A Method For Analysis Of Clinical Tissue Samples Using Ft-Ir Microspectroscopic Imaging

1999 ◽  
Vol 5 (S2) ◽  
pp. 68-69
Author(s):  
Gloria M. Story ◽  
Curtis Marcott ◽  
Rina K. Dukor
Keyword(s):  
Fourier Transform ◽  
Focal Plane ◽  
Mercury Cadmium Telluride ◽  
Single Element ◽  
Imaging Studies ◽  
Pathology Laboratory ◽  
Tissue Samples ◽  
The Past ◽  
Ft Ir ◽  
Ir Microspectroscopy

During the past decade, Fourier transform infrared (FT-IR) microspectroscopy has been used successfully in many studies to differentiate normal and diseased tissue samples obtained from a variety of organs, including colon, cervix, prostate, and breast. IR images were constructed by collecting spectra point-by-point using a mapping stage on a FT-IR microscope equipped with a single-element detector. Five years ago, in collaboration with NIH scientists Dr. Neil Lewis and Dr. Ira Levin, Procter and Gamble researchers developed a technique for performing vibrational spectroscopic imaging microscopy using a liquid-nitrogen-cooled focal-plane array (FPA) detector and a step-scanning FT-IR spectrometer coupled to a refractive microscope. With this configuration, equipped with a new 64-pixel x 64-pixel Mercury-Cadmium-Telluride (MCT) FPA detector, we are able to image an 800-μm x 800-μm area of a specimen without moving the sample.For all of these IR imaging studies, the tissue samples were prepared in a manner that was special and different from that commonly used in a pathology laboratory, i.e.,the histopathology sample was not identical to the spectroscopic sample.

scholarly journals An Application of Fourier Transform Infra-red (FT-IR) Microspectroscopy to the Study of Cigarette Manufacturing Defects

1992 ◽  
Vol 15 (2) ◽  
pp. 59-64
Author(s):  
MM Thompson ◽  
MS Ireland
Keyword(s):  
Fourier Transform ◽  
Common Type ◽  
Manufacturing Defects ◽  
Infra Red ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Cigarette Manufacturing

AbstractFT-IR microspectroscopy was used to investigate a common type of cigarette defect in which the filter separates from the tobacco rod. Infra-red imagings of the adhesive located at this junction on the tipping papers from both defective and acceptable cigarettes were obtained. A comparison of these data revealed that although adhesive was present in the seam area of the defective cigarettes, the amount of adhesive was significantly less and its distribution was not uniform.

scholarly journals Identification of a Glass Substrate to Study Cells Using Fourier Transform Infrared Spectroscopy: Are We Closer to Spectral Pathology?

2019 ◽  
Vol 74 (2) ◽  
pp. 178-186 ◽  
Author(s):  
Abigail V. Rutter ◽  
Jamie Crees ◽  
Helen Wright ◽  
Marko Raseta ◽  
Daniel G. van Pittius ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Cancer Cells ◽  
Cell Line ◽  
Glass Substrate ◽  
Fourier Transform Infrared ◽  
Leukemia Cell Line ◽  
Different Types ◽  
Ft Ir ◽  
Fingerprint Region ◽  
Ir Microspectroscopy

The rising incidence of cancer worldwide is causing an increase in the workload in pathology departments. This, coupled with advanced analysis methodologies, supports a developing need for techniques that could identify the presence of cancer cells in cytology and tissue samples in an objective, fast, and automated way. Fourier transform infrared (FT-IR) microspectroscopy can identify cancer cells in such samples objectively. Thus, it has the potential to become another tool to help pathologists in their daily work. However, one of the main drawbacks is the use of glass substrates by pathologists. Glass absorbs IR radiation, removing important mid-IR spectral data in the fingerprint region (1800 cm−1 to 900 cm−1). In this work, we hypothesized that, using glass coverslips of differing compositions, some regions within the fingerprint area could still be analyzed. We studied three different types of cells (peripheral blood mononuclear cells, a leukemia cell line, and a lung cancer cell line) and lymph node tissue placed on four different types of glass coverslips. The data presented here show that depending of the type of glass substrate used, information within the fingerprint region down to 1350 cm−1 can be obtained. Furthermore, using principal component analysis, separation between the different cell lines was possible using both the lipid region and the fingerprint region between 1800 cm−1 and 1350 cm−1. This work represents a further step towards the application of FT-IR microspectroscopy in histopathology departments.

