Robustness testing overview

Table 1 gives an overview of potential factors to be considered when performing a robustness test on a capillary electrophoretic (CE) method. In references 6, 9, 17, and 19, lists of factors for high-performance liquid chromatographic (HPLC), gas chromatographic (GC), and/or thin-layer chromatographic (TLC) methods, can be found. 

In a robustness test the following steps can be identified (a) identification of the variables to be tested, (b) definition of the different levels for the variables, (c) selection of the experimental design, (d) definition of the experimental protocol, (e) definition of the responses to be determined, (f) execution of the experiments and determination of the responses of the method, (g) calculation of effects, (h) statistical and/or graphical analysis of the effects, and (i) drawing chemically relevant conclusions from the statistical analysis and, if necessary, taking measures to improve the performance of the method. A general overview of robustness testing can be found in [35). 

Table 2.1 presents an overview of factors that can potentially be considered for optimization and robustness testing of CE methods. Lists of commonly used electrolytes/buffers (20-23) or additives (20) and characteristic properties of frequently applied solvents and surfactants (20) can be found in the literature. Sample concentration (see Table 2.1) is a factor occasionally included. However, the aim of the analytical method is to estimate this concentration through the measured signal, from a calibration procedure. In method optimization, responses related to the quality of the separation, for example, resolutions, are considered, and in this situation one can verify whether the sample... 

Neuronal networks are nowadays predominantly applied in classification tasks. Here, three kind of networks are tested First the backpropagation network is used, due to the fact that it is the most robust and common network. The other two networks which are considered within this study have special adapted architectures for classification tasks. The Learning Vector Quantization (LVQ) Network consists of a neuronal structure that represents the LVQ learning strategy. The Fuzzy Adaptive Resonance Theory (Fuzzy-ART) network is a sophisticated network with a very complex structure but a high performance on classification tasks. Overviews on this extensive subject are given in [2] and [6]. 

Berger JO. 1994. An overview of robust Bayesian analysis [with Discussion]. Test 3 5-124. 

This book consists of eight chapters. Chapters 2, 3 and 4 give methodological background and reviews. Chapters 5 to 8 carry the applications both in the field of analytical chemistry and pharmaceutical formulations. Since the field of which this book tries to give an overview is still under active research, this book is by no means a monograph with well established and tested methods. There are still a lot of questions and open ends. This book, however, does give some ideas how to tackle the problems of robustness. 

The methods of robust statistics have recently been used for the quantitative description of series of measurements that comprise few data together with some outliers [DAVIES, 1988 RUTAN and CARR, 1988]. Advantages over classical outlier tests, such as those according to DIXON [SACHS, 1992] or GRUBBS [SCHEFFLER, 1986], occur pri-marly when outliers towards both the maximum and the minimum are found simultaneously. Such cases almost always occur in environmental analysis without being outliers in the classical sense which should be eliminated from the set of data. The foundations of robust statistics, particularly those of median statistics, are described in detail by TUKEY [1972], HUBER [1981], and HAMPEL et al. [1986] and in an overview also by DANZER [1989] only a brief presentation of the various computation steps shall be given here. 

Spectroscopic methods can provide fast, non-destructive analytical measurements that can replace conventional analytical methods in many cases. The non-destructive nature of optical measurements makes them very attractive for stability testing. In the future, spectroscopic methods will be increasingly used for pharmaceutical stability analysis. This chapter will focus on quantitative analysis of pharmaceutical products. The second section of the chapter will provide an overview of basic vibrational spectroscopy and modern spectroscopic technology. The third section of this chapter is an introduction to multivariate analysis (MVA) and chemometrics. MVA is essential for the quantitative analysis of NIR and in many cases Raman spectral data. Growth in MVA has been aided by the availability of high quality software and powerful personal computers. Section 11.4 is a review of the qualification of NIR and Raman spectrometers. The criteria for NIR and Raman equipment qualification are described in USP chapters <1119> and < 1120>. The relevant highlights of the new USP chapter on analytical instrument qualification <1058> are also covered. Section 11.5 is a discussion of method validation for quantitative analytical methods based on multivariate statistics. Based on the USP chapter for NIR <1119>, the discussion of method validation for chemometric-based methods is also appropriate for Raman spectroscopy. The criteria for these MVA-based methods are the same as traditional analytical methods accuracy, precision, linearity, specificity, and robustness however, the ways they are described and evaluated can be different. 

Most consumers believe the price paid for a product is directly related to its inherent level of quality. With decorated plastics, how robust the product finish is before it becomes damaged or worn out is an essential element of this perceived quality. This paper presents an introduction to the mechanisms of surface wear and scratch damage, and the importance of conducting controlled laboratory tests. An overview of several commercially available instruments is offered including suggestions on how to recreate and measure real-world damage. 

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