Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands

Kauppinen, J.; Moffatt, Douglas J.; Mantsch, Henry H.; Cameron, David G.

doi:10.1366/0003702814732634

Cited by 1,312 publications

(609 citation statements)

References 5 publications

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“…This kind of signal treatments has already been employed in the bread staling process study, particularly to follow the change with time of the ratio between the absorbance at 1019 and 1037 cm -1 , where two bands connected to the staling process are located. 15,19,20,26 This kind of data treatment may result quite laborious, since it is based on many adjustable parameters, with the risk of diminishing the objectivity of the results. For this reason, in the present work an alternative approach for the study of the staling process by MIR spectroscopy, based on the use of principal component scores, has been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Use of Multivariate Analysis of MIR Spectra to Study Bread Staling

et al. 2005

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Different kinds of bread, stored at constant temperature and at controlled humidity conditions for a week since their manufacturing date, were analysed by Attenuated Total Reflectance‐Fourier Transform InfraRed (ATR‐FTIR) spectroscopy. The collected spectra were processed by Principal Component Analysis (PCA), in order to evaluate the changes occurring during bread ageing. For the sake of comparison, the 1060‐950 cm−1‐1 spectral window has been also investigated by curve‐fitting methods. It was observed that the first PC increases monotonically with ageing of samples. Furthermore, the more influential variables on PC1 correspond to spectral regions where are located stretching and bending bands, which are mainly attributed to typical starch bonds vibrations.

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Section: Introductionmentioning

confidence: 99%

Use of Multivariate Analysis of MIR Spectra to Study Bread Staling

et al. 2005

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“…Fourier self-deconvolution: Fourier self-deconvolution (FSD) is an alternative method for resolution enhancement. The FSD method was initially presented by Stone [30] and has been developed further as a technique to computationally resolve overlapped IR bands from spectra of condensed phase samples [31][32][33][34]. FSD has been employed in countless studies to reduce the degree of overlap between two adjacent bands, particularly in the field of secondary structure analysis of proteins [35][36][37].…”

Section: Spectral Filtering (Smoothing/derivatives Fourier-self Decomentioning

confidence: 99%

“…Furthermore, FSD filter functions determine the shape of deconvolved bands and the SNR degradation in the FSD spectra [38]. Inadequate FSD filter parameters may result in under-or over-deconvolution, with the latter one characterized by noise amplification and the appearance of large negative side-lobes (see [31,38,39] for details].…”

Section: Spectral Filtering (Smoothing/derivatives Fourier-self Decomentioning

confidence: 99%

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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“…Fourier filtering methods (31,32) were used to enhance resolution in the EPR spectra. The spectra were analyzed using the following spin Hamiltonian: (1) The first two terms in Equation 1 represent the Zeeman interaction of the low-spin Co 2+ of cob (II)alamin and the anhydroadenosyl radical with the external magnetic field, respectively; the third and fourth terms represent the magnetic dipole-dipole 2 (ZFS) and isotropic exchange interactions between cob(II)alamin and the anhydroadenosyl radical, respectively; and H nuc represents the nuclear hyperfine interactions: (2) In general, Euler rotations (33) are needed to bring each of the tensors in Equation 1 and Equation 2 into a common frame of reference, in this case the g-axis system of Co 2+ .…”

Section: Spectral Simulationsmentioning

confidence: 99%

Analysis of the Cob(II)alamin−5‘-Deoxy-3‘,4‘-anhydroadenosyl Radical Triplet Spin System in the Active Site of Diol Dehydrase

et al. 2006

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A triplet spin system (S = 1) is detected by low-temperature electron paramagnetic resonance (EPR) spectroscopy in samples of diol dehydrase and the functional adenosylcobalamin (AdoCbl) analog 5′-deoxy-3′,4′-anhydroadenosylcobalamin (anAdoCbl). Different spectra are observed in the presence and absence of the substrate (R,S)-1,2-propanediol. In both cases, the spectra include a prominent half-field transition (ΔM S = 2) that is a hallmark of strongly-coupled triplet spin systems. The appearance of 59 Co-hyperfine splitting in the EPR signals as well as the positions (g-values) of the signals in the spectra show that half of the triplet spin is contributed by the low spin Co 2+ of cob (II)alamin. Linewidth effects from isotopic labeling ( 13 C and 2 H) in the 5′-deoxy-3′,4′-anhydroribosyl ring demonstrate that the other half of the spin triplet is from an allylic 5′-deoxy-3′, 4′-anhydroadenosyl (anhydroadenosyl) radical. The zero-field splitting (ZFS) tensors describing the magnetic dipole-dipole interactions of the component spins of the triplets have rhombic symmetry because of electron spin delocalization within the organic radical component and nearness of the radical to the low spin Co 2+ . The dipole-dipole interaction was modeled as a summation of pointdipole interactions involving the spin-bearing orbitals of the anhydroadenosyl radical and cob(II) alamin. Geometries which are consistent with the ZFS tensors in the presence and absence of substrate position the 5′-carbon of the anhydroadenosyl radical 3.5 Å and 4.1 Å and from Co 2+ , respectively. Homolytic cleavage of the cobalt-carbon bond of the analog in the absence of substrate indicates that, in diol dehydrase, binding of the coenzyme to the protein weakens the bond prior to binding of substrate.A common characteristic of AdoCbl 1 -dependent enzymes is the generation and utilization of enzyme-bound organic radicals (1-3). The Co-C5′ bond of AdoCbl is relatively weak having a bond dissociation energy of ~ 30 kcal mol −1 (4). A prevalent mechanism for AdoCbldependent enzymes begins with the homolytic cleavage of the Co-C5′ bond to give cob(II) alamin and the 5′-deoxyadenosyl radical (1). The 5′-deoxyadenosyl radical being an unstabilized, primary alkyl radical is expected to have a short lifetime, low concentration, and a high reactivity towards H atom abstraction. Thus far, these properties of the 5′-deoxyadenosyl radical have precluded direct observation of the radical by EPR spectroscopy.

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Fourier Self-Deconvolution: A Method for Resolving Intrinsically Overlapped Bands

Cited by 1,312 publications

References 5 publications

Use of Multivariate Analysis of MIR Spectra to Study Bread Staling

Use of Multivariate Analysis of MIR Spectra to Study Bread Staling

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Analysis of the Cob(II)alamin−5‘-Deoxy-3‘,4‘-anhydroadenosyl Radical Triplet Spin System in the Active Site of Diol Dehydrase

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