Linear calibrations in chromatography: The incorrect use of ordinary least squares for determinations at low levels, and the need to redefine the limit of quantification with this regression model. Issue 13 (30th April 2020)

• Record Type: Journal Article
• Authors: Sanchez, Juan M.
• Is Part Of: Journal of separation science. Volume 43:Issue 13(2020)
• Page Start: 2708
• Page End: 2717
• Publication Date: 2020-04-30
• Subjects: accuracy profiles -- analytical calibrations -- quantification limit -- least squares; Separation (Technology) -- Periodicals; Chromatographic analysis -- Periodicals; 543.089
• DOI: 10.1002/jssc.202000094
• Languages: English
• ISSNs: 1615-9306

Abstract

Abstract: Ordinary least squares is widely applied as the standard regression method for analytical calibrations, and it is usually accepted that this regression method can be used for quantification starting at the limit of quantification. However, it requires calibration being homoscedastic and this is not common. Different calibrations have been evaluated to assess whether ordinary least squares is adequate to quantify estimates at low levels. All calibrations evaluated were linear and heteroscedastic. Despite acceptable values for precision at limit of quantification levels were obtained, ordinary least squares fitting resulted in significant and unacceptable bias at low levels. When weighted least squares regression was applied, bias at low levels was solved and accurate estimates were obtained. With heteroscedastic calibrations, limit values determined by conventional methods are only appropriate if weighted least squares are used. A "practical limit of quantification" can be determined with ordinary least squares in heteroscedastic calibrations, which should be fixed at a minimum of 20 times the value calculated with conventional methods. Biases obtained above this "practical limit" were acceptable applying ordinary least squares and no significant differences were obtained between the estimates measured using weighted and ordinary least squares when analyzing real‐world samples.