Ensuring both normality and homocedasticity of chromatographic data-ratios for internal-standard least-squares calibration

Abstract

Because the quotient of two normal variables does not fit a normal distribution and tends to be heterocedastic, the use of internal-standard least-squares regression on data obtained from such ratios gives a calibration function which can result in inaccurate determination of the concentration of analyte in a sample. In this work we have studied simulated situations in which area-ratios are fitted to a normal distribution. It is shown that the least-squares model is inappropriate when the ordinate data-ratios are not normally distributed, and some recommendations are proposed to ensure both normality and homocedasticity of the quotient of analyte/ internal-standard signals. Finally, GC-MS separation and quantification of nine fatty acids by use of an internal standard are used for experimental verification of the conclusions reached. 550

Development and validation of a method for the analysis of Ochratoxin A in roasted coffee by liquid chromatography/electrospray-mass spectrometry in Tandem (LC/ESI-MS/MS)

“…38 For homoscedasticity, the results from the calibration curve were submitted to a Cochran's test to verify that random errors in the instrumental signal measurements are constant and independent of the predicted value. 41 The limit of quantification (LOQ) was determined from the lowest concentration of the working range (3.0 ng g -1 ). The chromatograms of samples that produced a signal-to-noise ratio equal to ten were considered as the LOQ.…”