Two-Point Maximum Entropy Noise Discrimination in Spectra over a Range of Baseline Offsets and Signal-to-Noise Ratios

Schulze, H. Georg; Foist, Rod B.; Jirásek, A; Ivanov, André; Turner, Robin F. B.

doi:10.1366/000370207779947558

Cited by 12 publications

(21 citation statements)

References 17 publications

(22 reference statements)

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“…The methods and justification for the implementation of each processing step have been described previously (Matthews et al 2010). Briefly, a very conservative amount of spectral smoothing was applied with an in-house version of the two-point maximum entropy method (Greek et al 1995, Schulze et al 2007, Jirasek et al 2006, Matthews et al 2008. Baseline estimation was performed with a modified version of the signal removal method (Schulze et al 2005) for the LWN window and with three-point linear interpolation for the HWN window.…”

Section: Spectral Processing and Data Analysismentioning

confidence: 99%

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

et al. 2010

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This work investigates the capability of Raman spectroscopy (RS) to study the effects of ionizing radiation on single human tumour cells. Prostate tumour cells (cell line DU145) are cultured in vitro and irradiated to doses between 15 and 50 Gy with single fractions of 6 MV photons. Single-cell Raman spectra are acquired from irradiated and unirradiated cultures up to 5 days post-irradiation. Principal component analysis is used to distinguish the uniquely radiation-induced spectral changes from inherent sources of spectral variability arising from cell cycle differences and other known factors. We observe uniquely radiation-induced spectral changes which are correlated with both the irradiated dose and the incubation time post-irradiation. The spectral changes induced by radiation arise from biochemical differences in lipids, nucleic acids, amino acids and conformational protein structures between irradiated and unirradiated cells. To our knowledge, this study is the first use of RS to observe radiation-induced biochemical differences in single cells, and is the first use of vibrational spectroscopy to observe uniquely radiation-induced biochemical differences in single cells independent of concurrent cell-cycle- or cell-death-related processes.

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Section: Spectral Processing and Data Analysismentioning

confidence: 99%

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

et al. 2010

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“…Peak clipping, the truncation of peak tops, is a common occurrence with many smoothing methods. 60,61,69,70 It can be seen in Fig. 6c that some high intensity bins had more counts than would be expected for a normal distribution.…”

Section: Smoothingmentioning

confidence: 82%

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples

et al. 2018

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Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.

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“…Common smoothing-induced problems are losses in peak height, increases in peak width, and possibly other distortions in peak shape, as well as the related loss of resolution in the smoothed spectrum. 77,79,124,125 Figure 5C shows some of these smoothing effects on a simulated Raman spectrum, to which normally distributed noise was added, for a few smoothing approaches: a zero order Savitzky-Golay filter; a second order Savitzky-Golay filter; and a v 2 -based 77 filter. The simulated Raman spectrum, without noise, is also shown for comparison.…”

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confidence: 99%

“…TPMEM is ineffective when used with Raman signals that ride on a large background because the negative two-point entropies for two adjacent points, which are approximately equal in relative magnitudes, also do not differ much; so little advantage accrues from smoothing. 79 In contrast, if the baseline is flattened and has a ''zero'' mean, TPMEM is quite effective in SNR improvement, especially for low-SNR spectra. [78][79][80] The effect of a baseline is evident in Fig.…”

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confidence: 99%

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Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

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Two-Point Maximum Entropy Noise Discrimination in Spectra over a Range of Baseline Offsets and Signal-to-Noise Ratios

Cited by 12 publications

References 17 publications

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

Raman spectroscopy of single human tumour cells exposed to ionizing radiationin vitro

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

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