Factors affecting method choice

The results of dynamic tests are dependent on the test conditions test piece shape, mode of deformation, strain amplitude, strain history, frequency and temperature. ISO 4664 gives a good summary of basic factors affecting the choice of test method. Forced vibration, non-resonant tests in simple shear using a sinusoidal waveform are generally preferred for design data as... 

The most common methods of paint applications are brush and roller, air or airless spray, roll coating, electrostatic spraying, electro deposition, and dip coating. Many factors affect the choice of method to be used for a particular application. These include film thickness, appearance requirements, and operating cost. 

Increasingly, the cost and acceptability of disposal of the reaction products is a major factor affecting the choice of abatement process. With lime and limestone, most of the reaction products are insoluble in water. Where landfill is the favoured disposal method, that is a significant advantage. 

Many factors affect the choice of assembly method when considering the manufacture of a new product. The following considerations are generally important ... 

In the selection of an appropriate DDA or other flame propagation control method consideration must he given to a nnmher of factors which affect the choice. Presented helow is a list of items to he considered in a logical sequence. 

To develop robust methods, you have to keep in mind that simple methods are preferable. Also, if you have a chromatographic background, then the factors affecting CE precision (and to a lesser extent accuracy) are widely different to HPLC, which is currently the predominantly employed technique for drug assay. There is no general preference for the choice between techniques CE, LC, or other. The choice should be made on a scientific basis, supported by the relative merits of the techniques for the specific problem and factors such as the experience of the method-developing scientist. Robust CE methods have been successfully transferred to relatively inexperienced QC and Contract Research Organisation (CRO) labs. 

The final factors affecting optimization are the choice for the initial Hessian and the method used to form Hessians at later steps. As discussed in Section 10.3.1, QN methods avoid the costly computation of analytic Hessians by using Hessian updating. In that section, we also showed the mathematical form of some common updating schemes and pointed out that the BEGS update is considered the most appropriate choice for minimizations. What may not have been obvious from Section 10.3.1 is that the initial... 

In this section, we consider a few points related to the practical application of the methods described above for TS optimization. The factors affecting TS optimization are the same as for minimization the starting structure provided by the user, coordinate system, algorithm choice, initial Hessian, and quality of the updated Hessian. Since the points made in Section 10.3.5 are also valid for TS optimization, we do not restate them here. Instead, we focus on specific issues unique to TS optimization and recommend that the reader first read Section 10.3.5. This section also contains suggestions for difficult TS problems. 

A major factor affecting the quality of the final result is the suitability of the analytical method applied. Ensuring that the method is fit for purpose can be considered a basic quality control criterion. It is important that laboratories restrict their choice of methods to those that have been characterized as suitable for the matrix and analyte of interest, and at the level of interest. In the EU, and in many other countries and regions, the regulatory limit for authorized veterinary medicinal products is the maximum residue limit (MRL), and for contaminants the maximum permitted limit. Eor prohibited or unauthorized analytes, there is often a threshold or action limit set in Europe, for example, the appropriate regulatory limit is the minimum required performance limit (MRPL) or the reference point for action (RPA), as defined in Article 4 of Commission Decision 2002/657/EC, Article 2 of Commission Decision 2005/34/EC, and Articles 18 and 19 of Council Regulation (EEC) 470/2009.2 ... 

Technical details, including solid support, coating and column filling techniques, eluents, and factors affecting the reproducibility have been reviewed [173, 235, 236]. Lipophilicity values from HPLC measurements are not on a unique scale, but the log k values can be converted to -octanol/water partition coefficients with the help of a set of HPLC calibration standard compounds for which classical shake-flask partition coefficients are known. Experience shows once the HPLC method is successfully established in a laboratory, it will remain the method of choice for lipophilicity determinations. 

Several factors must be considered for a particular biomacromolecular structure application that will affect the choice of spectroscopic methods. These include structural resolution necessary, chemical nature of biomacromolecule (protein, nucleic add, or glycan), amount/concentration of biopolymer available, sample preparation (solid or solution), solvents of interest, and desired structure information (secondary or tertiary structure). Structural resolution varies considerably for the various spectroscopic methods, with X-ray diffraction and NMR providing atomic resolution (high resolution) and ultraviolet (UV) absorption revealing merely information about the polarity of the chromophore s environment (low resolution). X-ray studies require crystals while NMR experiments prefer solutions in deuterated solvent. Solvent preferences can affect the choice of spectroscopic method as, for example, infrared (IR) encoimters strong interference from water, while optical rotatory dispersion (ORD) and circniar dichroism (CD) do not. Some of the commonly used spectroscopic methods in structural analyses of biomacromolecules will be discussed. 

Factor Analysis. Several choices had to be made in preparing the data for factor analysis as well as in choosing criteria for selecting the number of factors needed to describe the data space (e.g. eigenvalue > 1.0, ratio adjacent eigenvalues > 2.0, etc.) and the number of factor scores to be used as input into the canonical correlation analysis. These choices may have affected subsequent interpretation of the multivariate spaces and evaluation of the chemometric analysis methods. Table II shows the types of spectral data input into factor analyses of the first 13 subfractions. 

---