Impurity levels statistics

The acceptance criterion for recovery data is 98-102% or 95-105% for drug preparations. In biological samples, the recovery should be 10%, and the range of the investigated concentrations is 20% of the target concentrations. For trace level analysis, the acceptance criteria are 70-120% (for below 1 ppm), 80-120% (for above 100 ppb), and 60-100% (for below 100 ppb) [2]. For impurities, the acceptance criteria are 20% (for impurity levels <0.5%) and 10% (for impurity levels >0.5%) [30], The AOAC (cited in Ref. [11]) described the recovery acceptance criteria at different concentrations, as detailed in Table 2. A statistically valid test, such as a /-test, the Doerffel-test, or the Wilcoxon-test, can be used to prove whether there is no significant difference between the result of accuracy study with the true value [29],... 

Death and taxes, as the old adage goes, are the only certainties in life. Although this is an overstatement, it does emphasize the uncertain world in which the pharmaceutical scientist lives and works. Faced with estimates of product characteristics such as potency, content uniformity, impurity levels, and dissolution performance, the scientist must make important go/ no-go decisions. These estimated values, as is true for most measured quantities, inherently vary from the true values on which correct decisions depend. Statistical methods provide tools that enable the pharmaceutical scientist to act decisively in an uncertain world. An understanding of basic statistical methods is important for all who work in the pharmaceutical field. 

Because the method had been previously validated for manual sample preparation, validation of the automated process required only the demonstration that equivalent samples were prepared by the two methods. To achieve the validation, triplicate preparations were made of product at four different strengths by both methods and assay and impurity levels compared. For assay, a Westlake interval of 0.7% was calculated to show excellent agreement between the data sets. Impurity levels were similarly shown to be statistically equivalent. Carryover was determined by running 20 samples at the highest concentration followed by a reagent blank. Measurement of Roxifiban in the blank showed carryover to be 0.07%. 

Response surface methodology (RSM) is a method of optimization using statistical techniques based upon the special factorial designs of Box and Behenkini and Box and Wilson.I It is a scientific approach to determining optimum conditions which combines special experimental designs with Taylor first and second order equations. The RSM process determines the surface of the Taylor expansion curve which describes the response (yield, impurity level, etc.) The Taylor equation, which is the heart of the RSM method, has the form ... 

Noise due to carrier number fluctuation is connected with detector bias and is denoted as shot noise or Schottky noise [5]. It is also denoted as the excess noise, but this expression is also sometimes used for 1//noise [62]. It is a consequence of carriers passing through energy barriers, i.e., it appears as a result of the statistic nature of interband transitions and transitions band-impurity level, and in the final instance it is a consequence of the discrete nature of carriers [63]. When carrier number fluctuations are caused by g-r processes, this noise is also denoted as generation-recombination (g-r) noise. 

The TLC method is good enough to in all cases determine that the impurity in question is below 0.5%, the criterion for product release. However, the data cannot be used in a quantitative statistical analysis because several levels of LOQ are involved from <0.5% down to 0.05%. All entries would have to be re-coded as <.5%, with the total loss of information, because otherwise distinctions would be introduced where... 

Compared with conventional impurities measurements, trace analyses caimot be expected to achieve the same linearity and precision values. This is due to the lower signal-to-noise ratios inevitable at low levels. Hence, while the same approaches can be used, greater latitude will be necessary in the acceptance criteria. What must be demonstrated is that the data is statistically valid to show that the levels of toxic analytes are below their specification limits. 

In Fermi-Dirac statistics, g is the Fermi energy Er, which is such that the probability, that a state of energy is occupied, is 1/2. States with energies higher than Et have a smaller probability of being occupied, those with lower energy, a higher probability. The position of the Fermi level in a semiconductor depends markedly on the temperature and on the concentration of impurities. The Fermi levels of two conductors in electrical contact and in thermal equilibrium are the same. 

Levels of three individual degradation products and the total amount of degradation impurities were monitored at timepoints of 1, 2, 4, 8, 12, and 24 weeks. Statistical software was used to analyze the data and derive models for the rates of degradation as a function of the temperature and sample water content. The analysis showed that temperature and water content were both significant in their effect on degradation rate (Table 4). The... 

Simple peak purity analysis is relatively accurate when the impurity is present at significant concentration levels but, as the level of impurity diminishes, its impact on the target analyte spectrum becomes subtler and may require more sophisticated techniques. For this, statistical software routines are available for automated spectral comparisons. In these cases, peak purity determination and analysis of spectral differences are achieved using vector analysis algorithms. The more similar the spectra are, the closer the value is to 0.0° the more spectrally different they are, the larger the value. All the spectral data points across the peak are analyzed the data are converted into vectors, compared, and graphically plotted so that the results can be visualized. These software routines provide both numerical results and graphical representations such as similarity and threshold curves. 

A precise description of the SWNT sorbent is also problematic. In theory, nanotubes should form perfecdy ordered hexagonal bundles, giving a structure as well-defined as zeolites. In practice, nanotubes contain significant quantities of metal catalyst particles, amorphous carbon impurities, and geometric and chemical defects in the nanotubes themselves. Thus, the accurate modeling of gas adsorption on SWNTs is a challenge at all levels. Nevertheless, statistical... 

The dependence of zirconium extraction as Zr(TTA)4 in the presence of chloride and nitrate was determined from two measurements for each ligand. The data were used to indicate that the species formed were ZrCl and ZrNOf. A statistical re-evaluation of the reported data indicate that the stability constants (y0,) for these species are (2.1 0.2) and (1.97 0.14), respectively, or logn,/ , (ZrCT ) = (0.32 0.04) and logio P (ZrNOj ) = (0.29 0.03) the uncertainties have been calculated considering the differences in, and uncertainty of, the data given by the authors and then changed to 95% confidence levels. It is not recommended to include these data in the review for the following reasons (a) the inadequacy in the number of experimental data points (b) the authors believed that the presence of an unknown impurity may have effected the stability constants by as much as 30% and (c) on the basis of other reported data it is likely that not only ZrCP or ZrNOj form in the experimental conditions studied. 

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