Photoluminescence Probes in Data-Enabled Sensing

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemometrics

Background:

• Photoluminescent probes offer sensitive detection but often yield complex data.
• Multidimensional photoluminescence detection generates rich datasets.
• Multivariate data analysis (MVA) is crucial for extracting meaningful information from complex spectral data.

Purpose of the Study:

• To review the development of photoluminescent probes, multidimensional detection, and MVA methods.
• To highlight recent advancements (2015-2022) in MVA of multidimensional photoluminescence measurements.
• To identify key trends and future directions in the field.

Main Methods:

• Literature review focusing on publications from June 2015 to June 2022.
• Analysis of trends in probe array development.
• Examination of machine learning (neural networks, deep learning) applications.
• Review of multiway MVA applied to higher-order spectral and lifetime data.

Main Results:

• Probe arrays enable fingerprint-like responses for complex sample analysis.
• Neural networks and deep learning improve pattern recognition in photoluminescence images.
• Multiway MVA effectively analyzes mining matrices and higher-order data, including hyperspectral images.
• Combining multidimensional measurements with MVA significantly increases information extraction.

Conclusions:

• The integration of multidimensional photoluminescence measurements and MVA is a powerful approach for enhanced analytical performance.
• Emerging trends like probe arrays and advanced computational methods are driving innovation in chemical sensing.
• Future research will likely focus on further developing these integrated strategies for increasingly complex analytical challenges.