Assessment of material blending distribution for electrospun nanofiber membrane by Fourier transform infrared (FT-IR) microspectroscopy and image cluster analysis

2014 ◽  
Vol 66 ◽  
pp. 141-145 ◽  
Author(s):  
Jackapon Sunthornvarabhas ◽  
Kanjana Thumanu ◽  
Wanwisa Limpirat ◽  
Hyun-Joong Kim ◽  
Kuakoon Piyachomkwan ◽  
...  
2019 ◽  
Vol 74 (2) ◽  
pp. 178-186 ◽  
Author(s):  
Abigail V. Rutter ◽  
Jamie Crees ◽  
Helen Wright ◽  
Marko Raseta ◽  
Daniel G. van Pittius ◽  
...  

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.


2016 ◽  
Vol 70 (7) ◽  
pp. 1150-1156 ◽  
Author(s):  
Irina A. Balakhnina ◽  
Nikolay N. Brandt ◽  
Andrey Yu Chikishev ◽  
Yurii I. Grenberg ◽  
Irina A. Grigorieva ◽  
...  

2020 ◽  
Vol 74 (9) ◽  
pp. 1185-1197 ◽  
Author(s):  
Josef Brandt ◽  
Lars Bittrich ◽  
Franziska Fischer ◽  
Elisavet Kanaki ◽  
Alexander Tagg ◽  
...  

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.


2002 ◽  
Vol 68 (10) ◽  
pp. 4717-4721 ◽  
Author(s):  
Mareike Wenning ◽  
Herbert Seiler ◽  
Siegfried Scherer

ABSTRACT Fourier-transform infrared (FT-IR) microspectroscopy was used in this study to identify yeasts. Cells were grown to microcolonies of 70 to 250 μm in diameter and transferred from the agar plate by replica stamping to an IR-transparent ZnSe carrier. IR spectra of the replicas on the carrier were recorded using an IR microscope coupled to an IR spectrometer, and identification was performed by comparison to reference spectra. The method was tested by using small model libraries comprising reference spectra of 45 strains from 9 genera and 13 species, recorded with both FT-IR microspectroscopy and FT-IR macrospectroscopy. The results show that identification by FT-IR microspectroscopy is equivalent to that achieved by FT-IR macrospectroscopy but the time-consuming isolation of the organisms prior to identification is not necessary. Therefore, this method also provides a rapid tool to analyze mixed populations. Furthermore, identification of 21 Debaryomyces hansenii and 9 Saccharomyces cerevisiae strains resulted in 92% correct identification at the strain level for S. cerevisiae and 91% for D. hansenii, which demonstrates that the resolution power of FT-IR microspectroscopy may also be used for yeast typing at the strain level.


2009 ◽  
Vol 394 (5) ◽  
pp. 1443-1452 ◽  
Author(s):  
R. Dell’Anna ◽  
P. Lazzeri ◽  
M. Frisanco ◽  
F. Monti ◽  
F. Malvezzi Campeggi ◽  
...  

Cellulose ◽  
2009 ◽  
Vol 16 (6) ◽  
pp. 1099-1107 ◽  
Author(s):  
Hejing Yan ◽  
Zhaozhe Hua ◽  
Guoshi Qian ◽  
Miao Wang ◽  
Guocheng Du ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document