Correction techniques for long-term studies in analytical chemistry

There is a new approach to data processing of long-term studies (which are mostly studies of population samples of specimens) in analytical chemistry. Specimens of each sample are analyzed in several runs of the analysis. In addition to specimens each run analyzed from 2 to 4 reference materials with different concentration of substances to determine and at least two measurements for every material are done. These materials are distributed randomly among specimens under study. The statistical models of errors of reference materials study results are used for the recalibration (corrective action) of population sample study result. This approach is applied to the determination of lipids (total cholesterol, triglycerides and cholesterol of high density lipoproteins) in human sera from subjects in the population studies in Tashkent which made up 12 samples of several hundreds specimens in each. Standard reference materials were the donorʹs frozen serum certified by reference methods. Data obtained appeared to be well described on the basis of linear models of the analysis of covariance and not so well by the model of the after-scaling analysis of variance. Correction results on the basis of both models were compared with the run-wise and sample-wise result correction. Change in the coefficient of variation of the sample was considered as the criterion of availability for service and effectiveness of the correction. In most cases run-wise calibration on the basis of either model was the most suitable. The analysis of covariance model turned out not to be suitable in determining cholesterol of high density lipoproteins because of unsuccessful study design. On the whole, this method proved effective and led to the improvement of the population sample studies results. Although corrective action within after-scaling analysis of variance model is not always as effective as analysis of covariance model but it is more steady. It can be expected that this approach will prove to be useful in long-term studies in other areas of analytical chemistry.