Error estimates on normal least squares linear regression with replicate injection of calibration standards. (August 2012)

• Record Type: Journal Article
• Authors: Huang, Chaoyang; Ammerman, Jennifer; Connolly, Paul; de Lisio, Pat; Wright, Daniel
• Is Part Of: Bioanalysis. Volume 4:Number 16(2012)
• Page Start: 1979
• Page End: 1987
• Publication Date: 2012-08
• Subjects: Drugs -- Analysis -- Periodicals; 615.19005
• DOI: 10.4155/bio.12.170
• Languages: English
• ISSNs: 1757-6180

Abstract

Background: In bioanalysis, including replicate injection of calibration standards, a normal least squares linear regression model can be generated from, A: the first injection dataset; B: the second injection dataset; C: averaged data from both injection datasets; and D: all data from both injections. Sample results, and their estimated confidence intervals, are expected to be different among these four models.Results: Models C and D yield same slopes and intercepts, which are the mathematical means of respective values in models A and B. Relatively narrower confidence intervals on sample results are estimated in both models C and D as the former reduces the overall standard error of the curve and the latter increases the total number of calibration points and leads to a lower Student's t-value.Conclusion: Replicate injection of calibration standards provides added benefits for an analytical measurement on instrument sensitivity compensation and relatively improved precision of results.