Infrared Spectroscopic Imaging Advances as an Analytical Technology for Biomedical Sciences

Wróbel, Tomasz P.; Bhargava, Rohit

doi:10.1021/acs.analchem.7b05330

Cited by 98 publications

(74 citation statements)

References 267 publications

(448 reference statements)

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“…These are becoming more and more precise and adaptive in nature, however, most of them are focused on photography, medical imaging and satellite imagery 1 . Spectroscopic hyperspectral imaging in this context is relatively new and denoising techniques have not yet been fully optimized for the specific structure and characteristic of IR data 2 – 6 .…”

Section: Introductionmentioning

confidence: 99%

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

Koziol

Raczkowska

Skibińska

et al. 2018

Sci Rep

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The recent emergence of High Definition (HD) FT-IR and Quantum Cascade Laser (QCL) Microscopes elevated the IR imaging field very close to clinical timescales. However, the speed of acquisition and data quality are still the critical factors in reaching the clinic. Denoising offers aide in both aspects if performed properly. However, there is a lack of a direct comparison of the efficiency of denoising techniques in IR imaging in general. To achieve such comparison within a rigorous framework and obtaining the critical information about signal loss, a simulated dataset strongly bound by experimental parameters was created. Using experimental structural and spectral information and experimental noise levels data as an input for the simulation, a direct comparison of spatial (Fourier transform, Mean Filter, Weighted Mean Filter, Gauss Filter, Median Filter, spatial Wavelets and Deep Neural Networks) and spectral (Savitzky-Golay, Fourier transform, Principal Component Analysis, Minimum Noise Fraction and spectral Wavelets) denoising schemes was enabled. All of these techniques were compared on the simulated dataset, taking into account SNR gain, signal distortion and sensitivity to tuning parameters as comparison metrics. Later, the best techniques were applied to experimental data for validation. The results presented here clearly show the benefit of using hyperspectral denoising schemes such as PCA and MNF which outperform other methods.

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Section: Introductionmentioning

confidence: 99%

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

Koziol

Raczkowska

Skibińska

et al. 2018

Sci Rep

Self Cite

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“…Spectroscopic imaging in the extended IR region, beyond the conventional NIR methods used, requires additional considerations for both sample preparation and imaging methodology. Due to the high absorption of water within the SWIR/MWIR region, biological samples are often chemically 'fixed' to remove the unwanted water signature [6], however this process can also remove significant spectral features for accurate classification [7]. Spectroscopic images of these fixed samples are collected using Atomic Force Microscopy (AFM), requiring the sample to be placed in a vacuum, further reducing the availability of imaging within this region in most studies.…”

Section: Introductionmentioning

confidence: 99%

Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy

Mellors¹,

Spear²,

Howle³

et al. 2020

PLoS ONE

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The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500-3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including 'raw' unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features.

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“…In addition, since FTIR is nonionizing, compositional variations can be investigated as time goes by on time scales from a few milliseconds to hours. Infrared (IR) microspectroscopy, as implied in the name, is a combination of vibrational mid-infrared (IR) spectroscopy and microscopy [23,[122][123][124]. It enables chemical composition of materials to be determined at microscopic level [125,126].…”

Section: Infrared (Ir) Microspectroscopy Fourier Transformmentioning

confidence: 99%

Worthwhile Relevance of Infrared Spectroscopy in Characterization of Samples and Concept of Infrared Spectroscopy-Based Synchrotron Radiation

Odularu

2020

Journal of Spectroscopy

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The study explores the nitty-gritty of infrared spectroscopy. Firstly, the review gives a concise history of infrared discovery and its location in the electromagnetic spectrum. Secondly, the infrared spectroscopy is reported for its mechanism, principles, sample preparation, and application for absence and presence of functional groups determination in both ligands and coordination compounds. Thirdly, it helps in purity determination of unknown samples. Additional studies regarding this study entail infrared spectroscopy-based synchrotron radiation. It serves as a giant microscope to give detailed information of samples under investigation compared to the conventional infrared instrument. Infrared will continue to be useful to both chemical and pharmaceutical industries, in order to make chemical products and manufactured drugs put on wholesome integrity.

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Infrared Spectroscopic Imaging Advances as an Analytical Technology for Biomedical Sciences

Cited by 98 publications

References 267 publications

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy

Worthwhile Relevance of Infrared Spectroscopy in Characterization of Samples and Concept of Infrared Spectroscopy-Based Synchrotron Radiation

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