Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra
Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra
复制标题
物理化学响应综合相似性测量 (PRISM)，用于通过近红外光谱动态评估样品相似性的全面定量视角
DOI：
10.1021/acs.analchem.3c01616
复制
发表时间：
2023
影响因子：
7.4
通讯作者：
Kalivas, John H.
中科院分区：
文献类型：
--
作者：
Spiers, Robert C.;Norby, Callan;Kalivas, John H.
确定样品相似性是分析化学中许多基本原则的基础。例如，校准模型不适合预测离群值。校准传递方法假设校准集和目标预测样本之间的样本和测量差异程度适中。分类方法将目标样本相似性与相似类样本的组相关联。虽然相似性在分析化学和日常生活中无处不在，但量化样品相似性并没有一个简单的解决方案，特别是当目标域样品未标记并且唯一已知的特征是可测量的时，例如光谱（本文的重点）。所提出的评估样品相似性的过程将光谱相似性信息与源分析物含量、模型和分析物预测之间的背景考虑相结合。这种混合的方法命名为物理化学响应的综合相似性度量（PRISM）放大隐藏的，但必要的物理化学特性编码内各自的光谱。PRISM在四个不同应用领域的四个近红外（NIR）数据集上进行了测试，以显示其有效性。这些应用是评估预测的可靠性和模型更新的模型推广，离群值检测和基本的矩阵匹配评估。讨论了适应PRISM分类问题。结果表明，PRISM收集了大量的相似性信息，并有效地将其整合，以产生一个定量的目标样本和源域之间的相似性评价。该方法也适用于具有额外理化变化的生物样品。虽然PRISM是在近红外数据上进行动态测试的，但PRISM的一部分以前曾应用于其他数据类型，PRISM应适用于受基质效应干扰的其他测量系统。
Determining sample similarity underlies many foundational principles in analytical chemistry. For example, calibration models are unsuitable to predict outliers. Calibration transfer methods assume a moderate degree of sample and measurement dissimilarities between a calibration set and target prediction samples. Classification approaches link target sample similarities to groups of similar class samples. Although similarity is ubiquitous in analytical chemistry and everyday life, quantifying sample similarity is without a straightforward solution, especially when target domain samples are unlabeled and the only known features are measurable, such as spectra (the focus of this paper). The process proposed to assess sample similarity integrates spectral similarity information with contextual considerations among source analyte contents, model, and analyte predictions. This hybrid approach named the physicochemical responsive integrated similarity measure (PRISM) amplifies hidden-but-essential physicochemical properties encoded within respective spectra. PRISM is tested on four near-infrared (NIR) data sets for four diverse application areas to show efficacy. These applications are the assessment of prediction reliability and model updating for model generalizability, outlier detection, and basic matrix matching evaluation. Discussion is provided on adapting PRISM to classification problems. Results indicate that PRISM collects large amounts of similarity information and effectively integrates it to produce a quantitative similarity evaluation between the target sample and a source domain. The approach is also useful for biological samples with additional physiochemical variations. While PRISM is dynamically tested on NIR data, parts of PRISM were previously applied to other data types, and PRISM should be applicable to other measurement systems perturbed by matrix effects.