High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams

Nasse, Michael; Walsh, Michael J.; Mattson, Eric C.; Reininger, R.; Kajdacsy-Balla, André; Macias, Virgilia; Bhargava, Rohit; Hirschmugl, Carol J.

doi:10.1038/nmeth.1585

Cited by 320 publications

(319 citation statements)

References 36 publications

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“…The deconvolution algorithm was tested on several different test samples (tissue, polystyrene beads). These tests confirmed that the spatially deconvolved spectra preserved the original spectral features [102]. In cases where the PSF is only poorly determined, or entirely unknown deconvolution can be performed also using an estimate of the point spread function.…”

Section: Deconvolution In Vibrational Hyperspectral Imagingsupporting

confidence: 53%

“…recently presented a de-blurring method for FT-IR hyperspectral imaging matrices in which deconvolution was carried out by the help of the instrument-specific PSF [102]. The suggested deconvolution technique relied on the knowledge of the wavelength-dependent PSF: this function was determined experimentally by MIR transmittance measurements of a 2 µm pinhole [102]. To avoid the introduction of additional noise, the pinhole data were fitted using the diffraction pattern of a Schwarzschild objective as the fit model [102,103].…”

Section: Deconvolution In Vibrational Hyperspectral Imagingmentioning

confidence: 99%

“…The suggested deconvolution technique relied on the knowledge of the wavelength-dependent PSF: this function was determined experimentally by MIR transmittance measurements of a 2 µm pinhole [102]. To avoid the introduction of additional noise, the pinhole data were fitted using the diffraction pattern of a Schwarzschild objective as the fit model [102,103]. Deconvolution first involved a series of 2D Fourier transforms (FT) of both, experimental image data and the PSF fits.…”

Section: Deconvolution In Vibrational Hyperspectral Imagingmentioning

confidence: 99%

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Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

286

203

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IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient data and has generated an urgent need for new and automated methods of spectral data analysis. Spectral data pre-processing is an important first step in the workflow of IR and Raman spectra analysis which involves specific processing procedures performed on the raw data. Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy. The main goals of data pre-processing can be summarized as follows: (i) Improvement of the robustness and accuracy of subsequent quantitative or classification analyses (ii) Improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines (iii) Detection and removal of outliers and trends (iv) Reduction of the dimensionality of the data mining task. Removal of irrelevant and redundant information by feature selection. For systematic reasons it is useful to subdivide data pre-processing procedures into at least eight different categories. These groups can be used to perform the following operations: 1. Exclusion (cleaning) -this class of data pre-processing methods is used to detect and eliminate spectral outliers. Tests for spectral quality are important examples and aim at the detection of outlier spectra prior to further data mining tasks. Removal of outlier spectra can be achieved by labeling them as NaNs (Not a Number), for example as "bad" pixel spectra in hyperspectral imaging applications. Another way of outlier removal in hyperspectral imaging applications is the interpolation of bad pixel spectra by using spectral data from neighboring locations. 2. Normalization -normalization is used to scale the spectra within a similar range. In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3. Filtering -in frequency analyses filtering is known as a group of methods that removes unwanted frequencies from the signals to be analyzed. Examples for...

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Section: Deconvolution In Vibrational Hyperspectral Imagingsupporting

confidence: 53%

Section: Deconvolution In Vibrational Hyperspectral Imagingmentioning

confidence: 99%

Section: Deconvolution In Vibrational Hyperspectral Imagingmentioning

confidence: 99%

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Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

286

203

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“…[17][18][19][20][21][22][23][24] However, spatial resolution in transmission mode is lower than that of micro-ATR mode even when measured using a powerful synchrotron source. [25][26] Although a recent report 27 has shown that, by using a multi-beam synchrotron source and a high magnifying power objective, the spatial resolution in transmission mode can approach that of micro-ATR mode.…”

Section: Introductionmentioning

confidence: 99%

Correcting the Effect of Refraction and Dispersion of Light in FT-IR Spectroscopic Imaging in Transmission through Thick Infrared Windows

Chan

Kazarian

2012

Anal. Chem.

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Transmission mode is one of the most common sampling methods for FTIR spectroscopic imaging because the spectra obtained generally have a reasonable signal to noise ratio. However, dispersion and refraction of infrared light occurs when samples are sandwiched between infrared windows or placed underneath a layer of liquid. Dispersion and refraction cause infrared light to focus with different focal lengths depending on the wavelength (wavenumber) of the light. As a result, images obtained are in focus only at a particular wavenumber while they are defocused at other wavenumber values. In this work, a solution to correct this spread of focus by means of adding a lens on top of the infrared transparent window, such that a pseudo hemisphere is formed, has been investigated. Through this lens (or hemisphere), refraction of light is removed and the light across the spectral range has the same focal depth. Furthermore, the lens acts as a solid immersion objective and an increase of both magnification and spatial resolution (by 1.4 times) is demonstrated. The spatial resolution was investigated using an USAF resolution target, showing that the Rayleigh criterion can be achieved, as well as a sample with a sharp polymer interface to indicate the spatial resolution that can be expected in real samples. The reported approach was used to obtain chemical images of cross-sections of cancer tissue and hair samples sandwiched between infrared windows showing the versatility and applicability of the method. In addition to the improved spatial resolution, the results reported herein also demonstrate that the lens can reduce the effect of scattering near the edges of tissue samples. The advantages of the presented approach, obtaining FTIR spectroscopic images in transmission mode with the same focus across all wavenumber values and simultaneous improvement in spatial resolution, will have wide implications ranging from studies of live cells to sorption of drugs into tissues.

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“…Done correctly, this should reduce the pixel-to-pixel illumination difference dramatically, removing the artifacts across IR images. We will finally be able to exploit the order of magnitude spectral advantage in signalto-noise expected per single FPA pixel at the highest magnification and image oversampling [8,9]. Currently under commissioning, the aim is to take users with this system within the next year.…”

Section: Technical Reportsmentioning

confidence: 99%

Synchrotron-Based Infrared Spectral Imaging at the MIRIAM Beamline of Diamond Light Source

Cinque

Frogley

Wehbe

et al. 2017

Synchrotron Radiation News

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High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams

Cited by 320 publications

References 36 publications

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Correcting the Effect of Refraction and Dispersion of Light in FT-IR Spectroscopic Imaging in Transmission through Thick Infrared Windows

Synchrotron-Based Infrared Spectral Imaging at the MIRIAM Beamline of Diamond Light Source

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