Hyperspectral Imaging Approach Research Articles

Evaluation of a hand‐held spectrophotometer as an in‐field phenotyping tool for tomato and pepper fruit quality

AbstractPhenotyping for vegetable fruit quality traits can involve laborious postharvest and biochemical assays, decreasing efficiency of data collection. Portable devices that are easy to use and withstand in‐field conditions to non‐destructively and accurately quantify internal fruit quality traits would greatly enhance efficiency in breeding programs. We evaluated a hand‐held quality spectrophotometer, the Felix‐750, as an in‐field tomato (Solanum lycopersicum L.) and pepper (Capsicum annuum) high‐throughput phenotyping tool. Fruit quality traits included pH, soluble solids, carotenoids, and shrink in germplasm grown in replicated split‐plot field trials. Germplasm included elite inbred cultivars and introgression lines of tomato, and diverse hybrid and open‐pollinated cultivars of pepper. Our study employed a multi‐faceted approach to evaluate the use of the Felix‐750 in a plant breeding program. Our approach included chemometrics and trait‐based partial least squares regression modeling, and examination of patterns in the λ‐specific spectroscopy data based on variables relevant to genetic, fruit, and environmental factors using principal component analysis and biplots. Results of our study revealed: (a) the scope and limitations of the Felix‐750 in fruit quality trait assessment based on the range of predictive power of partial least squares models; (b) insights into the complex relationships of spectroscopy data with genetic diversity, fruit biology and biochemistry, and factors related to environment. Additional research on the Felix‐750 is needed to determine its potential applications at early and later stages of a breeding pipeline. We also suggest researchers explore more advanced chemometric tools and 3‐D fruit hyperspectral imaging approaches.

Deep Learning for Dental Hyperspectral Image Analysis

The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.

Effect of synthetic amorphous silica powder on the cuticle of Tribolium castaneum and Sitophilus oryzae using hyperspectral imaging technique.

Synthetic amorphous silica (SAS) is safe for human consumption. SAS damages insect cuticles. Qualitative measurement of cuticle properties of insects affected by SAS is essential to understand the mode of action and develop new pesticides. A hyperspectral reflectance imaging approach was used to directly indicate the impact of SAS on the insect cuticle. There were significant differences in the LT95 values of hydrophobic and hydrophilic SAS against Tribolium castaneum and Sitophilus oryzae (P < 0.01). T. castaneum was more susceptible to hydrophobic SAS while no difference was found for S. oryzae exposed to both SAS types. In a hyperspectral study, the ventral reflectance of control groups was higher than that of SAS-treated groups in both visible and short-wave near-infrared wavelength ranges. The SAS-treated groups showed much higher dorsal reflectance. The differences in absorption characteristics of cuticular fat and protein may contribute to the varied performance. The effects of both SASs on insect cuticles was significant, as the 100% recognition rate of the back propagation neural network models suggested. Consistent with the assumption that the efficacy was different between the two SAS types, the lowest rates of the model for two treatment groups were 62.2 and 73.3% in the target and output class. The efficacy varied considerably between the two insect species and the two SASs. Hyperspectral image analyzing coupled with back propagation artificial neural network accurately recorded how SAS impacts the insect cuticle via the effective wavelengths. These findings showed that SAS is a promising candidate for new pesticide products. © 2019 Society of Chemical Industry.

Hyperspectral imaging in the spatial frequency domain with a supercontinuum source.

.We introduce a method for quantitative hyperspectral optical imaging in the spatial frequency domain (hs-SFDI) to image tissue absorption () and reduced scattering () parameters over a broad spectral range. The hs-SFDI utilizes principles of spatial scanning of the spectrally dispersed output of a supercontinuum laser that is sinusoidally projected onto the tissue using a digital micromirror device. A scientific complementary metal–oxide–semiconductor camera is used for capturing images that are demodulated and analyzed using SFDI computational models. The hs-SFDI performance is validated using tissue-simulating phantoms over a range of and values. Quantitative hs-SFDI images are obtained from an ex-vivo beef sample to spatially resolve concentrations of oxy-, deoxy-, and met-hemoglobin, as well as water and fat fractions. Our results demonstrate that the hs-SFDI can quantitatively image tissue optical properties with 1000 spectral bins in the 580- to 950-nm range over a wide, scalable field of view. With an average accuracy of 6.7% and 12.3% in and , respectively, compared to conventional methods, hs-SFDI offers a promising approach for quantitative hyperspectral tissue optical imaging.

