Optimisation analytical methods

Reports of on-line SFE-FIPLC are rare, perhaps because the majority of analytes that have been extracted using SFE can be separated using either GC or SFC. On-line SFE-HPLC is often used to monitor extraction efficiencies. SFE-HPLC optimised for temperature (120 °C), pressure (384 bar), SCF flow and modifier (methanol) has been used for the quantification of Irganox 1010 and Irgafos 168 extracted from PP. In this case Thilen and Shishoo [12] varied three SFE parameters for optimisation of the extraction efficiency, and five parameters for the collection efficiency, see Figures 7.7 and 7.8. Despite these efforts, low recoveries were observed (Table 7.16). This was attributed to problems associated with the compounding process, and not to uncertainties in the extraction and analytical method. 

The first publications on SFE of APEO were discussed in a review on analytical methods for APEO [42]. For the determination of alkylphe-nols in sewage sludge and sediment, a SFE technique was optimised, using C02 at 80°C and 351 atm, at a flow rate of 2 mL min-1. Extraction times were 15 min static and 10 min dynamic, with a sample intake of 0.1-1 g [41]. In this method, in situ acetylation of the alkylphenols using acetic anhydride was performed. The extract was washed with an aqueous K2C03 solution to remove co-extracted acetic acid, and cleaned up using 5% deactivated silica. 

B. Yan, Chapter 3 Solid-Phase Reaction Optimisation Using FTIR Methods, in Analytical Methods in Combinatorial Chemistry, Technomic, Pennsylvania,... 

However, the actual change observed when measured against the method value would be much more significant. This problem is widespread in analytical methods particularly for factors that are optimised and thus changes to either side of the method value are likely to cause deterioration in the method s performance. 

A systematical approach of sample preparation methods and optimisation of the quality aspects of sample preparation may enhance the efficiency of total analytical methods. This approach may also enhance the quality and knowledge of the methods developed, which actually enhances the quality of individual sample analyses. Unfortunately, in bioanalysis, systematical optimisation of sample preparation procedures is not common practice. Attention to systematical optimisation of assay methods has always been mainly on instrumental analyses problems, such as minimising detection limits and maximising resolution in HPLC. Optimisation of sample extraction has often been performed intuitively by trial and error. Only a few publications deal with systematical optimisation of liquid-liquid extraction of drugs from biological fluids [3,4,5]. 

Figure 3.8 shows an example dataset of mixed hydrocarbons used as a petrochemical feedstock. These are straight-run naphthas which consist of a wide range of alkane, alkene, aromatic and naphthenic hydrocarbons, mainly in the range of C4-C9. The conventional analytical method for naphtha analysis is temperature-programmed gas chromatography (GC), which can provide a full analysis including C-number breakdown, but which is rather slow for process optimisation purposes. 

The ability to monitor changes in the concentration of species over the course of a reaction is central to any mechanistic investigation, and time spent in selecting or developing an appropriate method is inevitably repaid both in terms of mechanistic understanding and in yield optimisation (see Chapters 2 and 3). Ideally, an analytical method permits continuous... 

Gardiner PE, Stoeppler M. 1987. Optimisation of the analytical conditions for the determination of aluminum in human blood plasma and serum by graphite furnace atomic absorption spectrometry. Part 2. Assessment of the analytical method. J Anal Atom Spectrom 2 401-404. 

Therefore, for some well-known matrices and some perfectly stable substances or elements, common sense is often better than any measurement. Also linked to the precision of the analytical method, it is always better to look for the appearance of degradation products with a method with optimised sensitivity rather than checking for the disappearance of few percent of a highly concentrated substance. Unfortunately, degradation products are not always known or they may already exist naturally in the sample at high concentration levels. Nevertheless, it is sometimes possible to find cases where degradation products are of help e.g. degradation of p,p -DDT into p,p -DDE in BCR-CRM 115 [12] and in BCR-CRM 598 [14] or arsenobetaine into dimethyl arsinic acid in BCR-CRM 627 [1,2]. 

The optimisation of an analytical method based in GCxGC is perhaps one of the most important problems associated with this technique. The number of operation conditions to be optimised is higher in GCxGC than in ID GC, but the main difficulties are the effect on the D separation of any change in the D conditions, and the high number of possibilities offered by the different two-columns sets. 

Analytical methods provide exact solutions that allow for direct analysis of the influence of experimental variables and the determination of the conditions for particular behaviours such as the achievement of a steady-state signal. Nevertheless the use of analytical methods is not always feasible due to the complexity of the problems. In such cases numerical methods offer a very accurate approximation to the true solution once the conditions of the simulation are optimised. 

Quite surprisingly, the number of papers using simplex methods to optimise analytical procedures is not so large and, therefore, our review started at 1990 (see Table 3.35). For older references on simplex applications see Grotti and Wienke et... 

M. B. Dessuy, Multivariate optimisation and validation of an analytical method for the determination of cadmium in wines employing ETAAS, /. Brazil. Chem. Soc., 2009, 20(4), 788-794. 

During the last two or three decades atomic spectroscopists have become used to the application of computers to control their instruments, develop analytical methods, analyse data and, consequently, to apply different statistical methods to explore multivariate correlations between one or more output(s) e.g. concentration of an analyte) and a set of input variables e.g. atomic intensities, absorbances). On the other hand, the huge efforts made by atomic spectroscopists to resolve interferences and optimise the instrumental measuring devices to increase accuracy and precision have led to a point where many of the difficulties that have to be solved nowadays cannot be described by simple univariate linear regression methods (Chapter 1 gives an extensive review of some typical problems shown by several atomic techniques). Sometimes such problems cannot even be addressed by multivariate regression methods based on linear relationships, as is the case for the regression methods described in the previous two chapters. 

As yet, there are no generally accepted formats for the overall method development of in-polymer additive analysis. However, one may take a lead from the work of Swartz et al. [6], and various other sources [20,80,87,128], who have presented a rationale for the process of successful development of (HPLC-based) analytical methods, their optimisation, and eventually validation. A sequence of steps is necessary in the development of a fully validated method for the analysis of additives in polymeric matrices, in which the user has specified validation parameters and limits, as follows ... 

Carlson R (1992) Design and optimization of organic synthesis. Elsevier, Amsterdam Box GEP (1952) Statistical design in the study of analytical methods. Analyst 77 879-889 Cochran WG, Cox GM (1957) Experimental designs. WUey, New York Small TS, Fell AF, Coleman MW, Berridge JC (1995) Central composite design forthe rapid optimisation of ruggedness and chiral separation of amlodipine in capillary electrophoresis. Chirahty 7 226-234... 

A drawback of the SCRF method is its use of a spherical cavity molecules are rarely exac spherical in shape. However, a spherical representation can be a reasonable first apprc mation to the shape of many molecules. It is also possible to use an ellipsoidal cavity t may be a more appropriate shape for some molecules. For both the spherical and ellipsoi cavities analytical expressions for the first and second derivatives of the energy can derived, so enabling geometry optimisations to be performed efficiently. For these cavil it is necessary to define their size. In the case of a spherical cavity a value for the rad can be calculated from the molecular volume ... 

D. L. Massart, A. Dijkstra, and L. Kaufman, Evaluation and Optimisation of Eaboratoy Methods and Analytical Procedures, Elsevier Science Publishing Co., Inc.,... 

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