Background removal from spectra by designing and minimising a non-quadratic cost function

Mazet, Vincent; Carteret, Cédric; Brie, David; Idier, Jérôme; Humbert, Bernard

doi:10.1016/j.chemolab.2004.10.003

Cited by 272 publications

(204 citation statements)

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“…More details about sequential weighted Wiener estimation have been described elsewhere [16]. In order to evaluate the accuracy of a reconstructed Raman spectrum, the reconstructed Raman spectrum was first preprocessed to remove fluorescence background by using the fifth-order polynomial fitting [20]. Then the relative root mean square error (RMSE) of the reconstructed Raman spectrum after the removal of fluorescence background, relative to the measured Raman spectrum in which fluorescence background was also removed in the same manner, was computed as in Eq.…”

Section: Simulation Of Narrow-band Measurements and Methods Of Reconsmentioning

confidence: 99%

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Chen

Ong

Lin

et al. 2015

Biomed. Opt. Express

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Abstract:Raman spectroscopy has shown great potential in biomedical applications. However, intrinsically weak Raman signals cause slow data acquisition especially in Raman imaging. This problem can be overcome by narrow-band Raman imaging followed by spectral reconstruction. Our previous study has shown that Raman spectra free of fluorescence background can be reconstructed from narrow-band Raman measurements using traditional Wiener estimation. However, fluorescence-free Raman spectra are only available from those sophisticated Raman setups capable of fluorescence suppression. The reconstruction of Raman spectra with fluorescence background from narrow-band measurements is much more challenging due to the significant variation in fluorescence background. In this study, two advanced Wiener estimation methods, i.e. modified Wiener estimation and sequential weighted Wiener estimation, were optimized to achieve this goal. Both spontaneous Raman spectra and surface enhanced Raman spectra were evaluated. Compared with traditional Wiener estimation, two advanced methods showed significant improvement in the reconstruction of spontaneous Raman spectra. However, traditional Wiener estimation can work as effectively as the advanced methods for SERS spectra but much faster. The wise selection of these methods would enable accurate Raman reconstruction in a simple Raman setup without the function of fluorescence suppression for fast Raman imaging. Appl. Spectrosc. 50(6), 805-811 (1996). Spectrosc. 51(12), 1845-1848 (1997). 11. S. Chen, Y. H. Ong, and Q. Liu, "Fast reconstruction of Raman spectra from narrow-band measurements based on Wiener estimation," J. Raman Spectrosc. 44(6), 875-881 (2013). 12. J. Zhao, M. M. Carrabba, and F. S. Allen, "Automated fluorescence rejection using shifted excitation Raman difference spectroscopy," Appl. Spectrosc. 56(7), 834-845 (2002). 13. P. A. Mosier-Boss, S. Lieberman, and R. Newbery, "Fluorescence rejection in Raman spectroscopy by shiftedspectra, edge detection, and FFT filtering techniques," Appl. Spectrosc. 49(5), 630-638 (1995

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Section: Simulation Of Narrow-band Measurements and Methods Of Reconsmentioning

confidence: 99%

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Chen

Ong

Lin

et al. 2015

Biomed. Opt. Express

View full text Add to dashboard Cite

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“…The modified polyfit function for fluorescence subtraction [64], for example uses an iterative polynomial fitting approach to minimize a classical least square error in which peaks are eliminated. Mazet et al suggested an alternative method that minimizes non-quadratic cost functions specifically designed for optical spectroscopy [21]. It could be demonstrated that asymmetric truncated quadratic cost functions are insensitivity to large Raman peaks.…”

Section: Removal Of the Fluorescence Backgroundmentioning

confidence: 99%

“…This criterion, however cannot be always satisfied when baseline points are defined automatically. Manual definition of baseline points requires on the other hand user intervention and could be troublesome and time consuming in cases where many spectra have to be processed [21]. In that respect, sophisticated methods for automated polynomial baseline correction were suggested [21,64,65].…”

