The quality control of measured data

Leisztner, L.; Barna, Peter

doi:10.1155/s1463924683000383

Cited by 4 publications

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“…From the data ofthe numerical method (tables and 2), it can also be seen that the empirical variances of the ratio of the peak height is much smaller than that of the peak areas. This fact is in good agreement with previous investigations [8]. As a consequence, the use of the peak height ratio allows the detection of smaller autocorrelation-type measurement error components than would be possible in the case of the ratio of the peak areas.…”

Section: Discussionsupporting

confidence: 92%

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Automatic detection of the autocorrelation‐type measurement error component

Hangos

Nagy²,

Leisztner³

1986

Journal of Analytical Methods in Chemistry

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Section: Discussionsupporting

confidence: 92%

“…xt a0 + M't.+ a(t-_) + S*et. (8) Note that in almost all practical cases, the standard deviation of the random measurement error component (S) and the mean of the measurement error (a0 + M't) depend on the true value Xt:…”

Section: Materials and Experimental Methodsmentioning

confidence: 99%

Automatic detection of the autocorrelation‐type measurement error component

Hangos

Nagy²,

Leisztner³

1986

Journal of Analytical Methods in Chemistry

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“…However, in this instance, a need to control the properties and especially the relative standard deviation (RSD) of the random error component arises. We showed previously [6] by applying Monte Carlo methods that it is possible to estimate the standard deviation of the measured data using the statistics of the difference test. Our long-term practical experience has proved the high practical value of such control.…”

Section: Introductionmentioning

confidence: 99%

Towards properly controlled analyticalmeasurement methods

Hangos

Leisztner

1989

Journal of Analytical Methods in Chemistry

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It is of great practical importance to develop simple methods for the automatic detection ofthe controlled state of the analytical method being applied. The key point is to find quantities that greatly affect the quality of the analytical results and that can be easily estimated during the measurement process from the measured data. The signal-to-noise ratio has proved to be such a quantity in gas chromatographic methods. The statistical properties of the estimation of the signal-to-noise ratio from gas chromatographic data have been investigated. The suggested practical method for estimating the signal-to-noise ratio proved to be biased from a mathematical statistical point of view, but the bias is usually not greater than 10%. It has been shown by practical examples that the signal-to-noise ratio affects the quality of the analytical results and it is easy to estimate its value from practical data.

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Chemometrics in Hungary

Veress¹

1988

Chemometrics and Intelligent Laboratory Systems

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The quality control of measured data

Cited by 4 publications

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Automatic detection of the autocorrelation‐type measurement error component

Automatic detection of the autocorrelation‐type measurement error component

Towards properly controlled analyticalmeasurement methods

Chemometrics in Hungary

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