Descriptive analysis development capability

I believe the future of sensory evaluation will involve an expansion of the use of descriptive analysis in many different situations, such as in plant quality control, as well as product development and research applications. Because of the increased competition in the flavor industry, flavor companies are increasingly expanding their sensory work and sensory capabilities. This is necessary, not only for the flavor company to understand the products they are producing but to be able to satisfactorily service their client companies. 

As noted previously a descriptive analysis capability attracts attention because of the results it provides for example, the ability to describe specific differences among an array of competitive products. This section provides some details on how a descriptive analysis capability can be developed and a panel made ready to evaluate products in a relatively short time period. Starting with newly recruited subjects, one can have a panel available in 2 weeks - three session days for screening, five for language, and one for a pre-test. Once operational, however, a test can be organized in a day, followed by data collection. The duration of a test depends on the number and type of products and, as always, the objective. So any discussion about rapid methods must first begin with what is meant by rapid in the context of existing methods. 

The theory is capable of describing both the regimes of equilibrium and nonequilibrium solvation for the latter we have developed a framework of natural solvent coordinates which greatly helps the analysis of the reaction system along the ESP, and displays the ability to reduce considerably the burden of the calculation of the free energy surface in the nonequilibrium solvation regime. While much remains to be done in practical implementations for various reactions, the theory should prove to be a very useful and practical description of reactions in solution. 

Because PB-PK models are based on physiological and anatomical measurements and all mammals are inherently similar, they provide a rational basis for relating data obtained from animals to humans. Estimates of predicted disposition patterns for test substances in humans may be obtained by adjusting biochemical parameters in models validated for animals adjustments are based on experimental results of animal and human in vitro tests and by substituting appropriate human tissue sizes and blood flows. Development of these models requires special software capable of simultaneously solving multiple (often very complex) differential equations, some of which were mentioned in this chapter. Several detailed descriptions of data analysis have been reported. 

QPPR can be derived from thermodynamic principles or by statistical analysis of measured data. In the latter case, a set of compounds for which Fand Pi, P2, , Pm are known is required to develop the model (the training set). An additional evaluation set of compounds with known F, Pi, P2, , Pm is recommended to evaluate the reliability and predictive capability of the model proposed. For a detailed description of the statistical methods, the reader is referred to [25], standard statistical texts, and to articles listed in the Toolkit Bibliography. 

Another problem that has been tackled by multivariate statistical methods is the characterization of the solvation capability of organic solvents based on empirical parameters of solvent polarity (see Chapter 7). Since such empirical parameters of solvent polarity are derived from carefully selected, strongly solvent-dependent reference processes, they are molecular-microscopic parameters. The polarity of solvents thus defined cannot be described by macroscopic, bulk solvent characteristics such as relative permittivities, refractive indices, etc., or functions thereof. For the quantitative correlation of solvent-dependent processes with solvent polarities, a large variety of empirical parameters of solvent polarity have been introduced (see Chapter 7). While some solvent polarity parameters are defined to describe an individual, more specific solute/solvent interaetion, others do not separate specific solute/solvent interactions and are referred to as general solvent polarity scales. Consequently, single- and multi-parameter correlation equations have been developed for the description of all kinds of solvent effects, and the question arises as to how many empirical parameters are really necessary for the correlation analysis of solvent-dependent processes such as chemical equilibria, reaction rates, or absorption spectra. 

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