Smoothing and differentiation of data by simplified least square procedure

Steinier, Jean.; Termonia, Yves.; Deltour, Jules.

doi:10.1021/ac60319a045

Cited by 957 publications

(362 citation statements)

References 3 publications

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“…Derivative analysis was used to enhance subtle absorption features and to separate overlapping absorptions (Murphy et al 2005a). Reflectance data were converted to pseudo-absorbance units and fourth-derivative spectra (Bidigare et al 1989) calculated using a combined smoothing and differentiation technique, with a 24 nm smoothing window (Savitzky & Golay 1964, Steinier et al 1972). Absorptions by different pigments were identified as separate peaks and the absorption by each pigment extracted from each spectrum as the maximal derivative value for each peak above the zero-baseline.…”

Section: Concretementioning

confidence: 99%

Differential patterns of distribution of limpets on intertidal seawalls: experimental investigation of the roles of recruitment, survival and competition

Iveša¹,

Chapman²,

Underwood³

et al. 2010

Mar. Ecol. Prog. Ser.

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On artificial surfaces (seawalls) in Sydney Harbour (Australia), local biodiversity of grazers differs between sandstone and concrete constructions because limpets Siphonaria denticulata are more abundant on sandstone, while Patelloida latistrigata are more abundant on concrete walls. Competition between siphonarian and patellid limpets is unstudied on artificial structures, although the limpets are common and are known to compete in natural habitats. We tested hypotheses that the substratum (concrete or sandstone), intra-and/or interspecific competition, or a combination of factors, influence recruitment, survival, rates of grazing, or the quantity and types of algal food to explain the observed patterns. Such analyses enable better understanding of the processes influencing diversity. Both species recruited more to concrete than to sandstone plates. S. denticulata recruited more in the presence of conspecifics and slightly less in the presence of P. latistrigata. Results varied between locations and experiments, but each species survived better on concrete, which had more macro-and micro-algal food. Increased densities of P. latistrigata reduced survival of conspecifics and of S. denticulata. S. denticulata had no effect on survival of P. latistrigata, but reduced amounts of macro-algae. P. latistrigata did not affect macro-algae. Initially, there were more micro-algae on concrete and a minor effect of large densities of S. denticulata. After 95 d, differences between habitats decreased and P. latistrigata were more strongly associated with reduced amounts of micro-algae. Neither substratum, density, nor mix of limpets affected rates of grazing. Thus, interspecific interactions were similar to predictions from knowledge of natural habitats, despite the different characteristics of artificial habitats and the reduced intertidal area available for grazing. Recruitment and competition were important in explaining different densities of these limpets on sandstone and concrete walls. Increasing urbanization requires more experimental tests to identify how well current theories of intertidal ecology and of the processes maintaining local biodiversity can apply to artificial shorelines.

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Section: Concretementioning

confidence: 99%

Differential patterns of distribution of limpets on intertidal seawalls: experimental investigation of the roles of recruitment, survival and competition

Iveša¹,

Chapman²,

Underwood³

et al. 2010

Mar. Ecol. Prog. Ser.

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“…Furthermore, they provided numerical tables of these vectors and demonstrated how convolution vectors can be used to obtain smoothed n-order derivatives in a single convolution operation. Although the original paper contained a number of typographical errors that were subsequently corrected by Steinier et al [28] it became a classic and is now one of the most widely cited papers in the journal Analytical Chemistry [29]. Fourier self-deconvolution: Fourier self-deconvolution (FSD) is an alternative method for resolution enhancement.…”

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

285

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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“…Peak search is here performed by calculating higher order derivatives of the experimental profiles, based on a deconvolution algorithm according to Steinier et al (1972). Minima in the second derivative and maxima in the fourth derivative curve indicate starting positions 28 p for the final profile refinement.…”

Section: Profile Fitting Procedure-profile Analysis Of the Diffractiomentioning

confidence: 99%

Crystal Structure Analysis and Refinement Using Integrated Intensities from Accurate Profile Fits

Will¹

1988

Aust. J. Phys.

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Careful experimental techniques, especially using synchrotron radiation facilities, give well resolved diffraction patterns. Remaining overlapping peaks can be separated by profile fitting and profile analysis. The derived parameters are: peak position for lattice constants, integrated intensities for crystal structure work and halfwidth for line broadening analysis. Crystal structure refinements with the powder least squares program POWLS yield R factors routinely around R = 1·5% and as low as R = 0·6%.

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Smoothing and differentiation of data by simplified least square procedure

Cited by 957 publications

References 3 publications

Differential patterns of distribution of limpets on intertidal seawalls: experimental investigation of the roles of recruitment, survival and competition

Differential patterns of distribution of limpets on intertidal seawalls: experimental investigation of the roles of recruitment, survival and competition

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Crystal Structure Analysis and Refinement Using Integrated Intensities from Accurate Profile Fits

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