Method performance terms calibration curve

The terms detective and detectivity rather than the frequently (mis)used terms sensitive and sensitivity, are used to denote analyte detection capability of the method. For a given method, although usually higher sensitivity (slope of calibration curve) is synonymous with greater detectivity, (lower detection limit), this is not invariably so, and the latter is the more accurate term. In addition, for performance comparisons among different methodologies, sensitivities cannot usually be compared due to the typically different response units inherent in the methods comparisons, however, of detectivities, if expressed as, e.g., mg of analyte kg-1 of sample, can be made. 

Arun et al. [30] developed and validated a rapid high-performance liquid chromatographic method for the estimation of buclizine hydrochloride in tablet dosage form. The stationary phase used was precoated silica gel 60 F254. The mobile phase used was a mixture of methanol chloroform ammonia (8 1 1). The detection of spots was carried out at 234 nm. The method was validated in terms of linearity, accuracy, precision, and specificity. The calibration curve was linear between 100 and 700 ng/spot. The limit of detection and the limit of quantification were 20 and 100 ng/spot, respectively. The method can be used to determine the drug content of tablet dosage formulation. 

Option (Valid) presents a graph of relative standard deviation (c.o.v.) versus concentration, with the relative residuals superimposed. This gives a clear overview of the performance to be expected from a linear calibration Signal = A + B Concentration, both in terms of (relative) precision and of accuracy, because only a well-behaved analytical method will show most of the residuals to be inside a narrow trumpet -like curve this trumpet is wide at low concentrations and should narrow down to c.o.v. = 5% and rel. CL = 10%, or thereabouts, at medium to high concentrations. Residuals that are not randomly distributed about the horizontal axis point either to the presence of outliers, nonlinearity, or errors in the preparation of standards. 

The review and discussion of existing methods sets out the rationale for a combined method, called FaMlVE (Failure Mechanism Identification and Vulnerability Evaluation), and developed by the first author and her co-researchers, in the past decade. The hypotheses and analytical approach to derive capacity curves and fragility functions are presented in detail in section Development of Analytical Methods FaMIVE. Results obtained from the FaMIVE method are presented in section Damage Thresholds and Performance Indicators in terms of damage limit states and drift and compared with the recommendation of Eurocode 8 (ECS) (CEN 2005) and experimental evidence to discuss issues of validation and calibration. 

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