11 results match your criteria: "Institute for FUMI Theory.[Affiliation]"

In pharmaceutical analysis using a high-performance liquid chromatography (HPLC) system, repeatability assessment is significant to obtain reliable and precise quantitative results. The purpose of the present study is to experimentally show the statistical reliability of a relative standard deviation (RSD) of peak area estimated by a chemometric tool based on probability theory, called the function of mutual information (FUMI) theory, which stochastically provided an RSD of peak area and SD of baseline areas with width k (s(k)) from noises and a signal on a single chromatogram. An ultra-high-performance liquid chromatography with ultraviolet detection (UHPLC-UV) for determining ergosterol was applied as an example of the repeatability assessment.

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ISO 11843 part 7 (ISO 11843-7) can provide a standard deviation (SD) of area measurements of a target peak through the stochastic behaviors of instrumental noises. The purpose of this study is to demonstrate that ISO 11843-7 can be applied to assess repeatability in an isocratic liquid chromatography-tandem mass spectrometry (LC-MS/MS) system without repetitive measurements. The relative standard deviation (RSD) of the peak area of ergosterol picolinyl ester, which was used as an example, on a multiple reaction monitoring (MRM) chromatogram was determined by ISO 11843-7.

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The function of mutual information (FUMI) theory proposes that it is possible to obtain a relative standard deviation (RSD) of the peak area of an analyte from baseline noises and a signal on a single chromatogram when the analyte concentrations are proportional to their peak areas. In this study, we demonstrate that the FUMI theory using noise parametrization by the difference method is applicable for the evaluations of repeatability and detection limit (DL) in high-performance liquid chromatography with electrochemical detection (HPLC-ECD). HPLC-ECD for determining vincristine (VCR) was taken as an example, and VCR was detected on a glassy carbon surface at +0.

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The present study examines whether short measurement time and noise filter processing in an ultra-high-performance liquid chromatography with ultraviolet detection (UHPLC-UV) contribute to limitations for repeatability assessment based on the ISO 11843 part 7 (ISO 11843-7), which can stochastically provide a measurement standard deviation (SD) caused by baseline noise (S). In this study, ergosterol was used as an example in UHPLC-UV analysis. From the results of power spectrum analysis of baseline noise, 1024 consecutive digital data points provided a suitable S.

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The purpose of this paper is to propose a simple method for daily inspections of a gas chromatography-mass spectrometry (GC-MS) system with an instrumental detection limit (IDL) as an indicator. A definition of DLs by ISO is 3.3σ where σ denotes the standard deviation (SD) of blank measurements.

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The purpose of this study is to elucidate uncertainty structures of internal standard (IS) methods as compared with absolute calibration methods in liquid chromatography. A quantitative test of indomethacin with butyl 4-hydroxybenzoate as an IS in high-performance liquid chromatography with ultra-violet detection is taken here as an example. The repeatability is evaluated by both a usual statistical method of repetition and a theoretical approach, called the function of mutual information (FUMI) theory.

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A previous paper of this series of study put forward a basic model of an automated system for predicting detection limits and showed its application to a simple example of isocratic high-performance liquid chromatography (HPLC). This paper describes an expansion of the basic system into gradient HPLC. The most serious problem with the expansion is a long-term variation in backgrounds, called gradient baseline drifts, which in theory cannot be covered by a noise model (stationary random process) of the original system.

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This paper presents a basic model of an automated system for predicting the detection limit and precision profile (plot of relative standard deviation (RSD) of measurements against concentration) in chromatography. The fundamental assumption is that the major source of response errors at low sample concentrations is background noise and at high concentrations, it is the volumes injected into an HPLC system by a sample injector. The noise is approximated by the mixed random processes of the first order autoregressive process AR(1) and white noise.

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The present study proposes a method for the assessment of repeatability in supercritical fluid chromatography with electrochemical detection (SFC-ECD), based on the ISO 11843 part 7 (ISO 11843-7:2018) which can theoretically provide detection limits and standard deviation (S.D.) through the stochastic properties of baseline noise without repetitive measurements of real samples.

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This paper puts forward a time and material-saving method for evaluating the repeatability of area measurements in gradient HPLC with UV detection (HPLC-UV), based on the function of mutual information (FUMI) theory which can theoretically provide the measurement standard deviation (SD) and detection limits through the stochastic properties of baseline noise with no recourse to repetitive measurements of real samples. The chromatographic determination of terbinafine hydrochloride and enalapril maleate is taken as an example. The best choice of the number of noise data points, inevitable for the theoretical evaluation, is shown to be 512 data points (10.

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This paper provides two approaches to estimate the standard deviation of measurements from baseline noise in instrumental output when (i) in theory, the noise can be approximated by a well-established random process in statistics and mathematics, referred to as a stationary process and (ii) in practice, the baseline noise is the predominant source of measurement error. For the first approach proposed, a general evaluation equation for measurement precision, when the baseline noise can be treated as a stationary process, is derived as a function of the process autocorrelations and process variance of the noise. In particular, for the second approach, when the baseline noise is a mixed random process of white noise and a first order autoregressive (AR(1)) process, the corresponding equation for the precision is also derived.

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