A remote infrared spectroscopy (RIRS) detection system was assembled using a mid infrared (MIR) Fourier
Transform interferometer useful in open-path (OP) mode, a reflective infrared telescope and a cryocooled wide
band, MCT detector. The system was used for passive mode IR thermal emission measurements and was also
coupled to another Newtonian telescope in conjunction with a globar source for active mode measurements. The
operation of the system was validated by measuring RIRS spectra of gases (NH3) and condensable vapors: acetone,
dichloromethane, methyl ether and acetonitrile. Solid samples were measured by smearing small amounts on
aluminum plates after dissolving in appropriate solvents. Highly energetic compounds: TNT, DNT, PETN and RDX
were also detected. Experiments of solids on metal surfaces were carried out in passive and active modes. The
analyzed samples were placed at different standoff distances up to a maximum of 30 m in active mode and 60 m in
passive mode.
The lipid distribution in the mouse meibomian gland was examined with picosecond spectral anti-Stokes Raman scattering (CARS) imaging. Spectral CARS data sets were generated by imaging specific localized regions of the gland within tissue sections at consecutive Raman shifts in the CH2 stretching vibrational range. Spectral differences between the location specific CARS spectra obtained in the lipid-rich regions of the acinus and the central duct were observed, which were confirmed with a Raman microspectroscopic analysis, and attributed to meibum lipid modifications within the gland. A principal component analysis of the spectral data set reveals changes in the CARS spectrum when transitioning from the acini to the central duct. These results demonstrate the utility of picosecond spectral CARS imaging combined with multivariate analysis for assessing differences in the distribution and composition of lipids in tissues.
Noise reduction algorithms for improving Raman spectroscopy signals while preserving signal information were
implemented. Algorithms based on Wavelet denoising and Kalman filtering are presented in this work as alternatives
to the well-known Savitky-Golay algorithm. The Wavelet and Kalman algorithms were designed based on
the noise statistics of real signals acquired using CCD detectors in dispersive spectrometers. Experimental results
show that the random noise generated in the data acquisition is governed by sub-Poisson statistics. The proposed
algorithms have been tested using both real and synthetic data, and were compared using Mean Squared Error
(MSE) and Infinity Norm (L∞) to each other and to the standard Savitky-Golay algorithm. Results show that
denoising based on Wavelets performs better in both the MSE and (L∞) the sense.
Charge-Coupled Device (CCD) detectors are becoming more popular in spectroscopy instrumentation. In spite of
technological advances, spurious signals and noise are unavoidable in Raman spectroscopes. In general, the noise
comes from two major sources, impulsive noise caused by high energy radiation from local or extraterrestrial
sources (cosmic rays), and noise produced in Raman backscattering estimation. In this work, two algorithms
for impulsive noise removal are presented, based in spectral and spatial features of the noise. The algorithms
combine pattern recognition and classical filtering techniques to identify the impulses. Once an impulse has been
identified, it is removed and substituted with data points having similar statistical properties as the surrounding
data.
Raman spectroscopy, in combination with optical microscopy provides a new non-invasive method to asses and image
cellular processes. Based on the spectral signatures of a cell's components, it is possible to image cellular organelles
such as the nucleus, chromatin, mitochondria or lipid bodies, at the resolution of conventional microscopy. Several
multivariate algorithms, for example hierarchical cluster analysis or orthogonal subspace projection, may be used to
reconstruct an image of a cell. The noninvasive character of the technique, as well as the associated chemical
information, may offer advantages over other imaging techniques such as fluorescence microscopy. Currently of
particular interest are uptake and intracellular fate of various pharmaceutical nanocarriers, which are widely used for
drug delivery purposes, including intracellular drug and gene delivery. We have imaged the uptake and distribution
patterns of several carrier systems over time. In order to distinguish the species of interest from their cellular
environment spectroscopically, the carrier particles or the drug load itself may be labeled with deuterium. Here, we
introduce the concept of Raman imaging in combination with vertex component data analysis to follow the uptake of
nanocarriers based on phospholipids as well as biodegradable polymers.
Synchrotron FTIR maps, focal plane array and linear array images recorded of 4 μm cervical biopsy sections from the surface epithelium and glandular endometrium are compared in terms of spatial resolution and applicability to the clinical environment. Synchrotron FTIR maps using a 10 μm aperture appear to provide a better spatial resolution capable of discerning single nuclei in the tissue matrix. Unsupervised hierarchical cluster analysis performed on the synchrotron, focal plane array and linear array data in the 1700-1400 cm-1 region show very similar clusters and mean-extracted spectra, demonstrating the robustness of FTIR microscopy and UHCA in the analysis of tissue sections. Maps recorded with the focal plane array using a conventional globar source take one-fortieth of the time but the spatial resolution precludes true single cell analysis in the tissue matrix. The high spatial resolution achieved with the synchrotron shows potential as a gold standard for FTIR diagnosis of cervical samples.
