CORRECTION TECHNIQUES FOR LONG-TERM STUDIES IN ANALYTICAL-CHEMISTRY

Authors
Citation
Vi. Dvorkin, CORRECTION TECHNIQUES FOR LONG-TERM STUDIES IN ANALYTICAL-CHEMISTRY, Chemometrics and intelligent laboratory systems, 34(2), 1996, pp. 173-185
Citations number
27
Categorie Soggetti
Computer Application, Chemistry & Engineering","Instument & Instrumentation","Chemistry Analytical","Computer Science Artificial Intelligence","Robotics & Automatic Control
ISSN journal
01697439
Volume
34
Issue
2
Year of publication
1996
Pages
173 - 185
Database
ISI
SICI code
0169-7439(1996)34:2<173:CTFLSI>2.0.ZU;2-2
Abstract
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 th e analysis. In addition to specimens each run analyzed from 2 to 4 ref erence materials with different concentration of substances to determi ne and at least two measurements for every material are done. These ma terials are distributed randomly among specimens under study. The stat istical 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 lipoprotei ns) in human sera from subjects in the population studies in Tashkent which made up 12 samples of several hundreds specimens in each. Standa rd reference materials were the donor's frozen serum certified by refe rence methods, Data obtained appeared to be well described on the basi s of linear models of the analysis of covariance and not so well by th e 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 an d effectiveness of the correction. In most cases run-wise calibration on the basis of either model was the most suitable. The analysis of co variance model turned out not to be suitable in determining cholestero l of high density lipoproteins because of unsuccessful study design, O n the whole, this method proved effective and led to the improvement o f the population sample studies results. Although corrective action wi thin after-scaling analysis of variance model is not always as effecti ve as analysis of covariance model but it is more steady. It can be ex pected that this approach will prove to be useful in long-term studies in other areas of analytical chemistry.