Chemometrics strategy

The data cube combines spectral and spatial information and therefore includes the requisite statistics for spectral classifications. However, new chemometric strategies have to be applied to interpret chemical imaging results. 

All new descriptors, QSAR approaches and chemometric strategies proposed since 2000 have been included in this handbook. Several new topics such as biodescriptors, characteristic polynomial-based descriptors, property filters, scoring functions, and cell-based methods have been added. Other topics, such as substructure descriptors, autocorrelation descriptors, delocalization degree indices, weighted matrices, connectivity indices, and so on, have been completely rewritten. 

The results obtained so far show the potential of NIR technology to identify the animal species in animal by-products. Further work is in progress to enlarge our special library files and to evaluate different quantitative and qualitative chemometric strategies to build models that can be applied to all the ABPs (animal meals and fats) circulating at intra- and inter-European Community level. 

Chemometrics provides a number of methods to obtain an insight into data sets, and to extract relevant information from them. The typical chemometric strategy consists of the following steps (a) collection of data for known cases (b) generation of a mathematical model which is usually based on multivariate statistics or neural networks (c) interpretation of the model parameters in terms of the underlying chemistry (d) application of the model to new cases. 

Since 1992 a variety of related but much more powerful data-handling strategies have been applied to the supervised analysis of PyMS data. Such methods fall within the framework of chemometrics the discipline concerned with the application of statistical and mathematical methods to chemical data.81-85 These methods seek to relate known spectral inputs to known targets, and the resulting model is then used to predict the target of an unknown input.86... 

Determination of the optimum complexity of a model is an important but not always an easy task, because the minimum of measures for the prediction error for test sets is often not well marked. In chemometrics, the complexity is typically controlled by the number of PLS or PCA components, and the optimum complexity is estimated by CV (Section 4.2.5). Several strategies are applied to determine a reasonable optimum complexity from the prediction errors which may have been obtained by CV (Figure 4.4). CV or bootstrap allows an estimation of the prediction error for each object of the calibration set at each considered model complexity. 

The most used resampling strategy in chemometrics to obtain a reasonable large number of predictions is cross validation (CV). CV is also often applied to optimize... 

Variable selection is an optimization problem. An optimization method that combines randomness with a strategy that is borrowed from biology is a technique using genetic algorithms—a so-called natural computation method (Massart et al. 1997). Actually, the basic structure of GAs is ideal for the purpose of selection (Davis 1991 Hibbert 1993 Leardi 2003), and various applications of GAs for variable selection in chemometrics have been reported (Broadhurst et al. 1997 Jouan-Rimbaud et al. 1995 Leardi 1994, 2001, 2007). Only a brief introduction to GAs is given here, and only from the point of view of variable selection. 

EXMAT - A Linked Network of Expert Systems for Materials Analysis. Seven individual expert systems comprise EXMAT (1) problem definition and analytical strategy (2) instrumental configuration and conditions (3) data generation (4) chemometric/search algorithms (5) results (6) interpretation (7) analytical goals. Dynamic headspace (DHS)/GC and pyrolysis GC (PGC)/concentrators... 

Given the quite simple and clear model of sampling strategies and the economically very important impact of sampling there has been published comparatively little about sampling strategies. The emphasis has been more on analytical techniques. Detection limit, precision and capacity have been the main topics in analytical chemistry for more then 30 years. Chemometrics, providing means to extract more informa-... 

Chemometrics sampling strategies/G. Kateman — Signal and data analysis in chromatography/H. C. Smit, E. J. v. d. Heuvel — [etc.]... 

Internal standard (IS) calibration requires ratioing of an analytical signal to an IS which has very similar characteristics to that of the analyte of interest (an element which is similar to the analyte either in mass, ionisation potential or chemical behaviour). Quantitative analysis applying internal standardisation is the most popular calibration strategy in ICP-MS, as improvements in precision are obtained when the technique is appropriately used. Of course, the validity of this calibration method requires that one ensures a good selection of the correct internal standard. For this purpose it is possible to resort to chemometric methods [16]. 

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