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

Direct detection of phycocyanin in sediments by hyperspectral imaging

Cyanobacteria are ubiquitous aquatic organisms with a remarkable evolutionary history reaching as far as 1.9 Ga. They play a vital role in ecosystems yet also raise concerns due to their association with harmful algal blooms. Understanding the historical patterns and drivers behind these blooms is crucial for effective ecosystem management. Lake-sediment cores are valuable natural environmental archives, recording the histories of such blooms. Among others, phycocyanin, a pigment specific to cyanobacteria, emerges as a promising biomarker for reconstructing past cyanobacterial bloom events. However, due to the physicochemical properties of phycocyanin, there is no validated method available to extract and measure this pigment from complex sediment matrix. This study explores the applicability of hyperspectral imaging (HSI), a non-destructive technique, as a novel approach for high resolution in-situ detection and quantification of phycocyanin in lake sediments. Our experiments show that phycocyanin can be detected by HSI with an absorption trough at 620 nm (relative absorption band depth, RABD620). We established a semi-quantitative calibration of the spectral index RABD620 by conducting spiking experiments with phycocyanin standard (known phycocyanin mass) on organic-rich and mineral-rich sediments of varying water contents. We also assessed potential interference from chlorophyll a, another photosynthetic pigment, ensuring the reliability of hyperspectral phycocyanin measurements. Our findings demonstrate a significant correlation (R2 ranging from 0.37 to 0.997) between the RABD620 index and associated phycocyanin amounts in organic-rich and minerogenic sediments. This indicates the potential of the spectral index to directly measure in-situ biomarker concentrations on split sediment cores. Although confounding factors such as water and chlorophyll a content can influence the spectral signal, this method offers a rapid and non-destructive approach for studying historical cyanobacterial blooms in sedimentary records. This opens promising grounds for various applications, including ecosystem-health assessment and environmental change monitoring.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10933-024-00350-y.

Simultaneous Imaging of Temperature and Oxygen by Utilizing Thermally Activated Delayed Fluorescence and Phosphorescence of a Single Indicator

Chemical gradients are essential in biological systems, affecting processes like microbial activity in soils and nutrient cycling. Traditional tools, such as microsensors, offer high-resolution data but are limited to one-dimensional measurements. Planar optodes allow for two-dimensional (2D) and three-dimensional (3D) chemical imaging but are often sensitive to temperature changes. This study presents an advanced dual-emission optical sensor that simultaneously measures temperature and oxygen using a modified platinum(II) meso-tetrakis(3,5-ditert-butylphenyl)-tetra(2-tert-butyl-1,4-naphthoquinono)porphyrin. The ratio between thermally activated delayed fluorescence and phosphorescence was optimized by modifying platinum(II) naphthoquinonoporphyrin with tert-butyl groups which simultaneously improved solubility in apolar solvents and polymer matrix (polystyrene). This dual-function sensor enables two-parameter chemical imaging with a consumer-grade RGB camera or a hyperspectral camera. We demonstrated 2D visualization of temperature and oxygen distribution in a model soil system. The RGB camera provided rapid and cost-effective imaging, while the hyperspectral camera offered more detailed spectral information despite some limitations. Our findings revealed the formation of a stable temperature gradient and oxygen depletion, driven by water content and temperature-sensitive microbial activity. This dual O2/T sensor, with further potential improvements, shows considerable promise for advanced multiparameter sensing in complex biological and environmental studies, providing deeper insights into dynamic microenvironments.

Multiparameter Sensing of Oxygen and pH at Biological Interfaces via Hyperspectral Imaging of Luminescent Sensor Nanoparticles

Chemical dynamics in biological samples are seldom stand-alone processes but represent the outcome of complicated cascades of interlinked reaction chains. In order to understand these processes and how they correlate, it is important to monitor several parameters simultaneously at high spatial and temporal resolution. Hyperspectral imaging is a promising tool for this, as it provides broad-range spectral information in each pixel, enabling the use of multiple luminescent indicator dyes, while simultaneously providing information on sample structures and optical properties. In this study, we first characterized pH- and O2-sensitive indicator dyes incorporated in different polymer matrices as optical sensor nanoparticles to provide a library for (hyperspectral) chemical imaging. We then demonstrate the successful combination of a pH-sensitive indicator dye (HPTS(DHA)3), an O2-sensitive indicator dye (PtTPTBPF), and two reference dyes (perylene and TFPP), incorporated in polymer nanoparticles for multiparameter chemical imaging of complex natural samples such as green algal biofilms (Chlorella sorokiniana) and seagrass leaves (Zostera marina) with high background fluorescence. We discuss the system-specific challenges and limitations of our approach and further optimization possibilities. Our study illustrates how multiparameter chemical imaging with hyperspectral read-out can now be applied on natural samples, enabling the alignment of several chemical parameters to sample structures.

