Digital compressive chemical quantitation and hyperspectral imaging

Wilcox, David S.; Buzzard, Gregery T.; Lucier, Bradley J.; Rehrauer, Owen G.; Wang, Ping; Ben‐Amotz, Dor

doi:10.1039/c3an00309d

Cited by 38 publications

(57 citation statements)

References 14 publications

(11 reference statements)

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“…Therefore, higher frame rates can be achieved by exploiting galvo scanners. We note that documented scanning methods of supervised approaches [16,17,20] were based on a single spectral realization per image. Hence, they are not compatible with the matrix completion framework.…”

Section: Discussionmentioning

confidence: 99%

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Fast compressive Raman bio-imaging via matrix completion

Soldevila¹,

Dong²,

Tajahuerce³

et al. 2019

Optica

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Raman microscopy is a powerful method combining non-invasiveness with no special sample preparation. Because of this remarkable simplicity, it has been widely exploited in many fields, ranging from life and materials sciences, to engineering. Notoriously, due to the required imaging speeds for bio-imaging, it has remained a challenge how to use this technique for dynamic and large-scale imaging. Recently, compressive Raman has been put forward, allowing for fast imaging, therefore solving the issue of speed. Yet, due to the need of strong a priori information of the species forming the hyperspectrum, it has remained elusive how to apply this technique for microspectroscopy of (dynamic) biological tissues. Combining an original spectral under-sampling measurement technique with matrix completion framework for reconstruction, we demonstrate fast and inexpensive label-free molecular imaging of biological specimens (brain tissues and single cells). Therefore, our results open interesting perspectives for clinical and cell biology applications using the much faster compressive Raman framework.

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Section: Discussionmentioning

confidence: 99%

“…The supervised method exploits the eigenspectra of H ( Fig. 1.B, rightmost spectra), as a priori information, to develop optimized spectral filters for fast, accurate, and precise chemical abundances determination [15,20]. Nevertheless, this supervised method fails in chemically changing environments, as the "eigenspectra library" may evolve.…”

mentioning

confidence: 99%

Fast compressive Raman bio-imaging via matrix completion

Soldevila¹,

Dong²,

Tajahuerce³

et al. 2019

Optica

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“…1 Ben Amotz et al developed a supervised spectral unmixing method they termed compressive detection. [2][3][4][5][6][7][8] The spectroscopic projection is performed within the instrumentation before the data set is recorded, lowering the dimensionality of the data set, vastly reducing its size, and providing real-time results. Particularly in hyperspectral imaging, where data sets can be enormous, this size reduction is highly advantageous.…”

Section: Introductionmentioning

confidence: 99%

Compressive Detection of Highly Overlapped Spectra Using Walsh–Hadamard-Based Filter Functions

Corcoran

2017

Appl Spectrosc

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In the chemometric context in which spectral loadings of the analytes are already known, spectral filter functions may be constructed which allow the scores of mixtures of analytes to be determined in on-the-fly fashion directly, by applying a compressive detection strategy. Rather than collecting the entire spectrum over the relevant region for the mixture, a filter function may be applied within the spectrometer itself so that only the scores are recorded. Consequently, compressive detection shrinks data sets tremendously. The Walsh functions, the binary basis used in Walsh-Hadamard transform spectroscopy, form a complete orthonormal set well suited to compressive detection. A method for constructing filter functions using binary fourfold linear combinations of Walsh functions is detailed using mathematics borrowed from genetic algorithm work, as a means of optimizing said functions for a specific set of analytes. These filter functions can be constructed to automatically strip the baseline from analysis. Monte Carlo simulations were performed with a mixture of four highly overlapped Raman loadings and with ten excitation-emission matrix loadings; both sets showed a very high degree of spectral overlap. Reasonable estimates of the true scores were obtained in both simulations using noisy data sets, proving the linearity of the method.

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“…Moreover, because the collection of high-dimensional data is often the slowest step in the process, a number of compressive detection strategies [3][4][5][6][7] have been introduced with the goal of increasing data collection speed by making measurements only in the low-dimensional space containing the information of interest. One such method is our previously described optimized binary compressive detection (OB-CD) strategy, in which OB filters are applied to a digital mirror microarray (DMD) to redirect or collect photons of (multiple) selected colors, for detection using a single channel detector, such as a photon counting photomultiplier tube (PMT) or an avalanche photodiode (APD) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Fluorescence modeling for optimized-binary compressive detection Raman spectroscopy

Rehrauer¹,

Mankani²,

Buzzard³

et al. 2015

Opt. Express

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The recently-developed optimized binary compressive detection (OB-CD) strategy has been shown to be capable of using Raman spectral signatures to rapidly classify and quantify liquid samples and to image solid samples. Here we demonstrate that OB-CD can also be used to quantitatively separate Raman and fluorescence features, and thus facilitate Raman-based chemical analyses in the presence of fluorescence background. More specifically, we describe a general strategy for fitting and suppressing fluorescence background using OB-CD filters trained on third-degree Bernstein polynomials. We present results that demonstrate the utility of this strategy by comparing classification and quantitation results obtained from liquids and powdered mixtures, both with and without fluorescence. Our results demonstrate high-speed Raman-based quantitation in the presence of moderate fluorescence. Moreover, we show that this OB-CD based method is effective in suppressing fluorescence of variable shape, as well as fluorescence that changes during the measurement process, as a result of photobleaching.

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Digital compressive chemical quantitation and hyperspectral imaging

Cited by 38 publications

References 14 publications

Fast compressive Raman bio-imaging via matrix completion

Fast compressive Raman bio-imaging via matrix completion

Compressive Detection of Highly Overlapped Spectra Using Walsh–Hadamard-Based Filter Functions

Fluorescence modeling for optimized-binary compressive detection Raman spectroscopy

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