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Title

Assessment of goodness-of-fit for the main analytical calibration models: Guidelines and case studies

AuthorsRaposo Bejines, Francisco CSIC ORCID ; Barceló, Damià CSIC ORCID
KeywordsBack-calculated concentration
Calibration
Determination coefficient
Goodness-of-fit
Least squares regression
Linear regression
Quadratic regression
Relative error
Weighted regression
Issue DateOct-2021
PublisherElsevier
CitationTRAC - Trends in Analytical Chemistry 143: 116373 (2021)
AbstractThis critical review paper will discuss the main analytical calibration models as well as the guidelines for their practical use. The main models used to fit a multiple-point calibration dataset are: 1) linear unweighted or ordinary least squares regression (OLSR); 2) quadratic unweighted least squares regression (QLSR); 3) linear weighted least squares regression (WLSR). Unfortunately, there is no standard procedure in analytical chemistry for objectively testing the goodness-of-fit of calibration models. Different proposals were reported in the literature. However, none is more commonly used, and probably not more controversial than R2. In this document, a three step simple calibration diagnosis has been proposed. It is based on a combination of different procedures such as graphical plots, statistical significance tests and numerical parameters. Experimental conditions and design of calibration procedures are very relevant for appropriate selection. Finally, some information on the choice of the different models will be reported in four case studies.
Publisher version (URL)https://doi.org/10.1016/j.trac.2021.116373
URIhttp://hdl.handle.net/10261/247394
DOI10.1016/j.trac.2021.116373
Appears in Collections:(IDAEA) Artículos




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