Noise, filters and detection limits

Sun, Xun-Yun; Singh, Hameraj; Millier, Brian; Warren, Charles H.; Aue, Walter A.

doi:10.1016/0021-9673(94)00805-1

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…Typically, in chromatography, a single representative run is used to calculate the S/N by taking the ratio of the peak height and the standard deviation of a section of baseline noise [43]. This approach cannot be taken on a GRAM signal (e.g., Figure 2.B) because the baseline fluctuations are not noise but part of the calculated signal shape.…”

Section: Resultsmentioning

confidence: 99%

Enhancing the Limit of Detection for Comprehensive Two-Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

Fraga¹,

Prazen²,

Synovec³

2000

J. High Resol. Chromatogr.

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Section: Resultsmentioning

confidence: 99%

Enhancing the Limit of Detection for Comprehensive Two-Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

Fraga¹,

Prazen²,

Synovec³

2000

J. High Resol. Chromatogr.

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“…In spectrophotometric measurements the uncertainty in the absorbance depends strongly on the absorbance itself and other parameters of the experiment as well. In these cases, the introduction of weighting factors may be considered (194,195,199).…”

Section: Non-uniform Variance In Analytical Chemistry (Weighted Lineamentioning

confidence: 99%

“…Random error is caused by noise and noise (190)(191)(192)(193)(194)(195)(196) sources may be a function of signal or concentration or other factors.…”

Section: Random Errors and Noisementioning

confidence: 99%

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Fitting Straight Lines with Replicated Observations by Linear Regression. III. Weighting Data

Asuero

González

2007

Critical Reviews in Analytical Chemistry

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The purpose of this article is to stress the importance of weighting in fitting straight lines with replicated observations. Nevertheless, single response data are also taken into account. Although the concept of weighting is treated on chemometric texts, a detailed procedure is not given. For this reason the present review covers the information concerning this topic. Ignoring non-constant variance (heterocedasticity) often leads to improper estimation and inference in a statistical model which quantifies a given relationship. There are two main solutions to remedy this problem: transform the data or perform a weighted least-squares regression analysis. Weighting with replication in homocedastic and heterodedastic condition, including transformation depending weights, and normalization of the weights are considered. Weighting of observations, however, presents a more difficult problem that has commonly been recognized. The review covers briefly topics as random errors and noise, modelling the variance as a function of the independent variable and variation of precision with concentration. By transforming variable it is possible to introduce non-linear terms to the mathematical framework of linear regression, in order to improve fit as to satisfy the necessary assumptions such as homocedasticity. However, transformation data, the analysis of variance and summary data analysis will be the subject of a future report. A number of applications concerning the uses of weighting in analytical chemistry and weighted linear regression are given in tabular form. The analytical, pharmaceutical, biochemical and clinical literature has been thoroughly revised.

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“…Richard Horne author, www.visualizationsoftware.com) and processed using Praat phonetics software (Version 4.0.26, Paul Boersma and David Weenink authors, University of Amsterdam, The Netherlands, www.praat.org). While chromatographic data are often collected at 500 ms intervals by this speech-based software, one pass of a 20 point moving average digital filter will typically remove most of any resulting high frequency signals as desired without distorting peak shape [29,31].…”

Section: Experimental 21 Instrumentationmentioning

confidence: 99%

gAn improved interface for universal acoustic flame detection in modified supercritical fluid chromatography

Mah¹,

Thurbide²

2008

J of Separation Science

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An improved interface for universal acoustic flame detection in modified supercritical fluid chromatography A novel method of interfacing the acoustic flame detector (AFD) with modified supercritical fluid chromatography (SFC) is presented. By applying resistive heating directly to the burner region between the restrictor outlet and the acoustic flame, infrequent severe noise, baseline drifting, and peak deformations that can occasionally be observed with the AFD are eliminated. For example, by increasing the interface temperature only a few hundred degrees Celsius, such sporadic noise in the detector can be reduced nearly ten-fold resulting in smooth stable operation of the AFD. Further, for various levels of methanol modified supercritical carbon dioxide mobile phase examined, the interface was observed to reduce detector noise in each to a common minimal range near 10 -25 Hz when an appropriate temperature was achieved. The method is simply assembled, inexpensive to construct, and robust in its daily operation. Overall, the heated interface developed and presented facilitates reliable AFD operation in modified SFC, and supports further exploration and implementation of this sensor as an alternative universal detector in separations requiring an organic cosolvent in the mobile phase.

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Noise, filters and detection limits

Cited by 19 publications

References 19 publications

Enhancing the Limit of Detection for Comprehensive Two-Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

Enhancing the Limit of Detection for Comprehensive Two-Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

Fitting Straight Lines with Replicated Observations by Linear Regression. III. Weighting Data

gAn improved interface for universal acoustic flame detection in modified supercritical fluid chromatography

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