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Linear regression for calibration lines revisited: weighting schemes for bioanalytical methods
Open accessAbstract
When the assumption of homoscedasticity is not met for analytical data, a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line is to use weighted least squares linear regression (WLSLR). The purpose of the present paper is to stress the relevance of weighting schemes for linear regression analysis and to show how this approach can be useful in the bioanalytical field. The steps to be taken in the study of the linear calibration approach are described. The application of weighting schemes was shown by using a high-performance liquid chromatography method for the determination of lamotrigine in biological fluids as a practical example. By using the WLSLR, the accuracy of the analytical method was improved at the lower end of the calibration curve. Bioanalytical methods data analysis was improved by using the WLSLR procedure.
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Cites
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Cited by
Can creatine supplementation form carcinogenic heterocyclic amines in humans?
Pereira, R. T. D. S., Dörr, F. A., Pinto, E., Solis, M. Y., Artioli, G. G., Fernandes, A. L., Murai, I. H., Dantas, W. S., Seguro, A. C., Santinho, M. A. R., et al. (2015) · The Journal of Physiology · doi:10.1113/jp270861 · Cited by 25
Metadata
- Authors
- Almeida, A. M., Castel-Branco, M. M., & Falcão, A. C.
- Venue
- Journal of Chromatography B
- Publisher
- Elsevier BV
- Published
- 2002-07
- Volume
- 774
- Issue
- 2
- Pages
- 215-222
- ISSN
- 1570-0232 (ISSN-L)
- OpenAlex
- W1989671297
- Cited by
- 607
- References
- 7
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- green
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- Almeida, A. M., Castel-Branco, M. M., & Falcão, A. C. (2002). Linear regression for calibration lines revisited: weighting schemes for bioanalytical methods. Journal of Chromatography B. https://doi.org/10.1016/s1570-0232(02)00244-1
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
