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

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理事会的计算和数据支持科学与工程计划资助的项目中，爱达荷州州立大学化学系的John Kalivas教授正在开发新的多元校准方法。校准是一个多学科的问题，但在分析化学中，它涉及在仪器信号（例如来自手持式近红外光谱仪）和样品中感兴趣的性质（例如树木的果肉含量或片剂中活性药物成分的量）之间形成定量数学关系。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，没有参考值的样品光谱。使用未标记的数据，校准成本和时间将大大减少。另一个关键特征是数学过程，即使没有未标记的数据，也可以以最小的努力将校准适应新的测量条件。至少有四名来自爱达荷州州立大学的本科生和一名西班牙裔教授作为阿尔伯克基中央新墨西哥州社区学院的研究机会奖，他们将学习最先进的校准方法，并精通包括传播在内的科学研究。本科生在这些先进的方法教育准备他们随后的科学专业追求。这个项目的重点是新的正则化过程的多变量校准和预测的基础上吉洪诺夫正则化。校准过程允许多个惩罚（调谐参数），从而在形成准确和精确的校准和机制以使校准适应新条件方面提供灵活性。这些正则化方法能够包含未标记的数据，没有参考值的样本。由于校准中的昂贵组件是获得参考值，因此使用未标记的数据可以大大降低形成和维护校准的成本。为了选择最佳值的调谐参数，新的融合排名方法将开发和应用。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）来使校准适应新的域。该项目的结果将推动多学科领域的多元校正。例如，制药和化工行业的过程分析技术、环境和农业监测以及医疗诊断都依赖于多变量校准。随着该项目的目标改进，这些不同的领域可能会更好地进行和维持校准。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，使新来者和从业人员能够直接访问。

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.