An approach to detection capabilities estimation of analytical procedures based on measurement uncertainty

• Published in: Accreditation and quality assurance Vol. 15; no. 1; pp. 19 - 28
• Main Authors: Jiménez-Chacón, Juan, Alvarez-Prieto, Manuel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.01.2010; Springer; Springer Nature B.V
• Subjects: Analytical Chemistry; Bias; Biochemistry; Blanks; Calibration; Chemistry; Chemistry and Materials Science; Commercial Law; Ecotoxicology; Estimation; Exact sciences and technology; Food Science; General Paper; Marketing; Probability distribution; Random errors; Uncertainty; Validation; Detection capabilities; Critical concentration; Detection limit; Uncertainty modelling; Uncertainty; Bias; Estimation; Use; Performance characteristic; Random error; Concentration; Experimental study; Detection
• ISSN: 0949-1775; 1432-0517
• DOI: 10.1007/s00769-009-0608-6

Detection capabilities are important performance characteristics of analytical procedures. There are several conceptual approaches on the subject, but in most of them a level of ambiguity is presented. It is not clear which conditions of measurements should be used, and there is a relative lack of definition concerning blanks. Moreover, there are no systematic experimental studies concerning the influence of uncertainty associated with bias evaluation. A new approach based on measurement uncertainty is presented for estimating quantities that characterize capabilities of detection. It can be applied to different conditions of measurement and it is not necessary to perform an additional experiment with blanks. Starting from a modelling process of the combined uncertainty of concentration, it is possible to include in the estimated quantities the effects due to random errors and the uncertainty associated to evaluation of bias. The detection capabilities are then compared with the results obtained using some other relevant approaches. Slightly higher values were obtained with the measurement uncertainty approach due to inclusion of uncertainty associated with bias.