Photoluminescence Probes in Data-Enabled Sensing.

Claudia Von Suskil, Micaih J Murray, Dipak B Sanap, Sharon L Neal
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引用次数: 0

Abstract

This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis.

数据传感中的光致发光探针。
本文综述了光致发光探针、多维光致发光检测和多变量数据分析方法的发展现状。然后重点介绍了2015年6月至2022年6月期间发表的光致发光探针多维测量的多变量分析报告,重点介绍了最近5年的工作。重要的趋势包括探针阵列的发展,它为感兴趣的分析物提供指纹响应,并促进复杂样品的分析;神经网络与深度学习在光致发光图像模式识别与特征选择中的应用以及多向多元分析在挖掘矩阵、三向阵列和高阶测量(包括高光谱强度和寿命图像)中的应用。这些示例说明了多维度量和多变量分析相结合所提供的信息提取的增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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