Use of hyperspectral imaging for cake moisture and hardness prediction

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on-line, real-time, stand-off cake quality monitoring.

Broadband near-infrared hyperspectral single pixel imaging for chemical characterization.

We introduce a compressive sensing based approach for single pixel hyperspectral chemical imaging in a broad spectral range in the near-infrared. Fully integrated MEMS based Fabry-Pérot tunable filter spectrometers and a digital micro-mirror device were employed to achieve spectral and spatial resolution, respectively. The available spectral range from 1500 to 2200 nm covers molecular overtone vibrations enabling chemical identification. Hyperspectral images of different adhesives deposited on a textile were recorded revealing their chemical composition. Furthermore, spectrally resolved near-infrared images with compression rates up to 90% are presented. The approach of single pixel imaging illustrates a promising technology for the infrared spectral range superior to conventionally used costly focal plane arrays.

Hyperspectral imaging microscopy for measurement of localized second messenger signals in single cells.

Ca2+ and cAMP are ubiquitous second messengers known to differentially regulate a variety of cellular functions over a wide range of timescales. Studies from a variety of groups support the hypothesis that these signals can be localized to discrete locations within cells, and that this subcellular localization is a critical component of signaling specificity. However, to date, it has been difficult to track second messenger signals at multiple locations within a single cell. This difficulty is largely due to the inability to measure multiplexed florescence signals in real time. To overcome this limitation, we have utilized both emission scan- and excitation scan-based hyperspectral imaging approaches to track second messenger signals as well as labeled cellular structures and/or proteins in the same cell. We have previously reported that hyperspectral imaging techniques improve the signal-to-noise ratios of both fluorescence and FRET measurements, and are thus well suited for the measurement of localized second messenger signals. Using these approaches, we have measured near plasma membrane and near nuclear membrane cAMP signals, as well as distributed signals within the cytosol, in several cell types including airway smooth muscle, pulmonary endothelial, and HEK-293 cells. We have also measured cAMP and Ca2+ signals near autofluorescent structures that appear to be golgi. Our data demonstrate that hyperspectral imaging approaches provide unique insight into the spatial and kinetic distributions of cAMP and Ca2+ signals in single cells.

Aerial and Ground Based Sensing of Tolerance to Beet Cyst Nematode in Sugar Beet

The rapid development of image-based phenotyping methods based on ground-operating devices or unmanned aerial vehicles (UAV) has increased our ability to evaluate traits of interest for crop breeding in the field. A field site infested with beet cyst nematode (BCN) and planted with four nematode susceptible cultivars and five tolerant cultivars was investigated at different times during the growing season. We compared the ability of spectral, hyperspectral, canopy height- and temperature information derived from handheld and UAV-borne sensors to discriminate susceptible and tolerant cultivars and to predict the final sugar beet yield. Spectral indices (SIs) related to chlorophyll, nitrogen or water allowed differentiating nematode susceptible and tolerant cultivars (cultivar type) from the same genetic background (breeder). Discrimination between the cultivar types was easier at advanced stages when the nematode pressure was stronger and the plants and canopies further developed. The canopy height (CH) allowed differentiating cultivar type as well but was much more efficient from the UAV compared to manual field assessment. Canopy temperatures also allowed ranking cultivars according to their nematode tolerance level. Combinations of SIs in multivariate analysis and decision trees improved differentiation of cultivar type and classification of genetic background. Thereby, SIs and canopy temperature proved to be suitable proxies for sugar yield prediction. The spectral information derived from handheld and the UAV-borne sensor did not match perfectly, but both analysis procedures allowed for discrimination between susceptible and tolerant cultivars. This was possible due to successful detection of traits related to BCN tolerance like chlorophyll, nitrogen and water content, which were reduced in cultivars with a low tolerance to BCN. The high correlation between SIs and final sugar beet yield makes the UAV hyperspectral imaging approach very suitable to improve farming practice via maps of yield potential or diseases. Moreover, the study shows the high potential of multi- sensor and parameter combinations for plant phenotyping purposes, in particular for data from UAV-borne sensors that allow for standardized and automated high-throughput data extraction procedures.

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning.

