A comparison of fast destruction methods for the determination of trace metals in biological materials

In clinical chemistry it can be very useful to have a rapid method for the determination of trace elements in biological material available. Because the classical wet digestion method which is generally used for the destruction of biological materials requires much attention, two procedures were tried which should be less time consuming or require less attention. The two new procedures which are the soluene (a quaternary ammonium hydroxide) method of Jackson (1) and the method of Adrian (2) utilizing pressure were applied to the determination of Cu and Zn in human brain tissue and in fish meal by atomic absorption spectroscopy

Application of linear mixed effects models to the evaluation of dissolution profiles

The performance of linear mixed effects models for the comparison of dissolution profiles is examined. This type of model is frequently used by statisticians, but is rather unknown to people that work in dissolution laboratories. Hence, an extensive theoretical part was introduced to make the methodology more accessible. Firstly, repeated measures ANOVA is discussed, followed by the 'real' linear mixed effects models. The theory is applied to two types of dissolution data: one corresponding to an immediate and another to a slow release formulation. We tried to use as much as possible the standard settings of the statistical software (S-plus). Suggestions are given to solve problems encountered during model fitting. It was found that the statistical limits are much more discriminative than the similarity factor

Measurement uncertainty from validation and duplicate analysis results in HPLC analysis of multivitamin preparations and nutrients with different galenic forms

An approach to calculate the measurement uncertainty in the HPLC analysis of several hydro- and liposoluble vitamins in multivitamin preparations with different galenic composition and properties is described. In the first instance it is examined if duplicate analysis results, obtained with a fully validated analysis method on different lots of an effervescent tablet preparation spread over several points of time, might contribute to calculate the measurement uncertainty of the HPLC method used and if the established uncertainty is acceptable in the assessment of compliance with the legal content limits. Analysis of variance (ANOVA) and precision calculations, based on the ISO 5725-2 norm are applied on the analysis results obtained to estimate precision components, necessary to derive the measurement uncertainty. In the second instance it is demonstrated to which extent the fully validated method of analysis for effervescent tablets is applicable to other galenic forms as e.g. capsules with oily emulsions, tablets, coated tablets, oral solutions, em leader and which specific modifications in the analysis steps are involved. By means of duplicate analysis results, acquired from a large series of real samples over a considerable period of time and classified according to their similarity in content, galenic forms and matrices, estimations of measurement uncertainty calculations are shown</p

Comparison of methods for the estimation of statistical parameters of censored data

Approaches based on the maximum likelihood (ML) method and on the order statistics are described and evaluated for the estimation of the mean and standard deviation of a normal population from a left-singly censored sample, i.e. a sample for which some measurement results fall below the reporting limit of the analytical method. The performance of the methods is evaluated by means of data simulations. The sample size considered is small to moderate: N=6–18. Simulation data show that the ML method performs better than the method based on order statistics, especially in difficult situations, e.g. large expected censored proportion hex (hex≥50%) and for small sample size (N=6). The reliability of the estimates depends on the censored proportion. The larger the censored proportion, the poorer the quality of the estimates. When the expected censored proportion does not exceed 50%, i.e. when the true mean μ of the measurement results is above the reporting limit, the performance of the ML method in the estimation of the mean of a censored sample is very acceptable, i.e. it is comparable to that using classical moment calculation on a complete (non-censored) sample. When the expected censored proportion is very high (e.g. 83%) the estimates are, as expected, largely biased. The performance of the ML method in the estimation of the standard deviation of censored data is not as good as in the estimation of the mean. A formula is given for the approximate sample size required to have a specified confidence level that a ML estimated mean for the censored sample will not differ from the true mean by a certain magnitude

Non-linear mixed effects models for the evaluation of dissolution profiles

The use of non-linear mixed effects models to describe dissolution data has been evaluated, A theoretical part is included to introduce this approach to scientists who are not Familiar with this type of statistics. The standard settings of the statistical software package (S-plus) are used as much as possible. Several mathematical functions like the Weibull, logistic, first-order and Gompertz are employed as basis for the non-linear mixed effects models. Examples are given using dissolution data of immediate and extended release tablets, The results are compared with those obtained using linear mixed effects models

Deepest regression in analytical chemistry

Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. The deepest regression (DR) is a method for linear regression which is defined as the fit with the best depth relative to the data. In this paper we explain the properties of the DR and give some applications of DR in analytical chemistry which involve regression through the origin, polynomial regression, the Michaelis–Menten model, and censored responses.status: publishe

Deepest regression in analytical chemistry

Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. The deepest regression (DR) is a method for linear regression which is defined as the fit with the best depth relative to the data. In this paper we explain the properties of the DR and give some applications of DR in analytical chemistry which involve regression through the origin, polynomial regression, the Michaelis-Menten model, and censored responses. © 2001 Elsevier Science B.V. All rights reserved.status: publishe

The mean and standard deviation of data, some of which are below the detection limit: An introduction to maximum likelihood estimation

In this article, the principle of maximum likelihood estimation (MLE) is introduced. It is illustrated by an application of the maximum likelihood method for the estimation of the mean and standard deviation of a single censored data set. This is a data set for which some data are only known to be below a lower limit (left-censored) or above an upper limit. An example of the determination of an impurity in a raw material, where the measurements are carried out around the detection limit and some fall below it, is given to illustrate the application of MLE to a single censored data set