The Biodata toolbox for MATLAB

Gussem, Kris De; Gelder, Joke De; Vandenabeele, Peter; Moëns, Luc

doi:10.1016/j.chemolab.2008.08.003

Cited by 32 publications

(21 citation statements)

References 8 publications

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“…The spectra were recorded with a 15 s accumulation time. After collection, the Raman spectra were processed in matlab with the Biodata toolbox (De Gussem et al, 2009). The Raman spectra were background-subtracted with the iterative polynomial fitting procedure described by Lieber & Mahadevan-Jansen (2003) (200 iterations, fifth-order polynomial).…”

mentioning

confidence: 99%

Aragonite-calcite-dolomite relationships in UHPM polycrystalline carbonate inclusions from the Kokchetav Massif, northern Kazakhstan

Korsakov¹,

Gussem²,

Zhukov³

et al. 2010

ejm

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The presence of aragonite in polycrystalline carbonate inclusions in garnet in diamond-grade metamorphic rocks from the Kokchetav Massif, N. Kazakhstan was identified for the first time by means of Raman analyses and mapping, cathodoluminescence images and optical and scanning electron microscopy. Aragonite appears within the inclusions as dirty, chaotically oriented materials surrounded by a clean monocrystalline calcite shell; the grain boundary between the host-garnet and the aragonite-bearing inclusions is often characterized by a wavy or amoeboid shape; no cracks occur around the aragonite-bearing inclusions; no significant shift in the main aragonite Raman band was measured. These observations indicate that residual pressure within the inclusion is minor. These features are inconsistent with an origin of aragonite at peak metamorphic conditions (6 GPa) by decomposition of dolomite.

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mentioning

confidence: 99%

Aragonite-calcite-dolomite relationships in UHPM polycrystalline carbonate inclusions from the Kokchetav Massif, northern Kazakhstan

Korsakov¹,

Gussem²,

Zhukov³

et al. 2010

ejm

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“…To illustrate the performance of our algorithm, we compare the baseline correction results obtained with the ones from three other algorithms for which implementations are available. The iterative polynomial method as described in Lieber and Mahadevan‐Jansen and its modified variant, optimized for tissue fluorescence from are available as part of the Biodata toolbox . The implementation of the automated baseline correction algorithm has been kindly made available by the authors.…”

Section: Resultsmentioning

confidence: 99%

“…The iterative polynomial method as described in Lieber and Mahadevan-Jansen [11] and its modified variant, optimized for tissue fluorescence from [12] are available as part of the Biodata toolbox. [19] The implementation of the automated baseline correction algorithm [15] has been kindly made available by the authors. The individual algorithms have been used with the following settings: The polynomial-based algorithms are employed by using a seventhorder polynomial, the fully automated as given, and our algorithm with default settings of five iteration steps, a noise width of 6 pixels and a manually chosen feature width of 180 pixels.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

Iterative morphological and mollifier-based baseline correction for Raman spectra

Koch

Suhr

Roth

et al. 2016

J. Raman Spectrosc.

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In vivo Raman spectroscopy with low signal-to-noise ratio and strong, irregularly shaped fluorescence background imposes a challenge for automatic baseline correction methods. In this work, an approach that enables fast and efficient batch baseline correction has been developed which is based on a morphological operation in combination with a mollifier algorithm. As this algorithm relies only on three parameters which are determined by the given experimental conditions, it can be used for automatic and objective processing of many Raman spectra. The applicability of the baseline correction is demonstrated on resonance Raman spectra of betacarotene mixed with fluorescent red ink as model system, on carotenoids in human skin and on an excitation-emission map of the green alga Haematococcus pluvialis. In the future, the algorithm opens the potential for wide application in Raman spectra analysis in biological contexts. In particular, it greatly facilitates data processing in cases where special photochemical sample preparation or complex experimental baseline removal were required before. Similarly, processing data of experiments using resonant excitation techniques yielding strong fluorescence background is possible.

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“…Boily and Ilton [145] published a Matlab code for generating 2D spectra. De Gussem et al [146] introduced a free software ''Biodata" toolbox for Matlab. 2D correlation analysis was discussed extensively for the article describing their software.…”

Section: Hetero Hybrid and Sample-sample Correlationmentioning

confidence: 99%