A Fast, Automated, Polynomial-Based Cosmic Ray Spike–Removal Method for the High-Throughput Processing of Raman Spectra

Schulze, H. Georg; Turner, Robin F. B.

doi:10.1366/12-06839

Cited by 24 publications

(19 citation statements)

References 24 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…44 In this cosmic ray removal algorithm, individual Raman spectra from one group ( primary or secondary) are compared with each other. The Matlab code was kindly provided by the author Schulze.…”

Section: Raman Spectroscopymentioning

confidence: 99%

Label-free identification and characterization of living human primary and secondary tumour cells

Tsikritsis

Richmond

Stewart

et al. 2015

Analyst

View full text Add to dashboard Cite

We used three label-free minimally invasive methods to characterize individual cells derived from primary and secondary tumours from the same patient, and of the same type – colorectal. Raman spectroscopy distinguished cells by their biochemical 'fingerprint' in a vibrational spectrum with 100% accuracy, and revealed that the primary cell line contains more lipids and alpha-helix proteins, whereas the secondary cell line contains more porphyrins and beta-sheet proteins. Stimulated Raman scattering (SRS) microscopy distinguished cells in chemically-specific images of CH2 bonds which revealed lipid droplets in secondary tumour cells. Atomic force microscopy (AFM) was used to distinguish cells with 80% accuracy by measuring their elasticity – secondary tumour cells (SW620) are around 3 times softer than primary ones (SW480). As well as characterizing the physical and biochemical differences between cell lines in vitro, these techniques offer three novel methods which could potentially be used for diagnosis – to assign a tumour as primary or secondary.

show abstract

Section: Raman Spectroscopymentioning

confidence: 99%

Label-free identification and characterization of living human primary and secondary tumour cells

Tsikritsis

Richmond

Stewart

et al. 2015

Analyst

View full text Add to dashboard Cite

show abstract

“…[35] The resulting spectra were flattened once more, in the same way as previously described, in case the first flattening procedure was performed on a spectrum with a cosmic ray. Fourthly, all 40 spectra were normalized to a 'zscore' distribution, where the mean value of the Raman signal (the y-axis in Fig.…”

Section: Methodsmentioning

confidence: 99%

Raman spectroscopy of fibroblast cells from a Huntington's disease patient

Tsikritsis

Elfick

Downes

2016

Spectroscopy Letters

View full text Add to dashboard Cite

We present a Raman spectroscopy case study of living fibroblast (skin) cells from a patient who developed Huntington's disease, with fibroblasts from a healthy volunteer as a control. Spectra were processed to remove cosmic rays, had a spectrum of the quartz substrate subtracted, and were flattened to remove cellular autofluorescence. We achieved an accuracy of 95% in discriminating individual cells, and assign spectral differences to (i) the reduction of cholesterol, (ii) the reduction of lipids, and (iii) an increase in beta-sheet proteins for fibroblasts with Huntington's disease. All these biochemical changes have been previously measured by other methods. Averages over all the cells in this study yield a difference which is extremely statistically significant [p < 0.0001]. ARTICLE HISTORY

show abstract

“…Iterative wavelet transform can also detect and remove cosmic spikes while smoothing Raman spectra . Two de‐spiking methods for processing online Raman spectra are effective for processing real‐time Raman data .…”

Section: Data Handling and Chemometricsmentioning

confidence: 99%

Real‐time in vivo cancer diagnosis using raman spectroscopy

Wang

Zhao

Short

et al. 2014

Journal of Biophotonics

118

View full text Add to dashboard Cite

Raman spectroscopy has becoming a practical tool for rapid in vivo tissue diagnosis. This paper provides an overview on the latest development of real-time in vivo Raman systems for cancer detection. Instrumentation, data handling, as well as oncology applications of Raman techniques were covered. Optic fiber probes designs for Raman spectroscopy were discussed. Spectral data pre-processing, feature extraction, and classification between normal/benign and malignant tissues were surveyed. Applications of Raman techniques for clinical diagnosis for different types of cancers, including skin cancer, lung cancer, stomach cancer, oesophageal cancer, colorectal cancer, cervical cancer, and breast cancer, were summarized. Schematic of a real-time Raman spectrometer for skin cancer detection. Without correction, the image captured on CCD camera for a straight entrance slit has a curvature. By arranging the optic fiber array in reverse orientation, the curvature could be effectively corrected.

show abstract

A Fast, Automated, Polynomial-Based Cosmic Ray Spike–Removal Method for the High-Throughput Processing of Raman Spectra

Cited by 24 publications

References 24 publications

Label-free identification and characterization of living human primary and secondary tumour cells

Label-free identification and characterization of living human primary and secondary tumour cells

Raman spectroscopy of fibroblast cells from a Huntington's disease patient

Real‐time in vivo cancer diagnosis using raman spectroscopy

Contact Info

Product

Resources

About