Hyperspectral Luminescence Imaging in Combination with Signal Deconvolution Enables Reliable Multi-Indicator-Based Chemical Sensing

Abstract

Although real-time monitoring of individual analytes using reversible optical chemical sensors (optodes) is well established, it remains a challenge in optical sensing to monitor multiple analyte concentrations simultaneously. Here, we present a novel sensing approach using hyperspectral imaging in combination with signal deconvolution of overlapping emission spectra of multiple luminescent indicator dyes, which facilitates multi-indicator-based chemical imaging. The deconvolution algorithm uses a linear combination model to describe the superimposed sensor signals and employs a sequential least-squares fit to determine the percent contribution of the individual indicator dyes to the total measured signal. As a proof of concept, we used the algorithm to analyze the measured response of an O₂ sensor composed of red-emitting Pd(II)/Pt(II) porphyrins and NIR-emitting Pd(II)/Pt(II) benzoporphyrins with different sensitivities. This facilitated chemical imaging of O₂ over a wide dynamic range (0-950 hPa) with a hyperspectral camera system (470-900 nm). The applicability of the novel method was demonstrated by imaging the O₂ distribution in the heterogeneous microenvironment around the roots of the aquatic plant <i>Littorella uniflora</i>. The presented approach of combining hyperspectral sensing with signal deconvolution is flexible and can easily be adapted for use of various multi-indicator- or even multianalyte-based optical sensors with different spectral characteristics, enabling high-resolution simultaneous imaging of multiple analytes.