RUI: Harmonious and Parsimonious Considerations for Correcting New Chemical and Instrumental Effects and Calibration Transfer

爱达荷州立大学John Kalivas教授的这项研究得到了化学学部分析和表面化学项目和数学科学部统计项目的支持，该项目隶属于美国国家科学基金会数学科学优先领域。这是一项旨在改进光谱数据分析的统计研究。这些活动属于称为“化学计量学”的专业领域。这个想法是开发多元校准，当预测因子的数量超过样本数量（pn，而不是通常的假设np）时，这是有趣的。Kalivas教授提出了三种新的模型方法，以解决将在一种仪器上开发的校准模型转移到其他仪器的一般问题，或为几种仪器建立校准模型的问题。这三种模型都基于吉洪诺夫正则化。每个都使用方差和偏差来解决和谐和和谐/简约平衡这两个问题。这项工作是由本科生使用MATLAB程序完成的。数据分析的效率和有效性在包括生物学在内的所有定量科学中变得越来越重要。在这些领域的进步需要在自然科学学科中整合最先进的统计方法。用这些先进的方法对本科生进行教育，为他们从事任何类型的科学专业工作做好准备。

This study by Professor John Kalivas of Idaho State University is supported by the Analytical and Surface Chemistry Program of the Chemistry Division and the Statistics Program of the Division of Mathematical Sciences under the umbrella of the NSF-wide Mathematical Sciences Priority Area. This is a statistical study aimed at improving analysis of spectroscopic data. These activities fall in the specialized field called "chemometrics." The idea is to develop multivariate calibration, which is of interest when the number of predictors exceeds the number of samples (pn, rather than the usual assumption np). Professor Kalivas proposes three new model approaches that address the general problem of the transfer of a calibration model developed on one instrument to other instruments, or the problem of building a calibration model for several instruments.All three models are based on Tikhonov Regularization. Each uses both variance and bias to address the two issues of harmony and harmony/parsimony balance. The work is being done by undergraduates using the program MATLAB.Efficiency and effectiveness of data analysis is becoming increasingly important in all of the quantitative sciences, including biology. The integration of state of the art statistical methods within the natural science disciplines is required for advancement in these fields. Education of undergraduates in these advanced methods prepare them for any type of scientific professional pursuit.