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

CDS

基本信息

批准号：
1904166
负责人：
John Kalivas
金额：
$ 40万
依托单位：
Idaho State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1904166&HistoricalAwards=false
关键词：
CDS&amp Adaptive Learning Multivariate Calibration

项目摘要

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.

在化学部化学测量和成像计划的支持下，以及国家司法研究所调查和法医科学办公室的部分共同资助下，爱达荷州立大学的约翰·卡利瓦斯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子信号和样品中感兴趣的信息之间建立数学关系，如植物叶片的氮含量、牛肉中的脂肪百分比或血糖水平。随着生成和共享的数据量的增长，需要改变校准方法的范式。Kalivas小组正在设计一种方法，利用历史校准数据来提高新的化学测量的实用性。该方法的一个战略特点是开发新的数学工具，使实地测量能够适应新的条件和样本类型，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，并精通包括传播在内的科学研究。由于该项目的多学科性质，结果直接造福于行业和社会，具有快速和准确的样本分析的高效算法。该项目利用光谱数据库日益增长的可用性来开发全新的计算过程并伴随算法，以满足对改进和简化的多变量校正日益增长的需求。考虑了导致测量信号的内在化学和物理分子相互作用的基本原理以及仪器特定的问题(条件)，以创建新的自我建模和交叉建模的数据挖掘工具。与传统的全局和局部建模不同，该方法不需要对任何调整参数进行优化。此外，对于目标样本条件，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”的水平，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)最匹配的样本。该项目的结果将推动多变量校准在一系列应用中的应用，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。有了该项目的改进，这些领域将能够更好地形成和维持现场校准，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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。