Bootstrap error-adjusted single-sample

The qualification concept was used by Wargo and Drennen to verify the homogeneity of solid mixtures. Qualitative analytical algorithms based on the Bootstrap Error-adjusted Single-sample Technique (BEST) proved sensitive to variations in sample homogeneity. Plugge and van der Vlies converted NIR spectra into polar coordinates and used them to calculate the Mahalanobis distance in order to identify nonhomogene-ous samples. 

In a 1988 paper, Lodder and Hieftje used the quantile-BEAST (bootstrap error-adjusted single-sample technique) [77] to assess powder blends. In the study, four benzoic acid derivatives and mixtures were analyzed. The active varied between 0 and 25%. The individual benzoic acid derivatives were classified into clusters using the nonparametric standard deviations (SDs), analogous to SDs in parametric statistics. Ace-tylsalicylic acid was added to the formulations at concentrations of 1 to 20%. All uncontaminated samples were correctly identified. Simulated solid dosage forms containing ratios of the two polymorphs were prepared. They were scanned from 1100 to 2500 nm. The CVs ranged from 0.1 to 0.9%. 

In NIR calibration, the introduction of unexpected components (e.g., incorrect chemicals or contaminated raw materials) into a sample may go undetected, causing erroneous results. This type of false sample is potentially more serious than samples in which the correct constituents are present in the wrong concentrations. In a 1988 paper, Lodder and Hieftje [33] discussed in detail the application of a new algorithm, the quantile-BEAST (Bootstrap Error-Adjusted Single-sample Technique), which was more sensitive to false sample detection. The quantile-BEAST was proposed as a new method to assess pharmaceutical powder blends qualitatively. 

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