2015
DOI: 10.2116/analsci.31.1219
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Evaluation of Measurement Precision from Stationary Baseline Noise in Instrumental Analyses

Abstract: 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 stationar… Show more

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Cited by 7 publications
(2 citation statements)
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References 25 publications
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“…22) Procedures for the parametrization are quite simple in the new theory. Furthermore, the number of data necessary for the parametrization is no longer limited to 2 to the n-th power as it usually is when the fast Fourier transform algorithm is applied.…”
Section: Resultsmentioning
confidence: 99%
“…22) Procedures for the parametrization are quite simple in the new theory. Furthermore, the number of data necessary for the parametrization is no longer limited to 2 to the n-th power as it usually is when the fast Fourier transform algorithm is applied.…”
Section: Resultsmentioning
confidence: 99%
“…As an example, the function of mutual information (FUMI) theory, the practical applicability of which has been proved experimentally in a variety of settings, has been adopted as an international standard (ISO 11843-7). 15) The FUMI theory approximates the baseline noise with the mixed random processes of the first order autoregressive process (designated AR(1)) and white noise, the parameterization of which is attained by the least-squares fitting to noise power spectra. 13,14) Recently, Hayashi and Zhang proposed another method that utilized the autocorrelation method instead of the power spectrum.…”
Section: Introductionmentioning
confidence: 99%