Optimizing Instrumental Factors

The optimization of the variables is a critical step in the design of new analytical methods. Optimization involves the selection of the chemical and instrumental factors which may affect the analytical signal, and the choice of the values of the variables to obtain the best response from the chemical system. For this purpose, two different strategies can be used. In the traditional univariate optimization, all values of the different factors except one are constant, and this one is the object of the examination. The alternative to this strategy is the use of chemometric techniques based mainly on the use of experimental designs (Tarley, et al. 2009). 

In many cases an optimized method may produce excellent results in the laboratory developing the method, but poor results in other laboratories. This is not surprising since a method is often optimized by a single analyst under an ideal set of conditions, in which the sources of reagents, equipment, and instrumentation remain the same for each trial. The procedure might also be influenced by environmental factors, such as the temperature or relative humidity in the laboratory, whose levels are not specified in the procedure and which may differ between laboratories. Finally, when optimizing a method the analyst usually takes particular care to perform the analysis in exactly the same way during every trial. 

Finally, we note that the size and shape of the particles of the packing, the packing technique, and column dimensions and configuration are additional factors which influence a GPC experiment. In addition, the flow rate, the sample size, the sample concentration, the solvent, and the temperature must all be optimized. Details concerning these considerations are found in analytical chemistry references, as well as in the technical literature of instrument manufacturers. 

As atomic fluorescence spectrometer a mercury analyzer Mercur , (Analytik-Jena, Germany) was used. In the amalgamation mode an increase of sensitivity by a factor of approximately 7-8 is obtained compared with direct introduction, resulting in a detection limit of 0,09 ng/1. This detection limit has been improved further by pre-concentration of larger volumes of samples and optimization of instrumental parameters. Detection limit 0,02 ng/1 was achieved, RSD = 1-6 %. 

In principle GD-MS is very well suited for analysis of layers, also, and all concepts developed for SNMS (Sect. 3.3) can be used to calculate the concentration-depth profile from the measured intensity-time profile by use of relative or absolute sensitivity factors [3.199]. So far, however, acceptance of this technique is hesitant compared with GD-OES. The main factors limiting wider acceptance are the greater cost of the instrument and the fact that no commercial ion source has yet been optimized for this purpose. The literature therefore contains only preliminary results from analysis of layers obtained with either modified sources of the commercial instrument [3.200, 3.201] or with homebuilt sources coupled to quadrupole [3.199], sector field [3.202], or time-of-flight instruments [3.203]. To summarize, the future success of GD-MS in this field of application strongly depends on the availability of commercial sources with adequate depth resolution comparable with that of GD-OES. 

The experimental designs discussed in Chapters 24-26 for optimization can be used also for finding the product composition or processing condition that is optimal in terms of sensory properties. In particular, central composite designs and mixture designs are much used. The analysis of the sensory response is usually in the form of a fully quadratic function of the experimental factors. The sensory response itself may be the mean score of a panel of trained panellists. One may consider such a trained panel as a sensitive instrument to measure the perceived intensity useful in describing the sensory characteristics of a food product. 

Consequently, which strategy is utilized in reaction optimization experiments is highly dependent on the type of instrument used. Whilst multimode reactors employ powerful magnetrons with up to 1500 W microwave output power, monomode reactors apply a maximum of only 300 W. This is due to the high density microwave field in a single-mode set-up and the smaller sample volumes that need to be heated. In principle, it is possible to translate optimized protocols from monomode to multimode instruments and to increase the scale by a factor of 100 without a loss of efficiency (see Section 4.5). 

We have discussed individual analyses and the demands to achieve optimization of instrumentation. However, an analytical laboratory must deal with series of samples and we must consider another factor if we want to optimize complete workflows cycle time optimization. Cycle time is defined as the time from finishing the analysis of one sample to the time the next sample is finished. This can be easily determined on Microsoft Windows -based operating systems by examining the data file creation time stamps of two consecutive samples. A better way is calculating the average of a reasonable number of samples. 

It has already been stated that a suitable quantitative assay technique must be available to measure the reaction of interest and it is assumed that the experimenter has determined optimal reaction conditions for the enzyme of interest. All kinetic assay techniques assume that v is a variable and that [S] is known as such, preparation of substrate must be meticulous in terms of ensuring that concentrations are correct, and this in turn will rely upon factors such as good weighing and pipetting techniques with calibrated instruments capable of precise, accurate, and sufficiently sensitive measurement. 

Standardizing the coefficients of the model entails modifying the calibration equation. This procedure is applicable when the original equipment is replaced (situation 1 above). Forina et al. developed a two-step calibration procedure by which a calibration model is constructed for the master (F-X), its spectral response correlated with that of the slave X-X) and, finally, a global model correlating variable Y with both X and X is obtained. The process is optimized in terms of SEP and SEC for both instruments as it allows the number of PLS factors used to be changed. Smith et al. propose a very simple procedure to match two different spectral responses. 

This study demonstrates that the nonlinear optimization approach to parameter estimation is a flexible and effective method. Although computationally intensive, this method lends itself to a wide variety of process model formulations and can provide an assessment of the uncertainty of the parameter estimates. Other factors, such as measurement error distributions and instrumentation reliability can also be integrated into the estimation procedure if they are known. The methods presented in the crystallization literature do not have this flexibility in model formulation and typically do not address the parameter reliability issue. 

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