Optimisation chemometrics used

Although application of chemometrics in sample preparation is very uncommon, several optimisation techniques may be used to optimise sample preparation systematically. Those techniques can roughly be divided into simultaneous and sequential methods. The main restrictions of a sequential simplex optimisation [6,7] find their origin in the complexity of the optimisation function needed. This function is a predefined function, often composed of several criteria. Such a composite criterion may lead to ambiguous results [8]. Other important disadvantages of simplex optimisation methods are that not seldom local optima are selected instead of global optima and that the number of experiments needed is not known beforehand. 

Optimisation methods may also be used to maximise key parameters, e.g. resolution, but are beyond the scope of this handbook. Miller and Miller s book on Statistics for Analytical Chemistry provides a gentle introduction to the topic of optimisation methods and response surfaces as well as digestible background reading for most of the statistical topics covered in this handbook. For those wishing to delve deeply into the subject of chemometric methods, the Handbook of Chemometrics and Qualimetrics in two volumes by Massart et al., is a detailed source of information. 

In order to overcome, or at least minimise, such drawbacks we can resort to the use of chemometric techniques (which will be presented in the following chapters of this book), such as multivariate experimental design and optimisation and multivariate regression methods, that offer great possibilities for simplifying the sometimes complex calibrations, enhancing the precision and accuracy of isotope ratio measurements and/or reducing problems due to spectral overlaps. 

A. M. Carro, I. Neira, R. Rodil and R. A. Lorenzo, Speciation of mercury compounds by gas chromatography with atomic emission detection. Simultaneous optimisation of a headspace solid-phase microextraction and derivatisation procedure by use of chemometric techniques, Chro-matographia, 56(11/12), 2002, 733-738. 

To shed light on the mechanism of formation of silsesquioxane a7b3, to identify the species formed during the process, and to try to explain the high selectivity towards structure a7b3 of the optimised synthetic method described above (64% yield in 18 h), the synthesis of cyclopentyl silsesquioxane a7b3 was monitored by electrospray ionisation mass spectrometry (ESI MS) [50-52] and in situ attenuated total reflection Fourier-transform infrared (ATR FTIR) spectroscopy [53, 54]. Spectroscopic data from the latter were analysed using chemometric methods to identify the pure component spectra and relative concentration profiles. 

Other new strategies in modem method development include the use of automation, computer software to optimise separations, and statistical and chemometric tools to enhance existing approaches. The application of these method development tools will be discussed in ihe final sections of this chapter. 

Chemometrics has been defined in some texts [155] as the entire process whereby data are transformed into information used for decision-making. It is this definition that is the most applicable to separation sciences, more specifically in method development and optimisation in liquid chromatography. In this example, chemometrics has been used to predict optimum separation conditions based on empirical data and other separation information. Chemometric approaches to method development can be based on either sequential simplex models [156] or simultaneous fixed factorial designs [157] or interactive mixture designs [158] which combine the advantages of simultaneous and simplex models. 

Other recently developed chemometric approaches include Heuristic evolving latent projections (HELP) [162] which is primarily used for the analysis of qualitative and quantitative data from 2-dimensional data (e.g. HPLC DAD) used for complex mixtures, and HELP lessens the demand on developing complicated separation methods mixed level orthogonal array design (ML-OAD) has been used to optimise the parameters involved in HPLC detection [163]. The LC methods for determining the purity of tetracycline HCl and resolution... 

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