Section: Removal Of the Fluorescence Backgroundmentioning

confidence: 99%

“…absorbance values are systematically evaluated. Subtracting the estimation of a background from the un-processed spectrum leads to a more interpretable signal, allowing to determine spectral parameters (band positions, intensity values) more accurately [21]. A large variety of different methods for background estimation and correction has been suggested.…”

Section: Baseline Correctionmentioning

confidence: 99%

“…It could be demonstrated that asymmetric truncated quadratic cost functions are insensitivity to large Raman peaks. Furthermore, the method of Mazet et al allows to consider also the noise level which makes the technique particularly useful for fluorescence correction of Raman spectra [21,76].…”

Section: Removal Of the Fluorescence Backgroundmentioning

confidence: 99%

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Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

291

203

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IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient data and has generated an urgent need for new and automated methods of spectral data analysis. Spectral data pre-processing is an important first step in the workflow of IR and Raman spectra analysis which involves specific processing procedures performed on the raw data. Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy. The main goals of data pre-processing can be summarized as follows: (i) Improvement of the robustness and accuracy of subsequent quantitative or classification analyses (ii) Improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines (iii) Detection and removal of outliers and trends (iv) Reduction of the dimensionality of the data mining task. Removal of irrelevant and redundant information by feature selection. For systematic reasons it is useful to subdivide data pre-processing procedures into at least eight different categories. These groups can be used to perform the following operations: 1. Exclusion (cleaning) -this class of data pre-processing methods is used to detect and eliminate spectral outliers. Tests for spectral quality are important examples and aim at the detection of outlier spectra prior to further data mining tasks. Removal of outlier spectra can be achieved by labeling them as NaNs (Not a Number), for example as "bad" pixel spectra in hyperspectral imaging applications. Another way of outlier removal in hyperspectral imaging applications is the interpolation of bad pixel spectra by using spectral data from neighboring locations. 2. Normalization -normalization is used to scale the spectra within a similar range. In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3. Filtering -in frequency analyses filtering is known as a group of methods that removes unwanted frequencies from the signals to be analyzed. Examples for...

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Raman Spectroscopy – A Powerful Tool for the Quantitative Determination of the Composition of Polymorph Mixtures: Application to CaCO₃ Polymorph Mixtures

et al. 2006

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Raman spectroscopy is an ideal method for identifying polymorphs, because it provides excellent fingerprint spectra specific to each crystal structure. Currently, Raman spectroscopy is successfully used forthe quantitative polymorphic analysis of calcium carbonate only in the case of binary mixtures. When a third polymorph is present in the mixture, errors in the quantitative determination increase due to the difficulty in discriminating the different phases in the Raman spectra. In the present work, a new method is developed for mixtures containing more than two polymorphs and tested in the case of vaterite, aragonite, and calcite mixtures of different compositions. It is proved that Raman spectroscopy is a powerful, accurate, and reliable technique for quantitative determinationofpolymorphmixturesofcalcium carbonate.Themethodcanbe usedforothercompoundsexhibitingmultiplepolymorphs.

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Background removal from spectra by designing and minimising a non-quadratic cost function

Cited by 272 publications

References 19 publications

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Raman Spectroscopy – A Powerful Tool for the Quantitative Determination of the Composition of Polymorph Mixtures: Application to CaCO₃ Polymorph Mixtures

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Background removal from spectra by designing and minimising a non-quadratic cost function

Cited by 272 publications

References 19 publications

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Optimization of advanced Wiener estimation methods for Raman reconstruction from narrow-band measurements in the presence of fluorescence background

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Raman Spectroscopy – A Powerful Tool for the Quantitative Determination of the Composition of Polymorph Mixtures: Application to CaCO3 Polymorph Mixtures

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Raman Spectroscopy – A Powerful Tool for the Quantitative Determination of the Composition of Polymorph Mixtures: Application to CaCO₃ Polymorph Mixtures