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

An unsupervised classification approach for hyperspectral images based adaptive spatial and spectral neighborhood selection and graph clustering

In remote sensing image processing, the classification is an interesting step to distinguish the image scene composition and can be of interesting role in different applications such as environmental monitoring and geological studies. Unlike the clustering, the classification needs labeled data for the training; however gaining these labeled data was always an expensive and hard task. For that, in this paper we propose an unsupervised classification approach that gains its labeled data from the proposed spatial and spectral graph clustering approach. The proposed adaptive spatial and spectral neighborhood selection approach is an extension of the k nearest neighborhood that assigns an adaptive number of neighbors to each pixel depending in its spatial and spectral relationship to its neighboring pixels. Then, this neighborhood will be clustered, to provide the first labeled training set, based on a hierarchical graph clustering algorithm. Finally, an SVM classification with a recursive kernel will be performed on the selected first labeled data at a first step and then the classification results are improved with the classification iterations of the recursive kernel. Experimental results on real hyperspectral images proved that with few iterations of the recursive kernel the proposed approach results are similar and even better then the supervised classification

Quantitative coating thickness determination using a coefficient-independent hyperspectral scattering model

BackgroundHyperspectral imaging is a technique that enables the mapping of spectral signatures across a surface. It is most commonly used for surface chemical mapping in fields as diverse as satellite remote sensing, biomedical imaging and heritage science. Existing models, such as the Kubelka-Munk theory and the Lambert-Beer law also relate layer thickness with absorption, and in the case of the Kubelka-Munk theory scattering, however they are not able to fully describe the complex behavior of the light-layer interaction.MethodsThis paper describes a new approach for hyperspectral imaging, the mapping of coating surface thickness using a coefficient-independent scattering model. The approach taken in this paper is to model the absorption and scattering behavior using a developed coefficient-independent model, calibrated using reference sample thickness measurements performed with optical coherence tomography.ResultsThe results show that this new model, by considering the spectral variation that can be recorded by the hyperspectral imaging camera, is able to measure coatings of 250 μm thickness with an accuracy of 11 μm in a fast and repeatable way.ConclusionsThe new coefficient-independent scattering model presented can successfully measure the thickness of coatings from hyperspectral imaging data.

A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

White matter hyperintensities (WMHs) are closely related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders of elderly people, and can be generally detected on T2 weighted (T2W) or fluid attenuation inversion recovery (FLAIR) brain magnetic resonance (MR) images. This paper develops a new approach to detect WMH in MR brain images from a hyperspectral imaging perspective. To take advantage of hyperspectral imaging, a nonlinear band expansion (NBE) process is proposed to expand MR images to a hyperspectral image. It then redesigns the well-known hyperspectral subpixel target detection, called constrained energy minimization (CEM), as an iterative version of CEM (ICEM) for WMH detection. Its idea is to implement CEM iteratively by feeding back Gaussian filtered CEM-detection maps to capture spatial information. To show effectiveness of NBE-ICEM in WMH detection, the lesion segmentation tool (LST), which is an open source toolbox for statistical parametric mapping (SPM), is used for comparative study. For quantitative analysis, the synthetic images in BrainWeb provided by McGill University are used for experiments where our proposed NBE-ICEM performs better than LST in all cases, especially for noisy MR images. As for real images collected by Taichung Veterans General Hospital, the NBE-ICEM also shows its advantages over and superiority to LST.

A dual-kernel spectral-spatial classification approach for hyperspectral images based on Mahalanobis distance metric learning

Hyperspectral images provide a precise representation of the earth’s surface, with abundant spectral and spatial features, but normal classification algorithms use only the information provided by the spectral features of each data point. In this paper, we propose a new approach to hyperspectral image classification based on Mahalanobis distance metric learning and kernel learning that considers both the features of the spectral bands and a spatial prior. This approach consists of two components. First, we obtain a primary labeled classification result and a posterior probability distribution for each pixel point using a Mahalanobis-kernel-based classifier. Second, instead of the original or extracted spectral features, we reconstruct the spatial relationship of the hyperspectral images using the posterior probability of every data point, smooth the boundaries, and revise suspicious points based on this piecewise information using a kernel-based multi-region segmentation method.In an experimental study, we adopt a support vector machine (SVM) classifier as the kernel classifier to obtain the posterior probabilities using dimensionally reduced data. The proposed method is compared with several other methods from various perspectives. Simulation experiments run on several real hyperspectral data sets are reported. The results show that the proposed method performs better than other comparable classification algorithms, especially in a condition-constrained environment.

Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing

In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writing the unmixing problem as the maximization of the correntropy criterion subject to the most commonly used constraints. Two unmixing problems are derived: the first problem considers the fully-constrained unmixing, with both the non-negativity and sum-to-one constraints, while the second one deals with the non-negativity and the sparsity-promoting of the abundances. The corresponding optimization problems are solved efficiently using an alternating direction method of multipliers (ADMM) approach. Experiments on synthetic and real hyperspectral images validate the performance of the proposed algorithms for different scenarios, demonstrating that the correntropy-based unmixing is robust to outlier bands.

Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients.

.A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube , consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of , sensitivity of , and specificity of . For tissue specimens from the thyroid, the method achieved an average accuracy of , sensitivity of , and specificity of . HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.

Discrimination of Absence or Presence of Pesticide Residue in Mulberry Leaf Using VIS-NIR Hyperspectral Imaging and Plsda

Fast and accurate discrimination of pesticide residue in mulberry leaf is very important for sericulture and silk textile industry. Therefore, a hyperspectral imaging approach with the spectral range of 390-1050 nm was used for the discrimination of pesticide residue in mulberry leaf. 120 mulberry leaves samples including 60 samples without pesticide residue and 60 samples with pesticide residue were imaged by the VIS-NIR hyperspectral imaging system. ENVI software was used to explore the region of interest and extract the corresponding spectral data. Partial least square discriminant analysis (PLSDA) was used to establish the discriminative model for the discrimination of pesticide residue in mulberry leaf and the model achieved 98.33% calibration accuracy and 93.33% prediction accuracy. A total of 9 important wavelengths were selected according to the regression coefficient in the PLSDA model. A simplified PLSDA model was developed based on the important wavelengths and it achieved similar results (96.67% and 93.33%). The results showed that the model based on the selected important wavelengths was comparable to the model based on the full wavelengths, and it was feasible to use hyperspectral imaging technology for discrimination of pesticide residue in mulberry leaf.

Multi-Feature Classification Approach for High Spatial Resolution Hyperspectral Images

High spatial resolution hyperspectral images not only contain abundant radiant and spectral information, but also display rich spatial information. In this paper, we propose a multi-feature high spatial resolution hyperspectral image classification approach based on the combination of spectral information and spatial information. Three features are derived from the original high spatial resolution hyperspectral image: the spectral features that are acquired from the auto subspace partition technique and the band index technique; the texture features that are obtained from GLCM analysis of the first principal component after principal component analysis is performed on the original image; and the spatial autocorrelation features that contain spatial band X and spatial band Y, with the grey level of spatial band X changing along columns and the grey level of spatial band Y changing along rows. The three features are subsequently combined together in Support Vector Machine to classify the high spatial resolution hyperspectral image. The experiments with a high spatial resolution hyperspectral image prove that the proposed multi-feature classification approach significantly increases classification accuracies.

5D imaging approaches reveal the formation of distinct intracellular cAMP spatial gradients.

Cyclic AMP (cAMP) is a ubiquitous second messenger known to differentially regulate many cellular functions. Several lines of evidence suggest that the distribution of cAMP within cells is not uniform. However, to date, no studies have measured the kinetics of 3D cAMP distributions within cells. This is largely due to the low signal-to-noise ratio of FRET-based probes. We previously reported that hyperspectral imaging improves the signal-to-noise ratio of FRET measurements. Here we utilized hyperspectral imaging approaches to measure FRET signals in five dimensions (5D) - three spatial (x, y, z), wavelength (λ), and time (t) - allowing us to visualize cAMP gradients in pulmonary endothelial cells. cAMP levels were measured using a FRET-based sensor (H188) comprised of a cAMP binding domain sandwiched between FRET donor and acceptor - Turquoise and Venus fluorescent proteins. We observed cAMP gradients in response to 0.1 or 1 μM isoproterenol, 0.1 or 1 μM PGE1, or 50 μM forskolin. Forskolin- and isoproterenol-induced cAMP gradients formed from the apical (high cAMP) to basolateral (low cAMP) face of cells. In contrast, PGE1-induced cAMP gradients originated from both the basolateral and apical faces of cells. Data suggest that 2D (x,y) studies of cAMP compartmentalization may lead to erroneous conclusions about the existence of cAMP gradients, and that 3D (x,y,z) studies are required to assess mechanisms of signaling specificity. Results demonstrate that 5D imaging technologies are powerful tools for measuring biochemical processes in discrete subcellular domains. This work was supported by NIH P01HL066299, R01HL058506, S10RR027535, AHA 16PRE27130004 and the Abraham Mitchell Cancer Research Fund.

