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Updated: Sep 22, 2025
Conducting Multiple Imaging Modes with One Fluorescence Microscope
Published on: October 28, 2018
A tutorial on multi-way data processing of excitation-emission fluorescence matrices acquired from semiconductor
Sarmento J Mazivila1, José X Soares1, João L M Santos1
1The Associated Laboratory for Green Chemistry (LAQV) of the Network of Chemistry and Technology (REQUIMTE) - the Portuguese Research Centre for Sustainable Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, 4050-313, Porto, Portugal.
This tutorial shows how to leverage the second-order advantage in excitation-emission fluorescence matrices (EEFMs) from quantum dot (QD) sensors for accurate quantification. It details chemometric models like PARAFAC, MCR-ALS, and U-PLS/RBL for complex samples with interferents.
Area of Science:
- Analytical Chemistry
- Chemometrics
- Spectroscopy
Background:
- Semiconductor quantum dots (QDs) offer sensitive fluorescence detection.
- Excitation-emission fluorescence matrices (EEFMs) capture complex spectral data.
- Quantification in complex samples is challenging due to overlapping signals and interferents.
Purpose of the Study:
- To demonstrate exploiting the second-order advantage in EEFMs for improved quantification.
- To provide a tutorial on applying advanced chemometric models to QD-based sensing data.
- To address various data structures arising from different analytical scenarios.
Main Methods:
- Application of Parallel Factor Analysis (PARAFAC) for trilinear data structures.
- Utilization of Multivariate Curve Resolution-Alternating Least-Squares (MCR-ALS) for non-trilinear data with single breaking modes.
- Implementation of Unfolded Partial Least-Squares with Residual Bilinearization (U-PLS/RBL) for non-trilinear data with multiple breaking modes (e.g., Inner Filter Effect).
Main Results:
- Successful quantification is achieved by matching chemometric models to specific data structures.
- The tutorial illustrates the practical application of PARAFAC, MCR-ALS, and U-PLS/RBL on real datasets.
- The U-PLS/RBL method is detailed for handling challenging data with Inner Filter Effects.
Conclusions:
- Selecting the appropriate chemometric model is crucial for exploiting the second-order advantage.
- Advanced multi-way fluorescence data processing enables accurate quantification even with significant interferents.
- This work provides a comprehensive guide for researchers using QD-based sensing platforms and chemometrics.
