Log-normal derived equations for the determination of chromatographic peak parameters from graphical measurements

Grimalt, Joan O.; Olivé, Joaquim

doi:10.1016/s0003-2670(00)80869-7

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Particles larger than 700 nm are measured by a TSI APS3321 aerodynamic particle sizer in combination with a dilution stage (TSI 3302A). The aerosol composition in terms of sulfate, nitrate, and dicarboxylates is determined online by steam jet aerosol collection/ion chromatography (SJAC/IC) …”

Section: Experimental Methodsmentioning

confidence: 99%

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

et al. 2009

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Reactive uptake coefficients for loss of N(2)O(5) to micron-size aerosols containing oxalic malonic, succinic, and glutaric acids, and mixtures with ammonium hydrogen sulfate and ammonium sulfate, are presented. The uptake measurements were made using two different systems: atmospheric pressure laminar flow tube reactor (Cambridge) and the Large Indoor Aerosol Chamber at Forschungszentrum Juelich. Generally good agreement is observed for the data recorded using the two techniques. Measured uptake coefficients lie in the range 5 x 10(-4)-3 x 10(-2), dependent on relative humidity, on particle phase, and on particle composition. Uptake to solid particles is generally slow, with observed uptake coefficients less than 1 x 10(-3), while uptake to liquid particles is around an order of magnitude more efficient. These results are rationalized using a numerical model employing explicit treatment of both transport and chemistry. Our results indicate a modest effect of the dicarboxylic acids on uptake and confirm the strong effect of particle phase, liquid water content, and particulate nitrate concentrations.

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Section: Experimental Methodsmentioning

confidence: 99%

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

et al. 2009

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“…Researchers have explored other mathematical functions as well to characterize the peaks that are significantly different from Gaussian peak mainly to characterize the tailing or fronting effects. Among the wide range of functions studied in the past, some of the commonly used functions include exponentially modified Gaussian function (EMG) and log‐normal function . Di Marco and Bombi provide an extensive list of functions that have been reported in the past for peak characterization.…”

Section: Introductionmentioning

confidence: 99%

The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

Misra

Wahab

Patel

et al. 2019

J of Separation Science

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Modern chromatographic data acquisition softwares often behave as black boxes where the researchers have little control over the raw data processing. One of the significant interests of separation scientists is to extract physico-chemical information from chromatographic experiments and peak parameters. In addition, column developers need the total peak shape analysis to characterize the flow profile in chromatographic beds. Statistical moments offer a robust approach for providing detailed information for peaks in terms of area, its center of gravity, variance, resolution, and its skew without assuming any peak model or shape. Despite their utility and theoretical significance, statistical moments are rarely incorporated as they often provide underestimated or overestimated results because of inappropriate choice of the integration method and selection of integration limits. The Gaussian model is universally used in most chromatography softwares to assess efficiency, resolution, and peak position. Herein we present a user-friendly, and accessible approach for calculating the zeroth, first, second, and third moments through more accurate numerical integration techniques (Trapezoidal and Simpson's rule) which provide an accurate estimate of peak parameters as compared to rectangular integration. An Excel template is also provided which can calculate the four moments in three steps with or without baseline correction.

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“…Model fitting was more robust with a fixed value for symmetry than when the peak symmetry was derived from the raw data. For the equation of the peak, we used the log-normal function as described by Grimalt et al Peak fitting was solved by the Levenberg–Marquardt algorithm (minpack.lm library), , and the RT, PH, PW, and peak symmetry (sym) were derived.…”

Section: Methodsmentioning

confidence: 99%

“…9,10 To achieve a robust estimation of the peak parameters of acquired peaks, a series of different models have been tested and applied, such as Bi-Gaussian, 11 the generalized exponential function, 12,13 the Chesler−Cram function, 14 and the log-normal function. 15 A comprehensive overview and review of 86 functions proposed to describe chromatographic peaks was given by Di Marco et al 16 In this work, we present a program that uses the Levenberg−Marquardt algorithm 17 to fit acquired chromatographic data to the log-normal model of the peak. Objective chromatographic parameters from this equation (model) were derived and compared with the initial results based on the acquired data.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In contrast with the theoretic chromatographic peak that corresponds to a Gaussian peak, acquired peaks are usually characterized by imperfections, such as asymmetry, skewness, or tailing. The description of chromatographic peaks by empirical peak shape models has been extensively studied and utilized. , To achieve a robust estimation of the peak parameters of acquired peaks, a series of different models have been tested and applied, such as Bi-Gaussian, the generalized exponential function, , the Chesler–Cram function, and the log-normal function . A comprehensive overview and review of 86 functions proposed to describe chromatographic peaks was given by Di Marco et al In this work, we present a program that uses the Levenberg–Marquardt algorithm to fit acquired chromatographic data to the log-normal model of the peak.…”

Section: Introductionmentioning

confidence: 99%

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Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis

Stingl

Luider

2021

J. Proteome Res.

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Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

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Log-normal derived equations for the determination of chromatographic peak parameters from graphical measurements

Cited by 10 publications

References 24 publications

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis

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Log-normal derived equations for the determination of chromatographic peak parameters from graphical measurements

Cited by 10 publications

References 24 publications

Reactive Uptake of N2O5 by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

Reactive Uptake of N2O5 by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis

Contact Info

Product

Resources

About

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content

Reactive Uptake of N₂O₅ by Aerosols Containing Dicarboxylic Acids. Effect of Particle Phase, Composition, and Nitrate Content