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.

中文翻译：

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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元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 月期间发表的对光致发光探针多维测量进行多变量分析的报告，并强调了过去 5 年的工作。重要趋势包括探针阵列的开发，它可以为感兴趣的分析物提供指纹响应并促进复杂样品的分析；神经网络和深度学习在光致发光图像的模式识别和特征选择中的应用；以及多路多元分析在挖掘矩阵、三路阵列和高阶测量（包括高光谱强度和寿命图像）中的应用。这些例子说明了多维测量和多变量分析相结合所提供的信息提取的增加。