Calibration methods, goal

For most spectroscopic applications, the goal of multivariate calibration is to predict the concentration of a given analyte(s) in a future (prospective) sample using only its measured spectrum and a previously determined model. To do this, the inverse calibration method is used in which equation (12.2) is rewritten as... 

The key characteristic of a RM is that the properties of interest are measured and certified on the basis of accuracy. The means of attaining the true value are varied, and several different philosophies have been utilized in the quest for the best estimate of the true value. The goal of all approaches is arrival at the best possible estimate of the true value a reliable and unassailable numerical value of the concentration of the chemical constituent, under constraints of economics, state-of-the-art analytical technologies, availability of (new and old) methods, analyst competence, availability of analysts and RM end-use requirement. The basic requirement for producing reliable data is appropriate methodology, adequately calibrated and properly used. 

Intensified metabolic control, especially in case of diabetes, demands minimal-invasive or non-invasive methods of analytical measurement. For this goal, a method has been developed to measure the blood glucose content in vivo, in direct contact with the skin, by means of diffuse reflection near infrared (NIR) spectroscopy on the basis of multivariate calibration and neural networks (Muller et al. [1997] Fischbacher et al. [1997] Danzer et al. [1998]). Because no patients with any standard blood glucose value are available in principle, a method of indirect calibration has... 

In contras to unsupervised methods, supervised pattern-recognition methods (Section 4.3) use class membership information in the calculations. The goal of these methods is to construct models using analytical measurements to predict class membership of future samples. Class location and sometimes shape are used in the calibration step to construct the models. In prediction, these moddsare applied to the analytical measurements of unknowu samples to predict dsss membership. 

In many applications, one response from an instrument is related to the concentration of a single chemical component. This is referred to as univariate calibration because only one instrument response is used per sample. Multivariate calibration is the process of relating multiple responses from an instrument to a property or properties of a sample. The samples could be, for example, a mixture of chemical components in a process stream, and the goal is to predict the concentration levels of the different chemical components in the stream from infrared measurements. The methods are quite powerful, but as Dr. Einstein noted, the application of mathematics to reality is not without its limitations. It is, therefore, the obligation of the analyst to use them in a responsible manner. 

In the Introduction it has been mentioned that the immediate reason for the development of phase distribution chromatography was the search for a fast and exact method to determine very narrow molecular weight distributions of polystyrenes obtained from anionic polymerization. The long way to reach this goal became evident only in the course of the investigations shown in the previous sections and representing a basis for the computation of such MWDs. In fact, not only exact measurements are necessary, but also mathematical expressions for the measured calibration curves and for the spreading of the injected concentration profile in the column must be stated, which cannot be done empirically. 

Often, calibration of natural products and materials is a desirable goal. In these kinds of assays, it is usually not feasible to control the composition of calibration and validation standards. Some well-known examples include the determination of protein, starch, and moisture in whole-wheat kernels and the determination of gasoline octane number by NIR spectroscopy. In cases such as these, sets of randomly selected samples must be obtained and analyzed by reference methods. 

The goal of methods that standardize instrument response is to find a function that maps the response of the secondary instrument to match the response of the primary instrument. This concept is used in the statistical analysis procedure known as Procrustes analysis [97], One such method for standardizing instrument response is the piecewise direct standardization (PDS) method, first described in 1991 [98,100], PDS was designed to compensate for mismatches between spectroscopic instruments due to small differences in optical alignment, gratings, light sources, detectors, etc. The method has been demonstrated to work well in many NIR assays where PCR or PLS calibration models are used with a small number of factors. 

A great deal of work in recent years has been carried out with the goal of gaining a better understanding of the electronic absorption spectra in metal carbonyl complexes. A plethora of methods have been calibrated and applied to these systems... 

Selected Examples. - Pulsed magnetic field gradient (PFG) NMR is today a routine method for the determination of self-diffusion coefficients. However, a remaining goal is the improvement of the precision of the method. The best procedure for the determination of accurate diffusion coefficients by PFG NMR is a calibration with a sample of precisely known D value. Thus Holz et al presented temperature-dependent self-diffusion coefficients of water and six selected molecular liquids. The gained accurate self-diffusion data are suited for an elaborate check of theoretical approaches in the physics of molecular liquids. Price et al examined the translational diffusion... 

Calibrating the Euler method) The goal of this problem is to test the... 

Multivariate Regression Methods. The main goal of this study was to build a multivariate model for the reliable prediction of a property of interest y (cheese ripening time) from a number of predictor variables, xi, X2. .. (peak area of casein and peptide obtained by CE). This model should describe the measured x and y data of the calibration set (cheese samples at different ripening time). In particular, in this research, the PCR and PLS methods were evaluated. 

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