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Open AccessArticle

Estimating Detection Limits in Chromatography from Calibration Data: Ordinary Least Squares Regression vs. Weighted Least Squares

Chemistry Department, University of Girona, Aurèlia Capmany 69, 17003 Girona, Spain
Separations 2018, 5(4), 49; https://doi.org/10.3390/separations5040049
Received: 31 July 2018 / Revised: 6 September 2018 / Accepted: 21 September 2018 / Published: 8 October 2018
(This article belongs to the Special Issue Chromatography and Chemometrics)
It is necessary to determine the limit of detection when validating any analytical method. For methods with a linear response, a simple and low labor-consuming procedure is to use the linear regression parameters obtained in the calibration to estimate the blank standard deviation from the residual standard deviation (sres), or the intercept standard deviation (sb0). In this study, multiple experimental calibrations are evaluated, applying both ordinary and weighted least squares. Moreover, the analyses of replicated blank matrices, spiked at 2–5 times the lowest calculated limit values with the two regression methods, are performed to obtain the standard deviation of the blank. The limits of detection obtained with ordinary least squares, weighted least squares, the signal-to-noise ratio, and replicate blank measurements are then compared. Ordinary least squares, which is the simplest and most commonly applied calibration regression methodology, always overestimate the values of the standard deviations at the lower levels of calibration ranges. As a result, the detection limits are up to one order of magnitude greater than those obtained with the other approaches studied, which all gave similar limits. View Full-Text
Keywords: limit of detection; analytical calibration; linear regression; ordinary least squares; weighted least squares limit of detection; analytical calibration; linear regression; ordinary least squares; weighted least squares
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Sanchez, J.M. Estimating Detection Limits in Chromatography from Calibration Data: Ordinary Least Squares Regression vs. Weighted Least Squares. Separations 2018, 5, 49.

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