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Bayesian Information Fusion for Robust Classification and Quantification of ill-defined Biosamples with Applications to Studies of Microalgae

贝叶斯信息融合对不确定生物样本的稳健分类和定量及其在微藻研究中的应用

基本信息

批准号：
1058695
负责人：
Frank Vogt
金额：
$ 40.46万
依托单位：
University of Tennessee Knoxville
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-02-15 至 2016-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1058695&HistoricalAwards=false
关键词：
Bayesian Information Fusion Robust Classification

项目摘要

Professor Frank Vogt of the University of Tennessee Knoxville is supported by the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry to develop chemometric methods for the qualitative and quantitative analysis of complex chemical and biological samples. The specific objective is to develop "Bayesian Information Fusion," a novel chemometric concept which merges different types of information from complementary sources for sample classification. To further enhance the accuracy of the classification and the quantification, a novel "Plausibility Analysis" software will be developed and incorporated. As a proof-of-principle, these novel chemometric methodologies will be applied to FTIR spectroscopic data obtained from microalgae samples with the goal to utilize them as environmental bioprobes.The analysis of complex chemical and biological samples is extremely challenging because of the large number of analytes present and their wide range of concentrations, and chemometric data analysis is increasingly becoming an invaluable analytical chemistry tool for improved classification and quantification of such samples.The project results will be integrated in classroom material by means of the cyber-enabled and interactive software package "Virtual Chemometrics Laboratory."

田纳西诺克斯维尔大学的Frank Vogt教授得到化学系化学测量和成像(CMI)计划的支持，开发用于对复杂的化学和生物样品进行定性和定量分析的化学计量学方法。具体目标是开发“贝叶斯信息融合”，这是一种新的化学计量学概念，它融合了来自互补来源的不同类型的信息，用于样品分类。为了进一步提高分类和量化的准确性，将开发并纳入一种新的“似是而非分析”软件。作为一项原则证明，这些新的化学计量学方法将应用于从微藻样本中获得的FTIR光谱数据，目的是将其用作环境生物样本。由于存在大量的分析物及其广泛的浓度范围，对复杂的化学和生物样本的分析极具挑战性，化学计量学数据分析正日益成为改进此类样本分类和量化的宝贵分析化学工具。项目成果将通过网络支持和交互的软件包“虚拟化学计量学实验室”整合到课堂材料中。