Triphenylamine-Based Polythioacetal for Selective Sensing of Mercury(II) with High Specificity and Sensitivity

Utilizing the mercury (Hg2+)-triggered deprotection of thioacetals to aldehyde groups, we constructed a water-soluble triphenylamine (TPA)-based polythioacetal PTA-TPA with thioacetal groups in the backbones for efficient sensing of Hg2+ in aqueous solutions. PTA-TPA is conveniently prepared by polycondensation of 3, 6-dioxa-1,8-octanedithiol (DODT) with 4-(N,N-diphenylamino) benzaldehyde (TPA-CHO) using thiol-terminated mPEG2k-SH as a capping agent. The interaction of Hg2+ with PTA-TPA activates the aggregation-induced emission (AIE) process of TPA-CHO molecules, which makes the emission enhanced, and the emission color changes to sky blue, while other metal ions do not interfere with the sensing process. PTA-TPA can be used as a highly selective and ultrafast detection system for Hg2+ with a low detection limit (LOD) of 9.88 nM and a fast response of less than 1 min. In addition, the prepared test strips report the presence of Hg2+ with an LOD as low as 1 × 10-5 M. Intracellular imaging applications have demonstrated that PTA-TPA acts as a biocompatible fluorescent probe for efficient Hg2+ sensing in HeLa cells. Overall, the PTA-TPA fluorescence probes have the characteristics of easy synthesis, cost-effective, ultrafast detection speed, high selectivity, and high sensitivity, which can be used in practical applications.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Chemical gradients and uneven distribution of analytes are common in natural and artificial systems. As a result, the ability to visualize chemical distributions in two or more dimensions has gained significant importance in recent years. This has led to the integration of chemical imaging techniques into all domains of analytical chemistry. In this review, we focus on the use of optical sensors, so-called optodes, to obtain real-time and multidimensional images of two or more parameters simultaneously. It is important to emphasize that multiparameter imaging in this context is not confined solely to multiple chemical parameters (analytes) but also encompasses physical (e.g., temperature or flow) or biological (e.g., metabolic activity) parameters. First, we discuss the technological milestones that have paved the way for chemical imaging using optodes. Later, we delve into various strategies that can be taken to enable multiparameter imaging. The latter spans from developing novel receptors that enable the recognition of multiple parameters to chemometrics and machine learning-based techniques for data analysis. We also explore ongoing trends, challenges, and prospects for future developments in this field. Optode-based multiparameter imaging is a rapidly expanding field that is being fueled by cutting-edge technologies. Chemical imaging possesses the potential to provide novel insights into complex samples, bridging not only across various scientific disciplines but also between research and society.

Machine learning for optical chemical multi-analyte imaging : Why we should dare and why it's not without risks

Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10-2 and < 1.96·10-1, respectively, and a root mean square error of < 2.12·10-1 and < 4.42·10-1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

On the Use of Polymer-Based Composites for the Creation of Optical Sensors: A Review

Polymers are widely used in many areas, but often their individual properties are not sufficient for use in certain applications. One of the solutions is the creation of polymer-based composites and nanocomposites. In such materials, in order to improve their properties, nanoscale particles (at least in one dimension) are dispersed in the polymer matrix. These properties include increased mechanical strength and durability, the ability to create a developed inner surface, adjustable thermal and electrical conductivity, and many others. The materials created can have a wide range of applications, such as biomimetic materials and technologies, smart materials, renewable energy sources, packaging, etc. This article reviews the usage of composites as a matrix for the optical sensors and biosensors. It highlights several methods that have been used to enhance performance and properties by optimizing the filler. It shows the main methods of combining indicator dyes with the material of the sensor matrix. Furthermore, the role of co-fillers or a hybrid filler in a polymer composite system is discussed, revealing the great potential and prospect of such matrixes in the field of fine properties tuning for advanced applications.

Noise versus Resolution in Optical Chemical Imaging-How Reliable Are Our Measurements?

