CDS&E: Regularization Adaption Processes for Multivariate Calibration and Maintenance

CDS

• 批准号: 1506417
• 负责人: John Kalivas
• 金额: $ 45.33万
• 依托单位: Idaho State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1506417&HistoricalAwards=false
• 关键词: CDS& Regularization; Adaption; Processes; Multivariate

In this project funded by the Chemical Measurement and Imaging program of the Chemistry Division, and the Computational and Data-Enabled Science and Engineering program of the Directorate for Mathematical and Physical Sciences, Professor John Kalivas of the Department of Chemistry at Idaho State University is developing new methods of multivariate calibration. Calibration is a multidisciplinary problem, but in analytical chemistry, it involves forming a quantitative mathematical relationship between an instrumental signal, such as from a hand held near infrared spectrometer, and a property of interest in a sample, such as the pulp content of trees or the amount of the active pharmaceutical ingredient in tablets. A key feature of the new mathematical calibration processes is the ability to include unlabeled data, for instance, spectra of samples without reference values. With unlabeled data, calibration costs and time will be substantially reduced. Another key feature is a mathematical process allowing adaption of a calibration to new measurement conditions with minimal effort, even if unlabeled data is not available. A minimum of four undergraduates from Idaho State University and a Hispanic professor as a Research Opportunity Award from Central New Mexico Community College in Albuquerque will learn state-of-the-art calibration methods and become proficient at performing scientific research including dissemination. Education of undergraduates in these advanced methods prepares them for subsequent scientific professional pursuits.This project focuses on new regularization processes for multivariate calibration and prediction based on the fundamentals of Tikhonov regularization. The calibration processes allow multiple penalties (tuning parameters) providing flexibility in forming accurate and precise calibrations and mechanisms to adapt a calibration to new conditions. With these regularization methods comes the ability to include unlabeled data, samples without reference values. Because the expensive component in calibration is obtaining reference values, costs in forming and maintaining calibrations can be substantially reduced with unlabeled data. In order to select optimal values for tuning parameters, new fusion ranking methods will be developed and applied. The overall objective is development of computational regularization processes to adapt a calibration to a new domain using multiple tuning parameters, multiple calibration merits, and unlabeled data if it is available. Results from this project will advance the multidisciplinary field of multivariate calibration. For example, process analytical technology for the pharmaceutical and chemical industries, environmental and agriculture monitoring, and medical diagnostics all rely on multivariate calibration. With the improvements being targeted in this project, these diverse fields may well be better equipped to perform and sustain calibrations. Another broader impact of this project is the planned dissemination of the calibration algorithms via the investigators' web sites, allowing newcomers and practitioners direct access.

在这个由化学部的化学测量和成像计划以及数学和物理科学局的计算和数据使能科学与工程计划资助的项目中，爱达荷州立大学化学系的约翰·卡利瓦斯教授正在开发新的多变量校准方法。校准是一个多学科的问题，但在分析化学中，它涉及到在仪器信号(如来自手持近红外光谱仪的仪器信号)和样品中感兴趣的性质(如树木的果肉含量或片剂中有效药物成分的量)之间建立定量的数学关系。新的数学校准过程的一个关键特征是能够包括未标记的数据，例如没有参考值的样品的光谱。有了未标记的数据，校准成本和时间将大大减少。另一个关键功能是一个数学过程，即使没有未标记的数据，也可以最小限度地使校准适应新的测量条件。爱达荷州立大学的至少四名本科生和阿尔伯克基新墨西哥州中部社区学院的一名西班牙裔教授将学习最先进的校准方法，并熟练地进行包括传播在内的科学研究。对本科生进行这些先进方法的教育，为他们随后的科学专业追求做好准备。本项目专注于基于Tikhonov正则化基本原理的多变量校准和预测的新正则化过程。校准过程允许多个惩罚(调整参数)，在形成准确和精确的校准和使校准适应新条件的机制方面提供了灵活性。随着这些正则化方法的出现，可以包括未标记的数据、没有参考值的样本。由于校准中昂贵的组成部分是获得参考值，因此利用未标记的数据可以大大降低形成和维护校准的成本。为了选择最优值来调整参数，将开发和应用新的融合排序方法。总体目标是开发计算正则化过程，以使用多个调谐参数、多个校准优点和未标记数据(如果可用)使校准适应新的领域。这一项目的结果将推动多变量校准的多学科领域。例如，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都依赖于多变量校准。随着这个项目的目标是改进，这些不同的领域可能会更好地装备来执行和维持校准。该项目的另一个更广泛的影响是计划通过调查人员的网站传播校准算法，允许新手和从业人员直接访问。