scholarly journals The Commercial Development of FT-IR Microspectroscopy

1993 ◽  
Vol 1 (3) ◽  
pp. 6-7
Author(s):  
John A. Reffner
Keyword(s):  
Fourier Transform ◽  
World War Ii ◽  
World War ◽  
Commercial Development ◽  
10Th Anniversary ◽  
Infrared Microspectroscopy ◽  
Fourier Transform Infrared Microspectroscopy ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Ft Ir Spectroscopy

Pittcon ‘93 marked the 10th anniversary of the introduction of a microscope attachment designed specifically for Fourier transform infrared microspectroscopy. The first commercial microscope developed for FT-IR spectroscopy was designed by Spectra-Tech, Inc., under the direction of then-owner D. W. Sting, in fulfillment of a contract with C. T. Foskett of the Digilab Division of BioRad. Digilab recognized a growing interest in the market for an FT-IR microscope and contracted with Spectra-Tech to design and build a microscope accessory for Digilab's FT-IR spectrometers. This microscope was introduced at the 1983 Pittsburgh Conference.Linking microscopy with Fourier transform spectroscopy was a very significant event, but the foundation of infrared microspectroscopy can be traced back to 1949. The explosive growth in the use of infrared-absorption spectroscopy following World War II led researchers R. Gore (in the U.S.A.), and R. Barer, A. R. Cole, and H. W. Thompson (in England), to investigate (in 1949) the possibility of recording infrared spectra of microscopic samples.

Fourier Transform Infrared (FT-IR) Microspectroscopy of 20th Century Russian Oil Paintings: Problem of Dating

2016 ◽  
Vol 70 (7) ◽  
pp. 1150-1156 ◽  
Author(s):  
Irina A. Balakhnina ◽  
Nikolay N. Brandt ◽  
Andrey Yu Chikishev ◽  
Yurii I. Grenberg ◽  
Irina A. Grigorieva ◽  
...  
Keyword(s):  
Fourier Transform ◽  
20Th Century ◽  
Fourier Transform Infrared ◽  
Oil Paintings ◽  
Ft Ir ◽  
Ir Microspectroscopy

scholarly journals Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Qingbo Li ◽  
Wei Wang ◽  
Xiaofeng Ling ◽  
Jin Guang Wu
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Tissue Samples ◽  
Normal Tissues ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Leave One Out

Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

High-Throughput Analyses of Microplastic Samples Using Fourier Transform Infrared and Raman Spectrometry

2020 ◽  
Vol 74 (9) ◽  
pp. 1185-1197 ◽  
Author(s):  
Josef Brandt ◽  
Lars Bittrich ◽  
Franziska Fischer ◽  
Elisavet Kanaki ◽  
Alexander Tagg ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Open Source Software ◽  
Fourier Transform Infrared ◽  
Particle Analysis ◽  
Particle Detection ◽  
Raman Spectrometry ◽  
Open Source Software Package ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Ir And Raman

Determining microplastics in environmental samples quickly and reliably is a challenging task. With a largely automated combination of optical particle analysis, Fourier transform infrared (FT-IR), and Raman microscopy along with spectral database search, particle sizes, particle size distributions, and the type of polymer including particle color can be determined. We present a self-developed, open-source software package for realizing a particle analysis approach with both Raman and FT-IR microspectroscopy. Our software GEPARD (Gepard Enabled PARticle Detection) allows for acquiring an optical image, then detects particles and uses this information to steer the spectroscopic measurement. This ultimately results in a multitude of possibilities for efficiently reviewing, correcting, and reporting all obtained results.

scholarly journals Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

2019 ◽  
Vol 73 (5) ◽  
pp. 556-564 ◽  
Author(s):  
Mahsa Lotfollahi ◽  
Sebastian Berisha ◽  
Davar Daeinejad ◽  
David Mayerich
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Cellular Level ◽  
Label Free ◽  
High Definition ◽  
Tissue Samples ◽  
Ft Ir ◽  
Downstream Analysis ◽  
Ir Spectroscopic ◽  
Multiple Samples

Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.

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