5 Dimensional Hyperspectral FRET Imaging and Analysis Approaches for Measuring cAMP Gradients

cAMP is a second messenger that regulates numerous cellular and physiological functions. A growing body of evidence suggests that cAMP signals are localized resulting in the formation of spatial cAMP gradients. Recently, Forster Resonance energy transfer (FRET) imaging has become a standard method for assessing changes in cAMP concentration. However, many FRET probes have low signal-to-noise ratios and limited dynamic range, making measurements of cAMP difficult. Our previous work suggested that hyperspectral imaging and analysis allows quantitative measurement of FRET efficiencies in 3D (x, y, and t) within single cells. However, traditional (x, y, and t) approaches of measuring FRET often lead to misinterpretation of data. Here, we present data demonstrating that hyperspectral imaging approaches can be used for 5D (x, y, z, λ and t) assessment of FRET efficiency in living cells allowing the study of spatially-localized cAMP signals. We utilized H188-2 (cAMP reporter) and AKAR4 (PKA activity reporter) FRET biosensors that were transfected into PMVECs and HEK 293 cells, respectively. Time lapse hyperspectral Z-stack images were acquired using a Nikon A1R confocal microscope. We used custom MATLAB scripts to calculate FRET efficiencies from image data. These approaches allowed the measurement of spatial cAMP gradients and PKA activity induced by agonist treatment. We found that cAMP gradients developed from the apical to the basolateral side of PMVECs when treated with isoproterenol, while gradients developed from both the basolateral and apical side when treated with PGE1. In addition, we observed an increase in localized phosphorylation upon treatment with isoproterenol and PGE1 in HEK293 cells. Thus, these studies demonstrate the feasibility for simultaneously studying cAMP gradients and PKA activity in cells.This work was supported by AHA 16PRE27130004, NIH P01HL066299, NIH S10RR027535, and the Abraham Mitchell Cancer Research Fund.

Hyperspectral Measurements Allow Separation of FRET Signals from Non-Uniform Background Fluorescence

In recent years Forster resonance energy transfer (FRET) has become a standard imaging approach to gain insight into localized biochemical processes within cells. These processes include changes in protein colocalization, second messenger concentration, and activation of effector proteins such as protein kinase A (PKA). However, there are several limitations in FRET measurements that are not often considered. In recent years our group has used hyperspectral imaging approaches to address a subset of these limitations, including the inherently low signal-to-noise ratio of standard two and three filter set FRET measurements. Here we present data demonstrating that hyperspectral imaging approaches can be used to correct for non-uniform background fluorescence in low intensity FRET measurements. We utilized the FRET-based cAMP probe H188. The H188 probe contains a cAMP binding domain between donor (Turquoise) and acceptor (Venus) fluorescent proteins. The probe was transfected into pulmonary microvascular endothelial cells plated on glass coverslips. Hyperspectral image stacks were acquired using a Nikon A1R inverted confocal microscope. Changes in cAMP were triggered by addition of 0.1 µM isoproterenol (a beta adrenergic receptor agonist) 0.1 µM PGE1 (a prostanoid receptor agonist) or 50 µM forskolin (an adenylyl cyclase activator). Data were analyzed using Nikon Elements software and custom MATLAB scripts. Results demonstrate that non-uniform background fluorescence associated with glass coverslips can contaminate FRET measurements from weak fluorescence signals. Linear unmixing approaches were able to separate the abundances of background, donor and acceptor fluorescence signals. Thus, results from this study demonstrate that hyperspectral unmixing approaches can readily separate non-uniform background fluorescence from other fluorescence signatures, allowing for more accurate quantification of FRET efficiency. This work was supported by NIH P01HL066299, NIH S10RR027535, NIH T32HL076125, AHA 16PRE27130004, USA SURF Program, and the Abraham Mitchell Cancer Research Fund.