Optical chemical imaging has established itself as a valuable technique for visualizing analyte distributions in 2D, notably in medical, biological, and environmental applications. In particular for image acquisitions on small scales between few millimeter to the micrometer range, as well as in heterogeneous samples with steep analyte gradients, image resolution is essential. When individual pixels are inspected, however, image noise becomes a metric as relevant as image accuracy and precision, and denoising filters are applied to preserve relevant information. While denoising filters smooth the image noise, they can also lead to a loss of spatial resolution and thus to a loss of relevant information about analyte distributions. To investigate the trade-off between image resolution and noise reduction for information preservation, we studied the impact of random camera noise and noise due to incorrect camera settings on oxygen optodes using the ratiometric imaging technique. First, we estimated the noise amplification across the calibration process using a Monte Carlo simulation for nonlinear fit models. We demonstrated how initially marginal random camera noise results in a significant standard deviation (SD) for oxygen concentration of up to 2.73% air under anoxic conditions, although the measurement was conducted under ideal conditions and over 270 thousand sample pixels were considered during calibration. Second, we studied the effect of the Gaussian denoising filter on a steep oxygen gradient and investigated the impact when the smoothing filter is applied during data processing. Finally, we demonstrated the effectiveness of a Savitzky-Golay filter compared to the well-established Gaussian filter.

Radial Oxygen Loss from Plant Roots—Methods

In flooded soils, an efficient internal aeration system is essential for root growth and plant survival. Roots of many wetland species form barriers to restrict radial O2 loss (ROL) to the rhizosphere. The formation of such barriers greatly enhances longitudinal O2 diffusion from basal parts towards the root tip, and the barrier also impedes the entry of phytotoxic compounds produced in flooded soils into the root. Nevertheless, ROL from roots is an important source of O2 for rhizosphere oxygenation and the oxidation of toxic compounds. In this paper, we review the methodological aspects for the most widely used techniques for the qualitative visualization and quantitative determination of ROL from roots. Detailed methodological approaches, practical set-ups and examples of ROL from roots with or without barriers to ROL are included. This paper provides practical knowledge relevant to several disciplines, including plant–soil interactions, biogeochemistry and eco-physiological aspects of roots and soil biota.

A Ratiometric Optical Dual Sensor for the Simultaneous Detection of Oxygen and Carbon Dioxide

Simultaneous detection of carbon dioxide (CO₂) and oxygen (O₂) has attracted considerable interest since CO₂ and O₂ play key roles in various industrial and domestic applications. In this study, a new approach based on a fluorescence ratiometric referencing method was reported to develop an optical dual sensor where platinum (II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) complex used as the O₂-sensitive dye, CdSe/ZnS quantum dots (QDs) combined with phenol red used as the CO₂-sensitive dye, and CdSe/ZnS QDs used as the reference dye for the simultaneous detection of O₂ and CO₂. All the dyes were immobilized in a gas-permeable matrix poly (isobutyl methacrylate) (PolyIBM) and subjected to excitation using a 380 nm LED. The as-obtained distinct fluorescence spectral intensities were alternately exposed to analyte gases to observe changes in the fluorescence intensity. In the presence of O₂, the fluorescence intensity of the Pt (II) complex was considerably quenched, while in the presence of CO₂, the fluorescence intensity of QDs was increased. The corresponding ratiometric sensitivities of the optical dual sensor for O₂ and CO₂ were approximately 13 and 144, respectively. In addition, the response and recovery for O₂ and CO₂ were calculated to be 10 s/35 s and 20 s/60 s, respectively. Thus, a ratiometric optical dual gas sensor for the simultaneous detection of O₂ and CO₂ was successfully developed. Effects of spurious fluctuations in the intensity of external and excitation sources were suppressed by the ratiometric sensing approach.

Optode Based Chemical Imaging-Possibilities, Challenges, and New Avenues in Multidimensional Optical Sensing

Seeing is believing, as the saying goes, and optical sensors (so-called optodes) are tools that can make chemistry visible. Optodes react reversibly and quickly (seconds to minutes) to changing analyte concentrations, enabling the spatial and temporal visualization of an analyte in complex environments. By being available as planar sensor foils or in the form of nano- or microparticles, optodes are flexible tools suitable for a wide array of applications. The steadily grown applications of in particular oxygen (O₂) and pH optodes in fields as diverse as medical, environmental, or material sciences is proof for the large demand of optode based chemical imaging. Nevertheless, the full potential of this technology is not exhausted yet, challenges have to be overcome, and new avenues wait to be taken. Within this Perspective, we look at where the field currently stands, highlight several successful examples of optode based chemical imaging and ask what it will take to advance current state-of-the-art technology. It is our intention to point toward some potential blind spots and to inspire further developments.