The ability of infrared (IR) spectroscopy to distinguish and map cancerous and non-cancerous tissue has opened the question of the origin of spectral differences between normal and cancerous cells. In this contribution, we report IR spectral maps of individual dried cancer cells, some of them in the process of cell division (mitosis), IR spectra of cells suspended in growth medium, and preliminary results of a statistical analysis of thousands of individual dried cancer cells.
Different cluster image reassembling methodologies have been used to generate infrared maps from FT-IR microspectra of human prostate tissue sections. Spectra were collected in transmission mode with high spatial resolution by the use of a HgCdTe focal plane array detector imaging system. While univariate imaging techniques such as chemical mapping often give unsatisfactory classification results, unsupervised multivariate data analysis techniques such as agglomerative hierarchical clustering, fuzzy C-means, or k-means clustering confirmed standard histopathological techniques and turned out to be helpful to identify and to discriminate tissues structures.
The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to chemical mapping. Among the cluster imaging methods, agglomerative hierarchical clustering (Ward's algorithm) turned out to be the best method in terms of tissue structure differentiation.
FT-IR microspectroscopy in combination with digital image reassembling methodologies have been used to characterize distinct tissue structures within thin sections from the human prostate. The spatially resolved microspectroscopic data were collected with a resolution near the diffraction limit (about 8 micrometers ) by using a HgCdTe focal plane array detector-based infrared imaging instrument. While IR imaging based on distinct spectral parameters such as intensities, frequency values, or half-widths (the univariate imaging technique of chemical mapping) often gives unsatisfactory results, multivariate data analysis techniques (e.g. hierarchical clustering or principal component analysis) confirmed standard histopathological techniques and turned out to be helpful to discriminate reliably between different tissues structures.
The diagnosis of prostate cancer is based on the visible microscopic evaluation of both cytological and architectural features of the prostate tissue sections. In order to determine whether IR spectral 'mapping' can be used to objectively distinguish between normal and neoplastic prostate tissue, a comparison between 'visual, point-by- point' and 'automated, point-by-point' IR measurements was performed. Automated, point-by-point analysis was performed without any prior diagnostic information. Visual, point-by- point measurements were based on histopathology, histochemistry and immunohistochemical analysis of the tissue samples. The spectra obtained from these measurements were compared to the spectra obtained from automated point- by-point analysis. Our results indicate that the spectra obtained from histopathologically directed measurements compares well with those of automated mapping methods. Therefore, we believe that current mapping methodology can be directly correlated with pathological diagnoses.
IR microspectral maps of healthy an diseased live tissue are reported, along with the methodology for obtaining such maps and methods for their interpretation. The result suggest that present technology permits maps to be collected that contain useful pathological information.
Advances in infrared spectroscopic methodology permit excellent infrared spectra to be collected from objects as small as single human cells. These advances have lead to an increased interest of the use of infrared spectroscopy as a medical diagnostic tool. Infrared spectra of myeloid leukemia (ML-1) cells are reported for cells derived from an asynchronous, exponentially-growing culture, as well as for cells that were fractionated according to their stage within the cell division cycle. The observed results suggest that the cells' DNA is detectable by infrared spectroscopy mainly when the cell is in the S phase, during the replication of DNA. In the G1 and G2 phases, the DNA is so tightly packed in the nucleus that it appears opaque to infrared radiation. Consequently, the nucleic acid spectral contributions in the G1 and G2 phases would be mostly that of cytoplasmic RNA. These results suggest that infrared spectral changes observed earlier between normal and abnormal cells may have been due to different distributions of cells within the stages of the cell division cycle.
Dispersive instruments for the observation of infrared circular dichroism were designed and constructed. Equations governing infrared CD are summarized, and the principles and details of the measurement of this effect are discussed. Results obtained from aqueous solutions of biological molecules are presented, and the conformational sensitivity of the technique is demonstrated.
The observation of Circular Dichroism (CD) in vibrational transitions in the infrared spectral region offers new possibilities to determine molecular solution conformation. The application of this technique, known as vibrational CD or VCD, to peptides and nucleic acids is described.
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