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  • 1
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Optimization of variables ; Information theory ; Kalman filter
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary The following chromatographic variables are totally optimized based on the recently developed information theory of optimization: mobile phase composition, column length, flow rate, wavelength, and the amount of internal standard. The optimal internal standard is selected from among six candidates. Two types of optimal conditions (Φ- and ϑ-optimals) are proposed: the Φ-optimal is defined as the most precise analysis (the maximal Φ) while the ϑ-optimal is the most efficient (rapid) analysis (the maximal ϑ). The observation times for the determination of an antipyretics mixture (three components) in liquid chromatography are ca. 50 s for the ϑ-optimal and ca. 8 min for the Φ-optimal. The reliability of the Φ- and ϑ-optimals is verified by experiments.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Chromatographia 30 (1990), S. 367-370 
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Optimization of variables ; Information theory ; Kalman filter
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary A simple optimization method based on the well-known Rs-minimum method and on the information theory of FUMI Φ is proposed. Resolution (Rs), peak area and height (or width) are the only parameters necessary for the calculation of the information Φ and information flow ϑ. The most precise analysis can be selected as the chromatogram having maximal ϑ. Mobile phase composition, column length, flow rate, detection wavelength, amount of internal standard, etc. can be optimized by this method.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Chromatographia 41 (1995), S. 66-74 
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Limit of detection ; Precision ; Uncertainty prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary A method to determine the limit of detection (LOD) in high performance liquid chromatography (HPLC) is described. The power spectral density of instrumental baseline variation is fitted by the simplex least squares methods with a mixed random process of white noise and Markov process as a model. The white noise is characterized by standard deviation (SD), ∼w; the Markov process by the SD, ∼m, and auto-correlation degree, ρ. All required parameters for calculating the LOD signal are obtained by experiment without repeat measurements. No arbitrary constants are needed. The LOD signal is uniquely determined and is characterized by 33.3% relative standard deviation (RSD) of analyte measurements and 0.13% of the error of the first type. This signal also specifies that the signal-to-noise ratio=3, using the definition of noise originating from the white noise and Markov process. The theoretical conclusion is verified by the Monte Carlo simulation using real baseline and peaks. The LOD concentrations for naphthalene, acenaphthene, pyrene and perylene are given.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Limit of detection ; Precision ; Uncertainty prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary The precision of integration over noisy instrumental output for quantitative analysis is studied. A probability theory is developed to predict the relative standard deviation (RSD) of integration results over an integration domain from one-point integation (peak height measurement) to entire area integration in HPLC. Common integration modes of horizontal zero line and oblique zero line are taken into account, but no peak overlap is assumed. The question of the analytical superiority of peak height measurement or integration for quantitation is answered. In the HPLC apparatus used, the minimum RSD of measurements is found in the integration domain of ca. ±0.5 σ for analytes [peaks are approximated by the Gaussian signal of width, σ (standard deviation)]. The RSD of integration measurements is also shown to depend on the stochastic properties of back-ground noise (uncorrelated noise and correlated 1/f type noise). The theoretical conclusion is verified by Monte Carlo simulation and HPLC experiments for some aromatic compounds.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Chromatographia 30 (1990), S. 371-376 
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Optimization of variables ; Information theory ; Kalman filter
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary The analytical roles of chromatographic variables (column length, etc.) can be soundly comprehended and compared in terms of the precision (Φ) of measurements and efficiency (ϑ) of analysis which are described as Shannon information and information flow, respectively. The φϑ plots of the optimization process and the information Φ transmitted by a single peak are useful to understand the analytical structure of optimization. Variables treated here are mobile phase composition (X), column length (L), mobile phase velocity rate (u), detection wavelength (λ) and plate number (N).
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Chromatographia 30 (1990), S. 171-175 
    ISSN: 1612-1112
    Keywords: Column liquid chromatography ; Optimization of variables ; Information theory ; Kalman filter
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary A chromatographic system set at an operating condition takes its own precision and efficiency which are numerically described by the information Φ called FUMI and the information flow ϑ, respectively. Optimization for a variable such as mobile phase composition draws a line in the Φ-ϑ space. This paper demonstrates that optimization of different variables displays different patterns of lines in the Φ-ϑ space. The variables examined here are mobile phase composition, column length, flow rate (velocity) and detection wavelength (or the amount of internal standard). Clear difference in the analytical roles of the variables can be known from the Φ-ϑ plots.
    Type of Medium: Electronic Resource
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