Raman Spectroscopic Methods for In Vitro and In Vivo Tissue Characterization

Wolthuis, Rolf; Schut, Tom C. Bakker; Caspers, Peter; Buschman, H.P.J.; Römer, T; Bruining, H. A.; Puppels, Gerwin J.

doi:10.1016/b978-012447836-7/50034-8

Cited by 62 publications

(67 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The constant background signal contribution originating from optical elements in the laser light delivery pathway was subtracted from all spectra. The reference spectrum of a tungsten band lamp of known temperature was used to correct for the wavelength-dependent signal detection efficiency of the Raman setup (29,47). Calibration of the wave number axis was performed by using the known wavelengths of the atomic lines from neon and argon.…”

Section: Yeastmentioning

confidence: 99%

Rapid Identification ofCandidaSpecies by Confocal Raman Microspectroscopy

et al. 2002

View full text Add to dashboard Cite

Candida species are important nosocomial pathogens associated with high mortality rates. Rapid detection and identification of Candida species can guide a clinician at an early stage to prescribe antifungal drugs or to adjust empirical therapy when resistant species are isolated. Confocal Raman microspectroscopy is highly suitable for the rapid identification of Candida species, since Raman spectra can be directly obtained from microcolonies on a solid culture medium after only 6 h of culturing. In this study, we have used a set of 42 Candida strains comprising five species that are frequently encountered in clinical microbiology to test the feasibility of the technique for the rapid identification of Candida species. The procedure was started either from a culture on Sabouraud medium or from a positive vial of an automated blood culture system. Prior to Raman measurements, strains were subcultured on Sabouraud medium for 6 h to form microcolonies. Using multivariate statistical analyses, a high prediction accuracy (97 to 100%) was obtained with the Raman method. Identification with Raman microspectroscopy may therefore be significantly faster than identification with commercial identification systems that allow various species to be identified and that often require 24 to 48 h before a reliable identification is obtained. We conclude that confocal Raman microspectroscopy offers a rapid, accurate, and easy-to-use alternative for the identification of clinically relevant Candida species.

show abstract

Section: Yeastmentioning

confidence: 99%

Rapid Identification ofCandidaSpecies by Confocal Raman Microspectroscopy

et al. 2002

View full text Add to dashboard Cite

show abstract

“…After acquisition, the spectra were first calibrated using Raman calibration standards as described earlier (Wolthuis et al, 1999). The reference spectrum of a tungsten-band lamp of known temperature was used to correct for the wavelength-dependent signal detection efficiency of the Raman setup.…”

Section: Pretreatment Of Spectramentioning

confidence: 99%

“…To minimize the influence of any slowly varying fluorescence or background scatter in the spectra which is noninformative, the first derivative of the spectra was taken (using the Savitzky-Golay method). The resulting spectra were subsequently scaled so that all the derivative spectra of a map had zero mean and unit standard deviation (autoscaling or standard normal variate scaling) (Wolthuis et al, 1999).…”

Section: Raman Mapsmentioning

confidence: 99%

“…In addition, pathologic changes in molecular composition or structure are reflected in the spectra, enabling development of diagnostic tools based on Raman spectroscopy. In recent years, significant progress has been reported in the application of Raman spectroscopy for ex vivo and in vivo tissue characterization (Bakker Schut et al, 2000;Boustany et al, 1999;Buschman et al, 2000;Frank et al, 1995;Hanlon et al, 2000;MahadevanJansen and Richards-Kortum, 1996;MahadevanJansen et al, 1998aMahadevanJansen et al, , 1998bManoharan et al, 1994Manoharan et al, , 1996Manoharan et al, , 1998Shim et al, 2000;Stone et al, 2000;Wolthuis et al, 1999). So far only a few studies have reported on the use of Raman spectroscopy for brain tissue characterization.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Discriminating Vital Tumor from Necrotic Tissue in Human Glioblastoma Tissue Samples by Raman Spectroscopy

Koljenović

Choo-Smith

Schut

et al. 2002

Laboratory Investigation

182

142

View full text Add to dashboard Cite

“…Moreover, clinical applications require such data to be transferable from one instrument to the next. This means that both reference Raman spectra and the spectra measured during an actual clinical measurement should be free of instrument signature or its biasing effects (15,16). Otherwise, considerable time needs to be spent for preparing the required data set for the particular instrument, which can in turn reduce the applicability of the clinical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Developing an Instrument-Independent Algorithm for Raman Spectroscopy: A Case of Cancer Detection

Dehghani-Bidgoli

Baygi

Kabir

et al. 2014

Technol Cancer Res Treat

View full text Add to dashboard Cite

One of the problems in the use of Raman spectroscopy for cancer detection in clinical application is the variety of Raman instruments, producing different spectra for the same sample, due to the nature of the measurement system. This prevents the measured spectra from different systems to be compared against one another without appropriate tools and techniques. Therefore, for each instrument one needs to spend considerable amount of time to prepare a set of reference data based on which the future measurements to be interpreted.For early diagnosis of cancer by Raman spectroscopy, there is a need for an algorithm by which such diagnosis can be made by any type of Raman instrument giving rise to the same findings. In the present study we have investigated the detection of breast cancer in three classes of breast samples (normal, benign and cancer) using three different Raman instruments (Almega, Bruker and R3000) to develop an algorithm that, irrespective of the type of Raman instrument, can be applied to the spectra to extract the features necessary to arrive at the same diagnosis. In doing so, we employed different pre-processing methods to eliminate the instrument-dependent effects on the spectra enabling us to fuse such spectra obtained from different instruments. Then, we classified the data using support vector machine (SVM) and multi-layer perception (MLP) to assess the degree to which the employed methods have been able to detect cancer. The results of the study showed that the range and resolution matching using spline interpolation, and noise and fluorescence elimination using wavelet and SNV normalizations were the most sensitive and accurate procedures for eliminating the instrumental specification-based effects and fusing the data from different instruments.

show abstract

Raman Spectroscopic Methods for In Vitro and In Vivo Tissue Characterization

Cited by 62 publications

References 56 publications

Rapid Identification ofCandidaSpecies by Confocal Raman Microspectroscopy

Rapid Identification ofCandidaSpecies by Confocal Raman Microspectroscopy

Discriminating Vital Tumor from Necrotic Tissue in Human Glioblastoma Tissue Samples by Raman Spectroscopy

Developing an Instrument-Independent Algorithm for Raman Spectroscopy: A Case of Cancer Detection

Contact Info

Product

Resources

About