industries, environmental and agriculture monitoring, and medical diagnostics. With the improvements contributed by this project, these fields will be better able to form and sustain calibrations on site, removing the need for complex chemical analysis. All developed algorithms will be posted to the Kalivas web site, allowing free access to potential users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.