Concentrations chemometrics

Other chemometrics methods to improve caUbration have been advanced. The method of partial least squares has been usehil in multicomponent cahbration (48—51). In this approach the concentrations are related to latent variables in the block of observed instmment responses. Thus PLS regression can solve the colinearity problem and provide all of the advantages discussed earlier. Principal components analysis coupled with multiple regression, often called Principal Component Regression (PCR), is another cahbration approach that has been compared and contrasted to PLS (52—54). Cahbration problems can also be approached using the Kalman filter as discussed (43). 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

Until fairly recently, IR spectroscopy was scarcely used in quantitative analysis owing to its many inherent shortcomings (e.g. extensive band overlap, failure to fulfil Beer s law over wide enough concentration ranges, irreproducible baselines, elevated instrumental noise, low sensitivity). The advent of FTIR spectroscopy, which overcomes some of these drawbacks, in addition to the development of powerful chemometric techniques for data processing, provides an effective means for tackling the analysis of complex mixtures without the need for any prior separation of their components. 

If we consider only a few of the general requirements for the ideal polymer/additive analysis techniques (e.g. no matrix interferences, quantitative), then it is obvious that the choice is much restricted. Elements of the ideal method might include LD and MS, with reference to CRMs. Laser desorption and REMPI-MS are moving closest to direct selective sampling tandem mass spectrometry is supreme in identification. Direct-probe MS may yield accurate masses and concentrations of the components contained in the polymeric material. Selective sample preparation, efficient separation, selective detection, mass spectrometry and chemometric deconvolution techniques are complementary rather than competitive techniques. For elemental analysis, LA-ICP-ToFMS scores high. 

In addition to the measured values and the analytical values (e.g. content, concentration), latent variables are included in the scheme. Latent variables can be obtained from measured values or from analytical values by means of mathematical operations (e.g. addition, subtraction, eigenanal-ysis). By means of latent variables and their typical pattern (represented in chemometric displays) special information can be obtained, e.g. on quality, genuineness, authenticity, homogeneity, origin of products, and health of patients. 

Principal Component Regression, PCR, and Partial Least Squares, PLS, are the most widely known and applied chemometrics methods. This is particularly the case for PLS, for which there is a tremendous number of applications and a never-ending stream of proposed improvements. The details of these latest modifications are not within the scope of this book and we concentrate on the essential, classical aspects. 

Highly correlating (collinear) variables make the covariance matrix singular, and consequently the inverse cannot be calculated. This has important consequences on the applicability of several methods. Data from chemistry often contain collinear variables, for instance the concentrations of similar elements, or IR absorbances at neighboring wavelengths. Therefore, chemometrics prefers methods that do not need the inverse of the covariance matrix, as for instance PCA, and PLS regression. The covariance matrix becomes singular if... 

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... 

The use of ultraviolet (UV) spectroscopy for on-line analysis is a relatively recent development. Previously, on-line analysis in the UV-visible (UV-vis) region of the electromagnetic spectrum was limited to visible light applications such as color measurement, or chemical concentration measurements made with filter photometers. Three advances of the past two decades have propelled UV spectroscopy into the realm of on-line measurement and opened up a variety of new applications for both on-line UV and visible spectroscopy. These advances are high-quality UV-grade optical fiber, sensitive and affordable array detectors, and chemometrics. 

Finally, the development of chemometrics over the past 20 years has also aided in the use of UV-vis technology for more complicated chemical matrices than was possible at earlier times. Chemometrics allows large quantities of spectral data to be analyzed and reduced to a useful bit of information such as the concentration of one or more chemical species. In addition, through analysis of residuals, it is possible to detect when something unexpected occurs in a process. 

Calibration Most process analyzers are designed to monitor concentration and/or composition. This requires a calibration of the analyzer with a set of prepared standards or from well-characterized reference materials. The simple approach must always be adopted first. For relatively simple systems the standard approach is to use a simple linear relationship between the instrument response and the analyte/ standard concentration [27]. In more complex chemical systems, it is necessary to adopt either a matrix approach to the calibration (still relying on the linearity of the Beer-Lambert law) using simple regression techniques, or to model the concentration and/or composition with one or more multivariate methods, an approach known as chemometrics [28-30]. 

Acoustic chemometrics has its greatest benefits in cases where haditional sensors and measurement techniques, such as flow, temperature and pressure transmitters cannot be used. In many processes it is preferable to use noninvasive sensors because invasive sensors may cause disturbances, for example fouling and clogging inside the process equipment such as pipelines, reactors cyclones, etc. In this chapter we concentrate mainly on new industrial applications for acoustic chemomehics, and only discuss the necessary elements of the more technical aspects of the enabling technology below - details can be found in the extensive background literature [3-5],... 

The acoustic chemometric approach can also be used to monitor industrial production processes involving particles and powders and to provide a complementary tool for process operators for more efficient process control, or to monitor particle movement in a fluidized bed [7] for example. Below we illustrate the application potential by focusing on two applications process monitoring of a granulation process and monitoring of ammonia concentration. 

For industrial fertilizer production reliable ammonia concentration data are essential. An experimental setup for acoustic chemometric prediction of ammonia concentration has been tested in a full-scale industrial plant. Figure 9.22 shows a bypass loop with the orifice plate. The acoustic sensor was again mounted onto the orifice plate [5]. To ensure constant differential pressure and temperature of the ammonia flow, two pressure transmitters and one temperature sensor were used. Reference samples were taken at the sample valve shown in Figure 9.22. 

Figure 9.22 Experimental setup for assessment of acoustic chemometrics potential to predict concentration of ammonia directly from acoustic emission generated by cavitation/turbulent flow.

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