Descriptive analysis language development

Sensory scientists will agree that descriptive analysis has many applications but there is relatively little agreement as to how specific methods are developed and used. For example, subjects may or may not have been screened the number of subjects can range from as few as 5 to as many as 20 there may be a formal language development process or not the number of attributes on a scorecard may be limited references may or may not be used replication may or may not be part of the design data analysis can be simple (e.g., summary statistics such as means and variance measures) or... 

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. 

Molecular databases and the associated data banks require the development of a conceptual structure for the information stored about the molecules, descriptive language representing the data, and methods for analysis enabling molecular modeling, similarity searches, classification, visualization, or other uses of the database.320 Currendy, the Protein Data Bank (PDB http 7www.rcsb.org/pdb/) is one of the best known examples of a molecular database. The PDB is a worldwide archive of three-dimensional structural data of biological macromolecules.321 The PDB is a common accentor to many structural databases.322 The success of... 

Identification of symbolic attributes is probably the most challenging activity within morphological analysis. It requires a rare ability to think in abstract terms of symbolic attributes. Even more rare and difficult is the ability to transform traditional descriptions of engineering systems (words, drawings, dimensions, etc.) into their abstract description using the language of symbolic attributes. Fortunately, these two abilities can be developed as a result of practice. 

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